Shapley Computations Using Surrogate Model-Based Trees

Shapley-related techniques have gained attention as both global and local interpretation tools because of their desirable properties. However, their computation using conditional expectations is computationally expensive. Approximation methods suggested in the literature have limitations. This paper proposes the use of a surrogate model-based tree to compute Shapley and SHAP values based on conditional expectation. Simulation studies show that the proposed algorithm provides improvements in accuracy, unifies global Shapley and SHAP interpretation, and the thresholding method provides a way to trade-off running time and accuracy

Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models

Low-order functional ANOVA (fANOVA) models have been rediscovered in the machine learning (ML) community under the guise of inherently interpretable machine learning. Explainable Boosting Machines or EBM (Lou et al. 2013) and GAMI-Net (Yang et al. 2021) are two recently proposed ML algorithms for fitting functional main effects and second-order interactions. We propose a new algorithm, called GAMI-Tree, that is similar to EBM, but has a number of features that lead to better performance. It uses model-based trees as base learners and incorporates a new interaction filtering method that is better at capturing the underlying interactions. In addition, our iterative training method converges to a model with better predictive performance, and the embedded purification ensures that interactions are hierarchically orthogonal to main effects. The algorithm does not need extensive tuning, and our implementation is fast and efficient. We use simulated and real datasets to compare the performance and interpretability of GAMI-Tree with EBM and GAMI-Net.Comment: 25 pages plus appendi

Monotone Tree-Based GAMI Models by Adapting XGBoost

Recent papers have used machine learning architecture to fit low-order functional ANOVA models with main effects and second-order interactions. These GAMI (GAM + Interaction) models are directly interpretable as the functional main effects and interactions can be easily plotted and visualized. Unfortunately, it is not easy to incorporate the monotonicity requirement into the existing GAMI models based on boosted trees, such as EBM (Lou et al. 2013) and GAMI-Lin-T (Hu et al. 2022). This paper considers models of the form $f(x)=\sum_{j,k}f_{j,k}(x_j, x_k)$ and develops monotone tree-based GAMI models, called monotone GAMI-Tree, by adapting the XGBoost algorithm. It is straightforward to fit a monotone model to $f(x)$ using the options in XGBoost. However, the fitted model is still a black box. We take a different approach: i) use a filtering technique to determine the important interactions, ii) fit a monotone XGBoost algorithm with the selected interactions, and finally iii) parse and purify the results to get a monotone GAMI model. Simulated datasets are used to demonstrate the behaviors of mono-GAMI-Tree and EBM, both of which use piecewise constant fits. Note that the monotonicity requirement is for the full model. Under certain situations, the main effects will also be monotone. But, as seen in the examples, the interactions will not be monotone.Comment: 12 page

Instance Segmentation in the Dark

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed method is motivated by the observation that noise in low-light images introduces high-frequency disturbances to the feature maps of neural networks, thereby significantly degrading performance. To suppress this ``feature noise", we propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. These components effectively reduce feature noise during downsampling and convolution operations, enabling the model to learn disturbance-invariant features. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our analysis indicates that high bit-depth can be critical for low-light instance segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a low-light RAW synthetic pipeline to generate realistic low-light data. In addition, to facilitate further research in this direction, we capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations. Remarkably, without any image preprocessing, we achieve satisfactory performance on instance segmentation in very low light (4~\% AP higher than state-of-the-art competitors), meanwhile opening new opportunities for future research.Comment: Accepted by International Journal of Computer Vision (IJCV) 202

Characteristics of High Risk People with Cardiovascular Disease in Chinese Rural Areas: Clinical Indictors, Disease Patterns and Drug Treatment

Background and Aims: Current cardiovascular disease (CVD) prevention is based on diagnosis and treatment of specific disease. Little is known for high risk people with CVD at the community level. In rural China, health records of all residents were established after the recent health reforms. This study aims to describe the characters of the rural population with high CVD risk regarding their clinical indicators, disease patterns, drug treatment and adherence. Methods and Results: 17042 (87%) of all the 19500 rural residents in the two townships had valid health records in 2009. We employed a validated tool, the Asian Equation, to screen 8182 (48%) resident health records of those aged between 40-75 years in 2010. Those who were identified with a CVD risk of 20% or higher were selected for a face-to-face questionnaire survey regarding their diagnosed disease and drug treatment. 453 individuals were identified as high risk of CVD, with an average age of 53 years, 62% males, 50% smoking rate and average systolic blood pressure of 161 mmHg. 386 (85%) participated in the survey, while 294 (76%) were diagnosed with and 88 (23%) were suspects of CVD, hypertension, diabetes or hyperlipidaemia. 75 (19%) took drug regularly and 125 (32%) either stopped treatment or missed drugs. The most often used drugs were calcium channel blockers (20%). Only 2% used aspirins and 0.8% used statins. The median costs of drugs were 17 RMB (USD2.66) per month. Conclusion: The majority of the high risk population in our setting of rural China had already been diagnosed with a CVD related disease, but very few took any drugs, and less still took highly effective drugs to prevent CVD. A holistic strategy focused on population with high risk CVD and based on the current China public health reform is suggested in the context of primary care. © 2013 Wei et al.published_or_final_versio

Growth-in-place deployment of in-plane silicon nanowires

International audienceUp-scaling silicon nanowire (SiNW)-based functionalities requires a reliable strategy to precisely position and integrate individual nanowires. We here propose an all-in-situ approach to fabricate self-positioned/aligned SiNW, via an in-plane solid-liquid-solid growth mode. Prototype field effect transistors, fabricated out of in-plane SiNWs using a simple bottom-gate configuration, demonstrate a hole mobility of 228 cm2/V s and on/off ratio >103. Further insight into the intrinsic doping and structural properties of these structures was obtained by laser-assisted 3 dimensional atom probe tomography and high resolution transmission electron microscopy characterizations. The results could provide a solid basis to deploy the SiNW functionalities in a cost-effective way