Time Warner (United States)

We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set, where sparsity is defined by the amount of historical viewing data available to explain a movie recommendation for a particular data instance. We find that LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIME) that is superior to LIME and much faster than SHAP.

For aquaculture resource evaluation and ecological environment monitoring, the automatic detection and identification of marine organisms is critical; however, due to the low quality of underwater images and the characteristics of underwater biological detection, the lack of abundant features can impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environments. Therefore, the goal of this study was to perform object detection in underwater environments. This study developed a novel method for capturing feature information by adding the convolutional block attention module (CBAM) to the YOLOv5 backbone network. The interference of underwater organism characteristics in object characteristics decreased and the output object information of the backbone network was enhanced. In addition, a self-adaptive global histogram stretching algorithm (SAGHS) was designed to eliminate degradation problems, such as low contrast and color loss, that are caused by underwater environmental features in order to restore image quality. Extensive experiments and comprehensive evaluations using the URPC2021 benchmark dataset demonstrated the effectiveness and adaptivity of the proposed methods. Additionally, this study conducted an exhaustive analysis of the impacts of training data on performance.

Introduction: Due to conduction disease and dyssynchrony in transthyretin amyloid cardiomyopathy (ATTR-CM), cardiac resynchronization therapy (CRT) may be beneficial. We aimed to determine the impact of CRT on clinical outcomes in ATTR-CM. Methods: We studied 116 consecutive ATTR-CM patients, 23 of whom underwent CRT (70% CRT + defibrillator, median 10 months (IQR 4-20) from ATTR-CM diagnosis). Outcome was a composite of 1 st cardiovascular hospitalization or all-cause mortality. CRT was treated as a time dependent co-variate and entered into multivariate Cox proportional hazards model. Results: Both groups were similar at diagnosis except for a higher proportion with atrial fibrillation (AF) and lower ejection fraction among those who underwent CRT ( Table 1) . CRT indication in 80% was progressive LV dysfunction with wide QRS [mean 152 ± 36 msec] and the remaining at time of AV node ablation for AF. Half of CRT group were ≥ NYHA class III at device implantation. Older age, higher National Amyloidosis Cohort (NAC) ATTR stage, and not taking tafamidis were significantly associated with the outcome. On multivariate analysis, CRT was associated with a HR of 0.6 (95% CI: 0.32 - 1.16) for all-cause mortality or cardiovascular hospitalization after adjusting for age at diagnosis, NAC ATTR stage, and tafamidis use ( Figure 1). Conclusion: CRT implantation maybe associated with improved outcomes in ATTR-CM. Larger prospective randomized, sham-controlled trials are warranted to confirm these findings.