Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network

It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions

Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator

Randomized experiments (REs) are the cornerstone for treatment effect evaluation. However, due to practical considerations, REs may encounter difficulty recruiting sufficient patients. External controls (ECs) can supplement REs to boost estimation efficiency. Yet, there may be incomparability between ECs and concurrent controls (CCs), resulting in misleading treatment effect evaluation. We introduce a novel bias function to measure the difference in the outcome mean functions between ECs and CCs. We show that the ANCOVA model augmented by the bias function for ECs renders a consistent estimator of the average treatment effect, regardless of whether or not the ANCOVA model is correct. To accommodate possibly different structures of the ANCOVA model and the bias function, we propose a double penalty integration estimator (DPIE) with different penalization terms for the two functions. With an appropriate choice of penalty parameters, our DPIE ensures consistency, oracle property, and asymptotic normality even in the presence of model misspecification. DPIE is more efficient than the estimator derived from REs alone, validated through theoretical and experimental results

MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction

Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in a material's appearance. Hyperspectral images, which are sets of spectral measurements sampled at multiple wavelengths, theoretically offer distinct information for material identification, as variations in intensity of electromagnetic radiation reflected by a surface depend on the material composition of a scene. However, existing hyperspectral datasets are impoverished regarding the number of images and material categories for the dense material segmentation task, and collecting and annotating hyperspectral images with a spectral camera is prohibitively expensive. To address this, we propose a new model, the MatSpectNet to segment materials with recovered hyperspectral images from RGB images. The network leverages the principles of colour perception in modern cameras to constrain the reconstructed hyperspectral images and employs the domain adaptation method to generalise the hyperspectral reconstruction capability from a spectral recovery dataset to material segmentation datasets. The reconstructed hyperspectral images are further filtered using learned response curves and enhanced with human perception. The performance of MatSpectNet is evaluated on the LMD dataset as well as the OpenSurfaces dataset. Our experiments demonstrate that MatSpectNet attains a 1.60% increase in average pixel accuracy and a 3.42% improvement in mean class accuracy compared with the most recent publication. The project code is attached to the supplementary material and will be published on GitHub.Comment: 7 pages main pape

Sparse Group Variable Selection for Gene-Environment Interactions in the Longitudinal Stud

Recently, regularized variable selection has emerged as a powerful tool to iden- tify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high di- mensional genetic factors, regularization methods for G×E interactions have not been systemati- cally developed. In this package, we provide the implementation of sparse group variable selec- tion, based on both the quadratic inference function (QIF) and generalized estimating equa- tion (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimen- sional genomic features. Alternative methods conducting only the group or individual level se- lection have also been included. The core modules of the package have been developed in C++

A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles

Accurate state-of-health (SOH) estimation is critical to guarantee the safety, efficiency and reliability of battery-powered applications. Most SOH estimation methods focus on the 0-100\% full state-of-charge (SOC) range that has similar distributions. However, the batteries in real-world applications usually work in the partial SOC range under shallow-cycle conditions and follow different degradation profiles with no labeled data available, thus making SOH estimation challenging. To estimate shallow-cycle battery SOH, a novel unsupervised deep transfer learning method is proposed to bridge different domains using self-attention distillation module and multi-kernel maximum mean discrepancy technique. The proposed method automatically extracts domain-variant features from charge curves to transfer knowledge from the large-scale labeled full cycles to the unlabeled shallow cycles. The CALCE and SNL battery datasets are employed to verify the effectiveness of the proposed method to estimate the battery SOH for different SOC ranges, temperatures, and discharge rates. The proposed method achieves a root-mean-square error within 2\% and outperforms other transfer learning methods for different SOC ranges. When applied to batteries with different operating conditions and from different manufacturers, the proposed method still exhibits superior SOH estimation performance. The proposed method is the first attempt at accurately estimating battery SOH under shallow-cycle conditions without needing a full-cycle characteristic test

Acoustofluidic Engineering Functional Vessel-on-a-Chip

Construction of in vitro vascular models is of great significance to various biomedical research, such as pharmacokinetics and hemodynamics, thus is an important direction in tissue engineering. In this work, a standing surface acoustic wave field was constructed to spatially arrange suspended endothelial cells into a designated patterning. The cell patterning was maintained after the acoustic field was withdrawn by the solidified hydrogel. Then, interstitial flow was provided to activate vessel tube formation. Thus, a functional vessel-on-a-chip was engineered with specific vessel geometry. Vascular function, including perfusability and vascular barrier function, was characterized by beads loading and dextran diffusion, respectively. A computational atomistic simulation model was proposed to illustrate how solutes cross vascular lipid bilayer. The reported acoustofluidic methodology is capable of facile and reproducible fabrication of functional vessel network with specific geometry. It is promising to facilitate the development of both fundamental research and regenerative therapy

Maternal and fetal/neonatal outcomes of pregnancies complicated by pulmonary hypertension: a retrospective study of 154 patients

Objectives: To determine the main clinical and demographic outcomes related to Pulmonary Hypertension (PH) and adverse obstetric and fetal/neonatal outcomes. Methods: This study retrospectively analyzed the medical record data of 154 patients with PH who were admitted to the Third Affiliated Hospital of Guangzhou Medical University between January 2011 and December 2020. Results: According to the severity of elevated Pulmonary Artery Systolic Pressure (PASP), 82 women (53.2%) were included in the mild PH group, 34 (22.1%) were included in the moderate PH group, and 38 (24.7%) were included in the severe PH group. There were significant differences in the incidence of heart failure, premature delivery, Very-Low-Birth-Weight (VLBW) infants, and Small-for-Gestational-Age (SGA) infants among the three PH groups (p < 0.05). Five (3.2%) women died within 7-days after delivery, 7 (4.5%) fetuses died in utero, and 3 (1.9%) neonates died. The authors found that PASP was an independent risk factor for maternal mortality. After adjustment for age, gestational weeks, systolic blood pressure, Body Mass Index (BMI), mode of delivery, and anesthesia, the risk of maternal mortality in the severe PH group was 20.21 times higher than that in the mild-moderate PH group (OR = 21.21 [95% CI 1.7∼264.17]), p < 0.05. All 131 (85.1%) patients were followed up for 12 months postpartum. Conclusions: The authors found that the risk of maternal mortality in the severe PH group was significantly higher than that in the mild-moderate group, highlighting the importance of pulmonary artery pressure screening before pregnancy, early advice on contraception, and multidisciplinary care

Disentangling and Operationalizing AI Fairness at LinkedIn

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the AI level. In this paper, we present the evolving AI fairness framework used at LinkedIn to address these three challenges. The framework disentangles AI fairness by separating out equal treatment and equitable product expectations. Rather than imposing a trade-off between these two commonly opposing interpretations of fairness, the framework provides clear guidelines for operationalizing equal AI treatment complemented with a product equity strategy. This paper focuses on the equal AI treatment component of LinkedIn's AI fairness framework, shares the principles that support it, and illustrates their application through a case study. We hope this paper will encourage other big tech companies to join us in sharing their approach to operationalizing AI fairness at scale, so that together we can keep advancing this constantly evolving field