Improving Rheological And Thermal Performance Of Gilsonite-Modified Binder With Phase Change Materials

Gilsonite, as a type of natural asphalt binder, has been used to improve the high-temperature performance of regular asphalt binders. However, the addition of Gilsonite may compromise binders\u27 low-temperature thermal cracking resistance. In this research, polyethylene glycol (PEG), as one type of the phase change materials (PCMs), was used as an innovative material to balance the impacts of Gilsonite on high and low performance of asphalt binders. The dosages of Gilsonite and PEG were first determined based on the materials\u27 rheological behaviors at low temperatures. The performance of the PEG-Gilsonite-modified binder was then fully evaluated in terms of the resistance to cracking and rutting at various temperatures. Thermal tests were also conducted to assess the thermal behaviors of the modified binders. The testing results indicate that with the proper dosage of Gilsonite and PEG, the rutting resistance of the binder can be improved without sacrificing its low-temperature performance. With the addition of the PCM, the binder was tested to have high volumetric heat capacity, which indicates PCM can reduce the rate and the magnitude of the temperature changes in pavements

Deep Metric Learning with Soft Orthogonal Proxies

Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compared to pair-based losses in terms of convergence speed. However, proxies that are assigned to different classes may end up being closely located in the embedding space and hence having a hard time to distinguish between positive and negative items. Alternatively, they may become highly correlated and hence provide redundant information with the model. To address these issues, we propose a novel approach that introduces Soft Orthogonality (SO) constraint on proxies. The constraint ensures the proxies to be as orthogonal as possible and hence control their positions in the embedding space. Our approach leverages Data-Efficient Image Transformer (DeiT) as an encoder to extract contextual features from images along with a DML objective. The objective is made of the Proxy Anchor loss along with the SO regularization. We evaluate our method on four public benchmarks for category-level image retrieval and demonstrate its effectiveness with comprehensive experimental results and ablation studies. Our evaluations demonstrate the superiority of our proposed approach over state-of-the-art methods by a significant margin

Decoding clinical biomarker space of COVID-19:exploring matrix factorization-based feature selection methods

Abstract One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases