Robert Mclaughlin

Robert McLaughlin, Nir Chemaya, Dingyue Liu et al.

This paper introduces a trade ordering rule that aims to reduce intra-block price volatility in Automated Market Maker (AMM) powered decentralized exchanges. The ordering rule introduced here, Clever Look-ahead Volatility Reduction (CLVR), operates under the (common) framework in decentralized finance that allows some entities to observe trade requests before they are settled, assemble them into "blocks", and order them as they like. On AMM exchanges, asset prices are continuously and transparently updated as a result of each trade and therefore, transaction order has high financial value. CLVR aims to order transactions for traders' benefit. Our primary focus is intra-block price stability (minimizing volatility), which has two main benefits for traders: it reduces transaction failure rate and allows traders to receive closer prices to the reference price at which they submit their transactions accordingly. We show that CLVR constructs an ordering which approximately minimizes price volatility with a small computation cost and can be trivially verified externally.

Zeyu Zhang, Xuyin Qi, Mingxi Chen et al.

The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders. However, continuous monitoring of SaO2 is time-consuming and highly variable depending on patients' conditions. Recently, optical coherence tomography angiography (OCTA) has shown promising development in rapidly and effectively screening eye-related lesions, offering the potential for diagnosing sleep-related disorders. To bridge this gap, our paper presents three key contributions. Firstly, we propose JointViT, a novel model based on the Vision Transformer architecture, incorporating a joint loss function for supervision. Secondly, we introduce a balancing augmentation technique during data preprocessing to improve the model's performance, particularly on the long-tail distribution within the OCTA dataset. Lastly, through comprehensive experiments on the OCTA dataset, our proposed method significantly outperforms other state-of-the-art methods, achieving improvements of up to 12.28% in overall accuracy. This advancement lays the groundwork for the future utilization of OCTA in diagnosing sleep-related disorders. See project website https://steve-zeyu-zhang.github.io/JointViT