ERC-20R and ERC-721R: Reversible Transactions on Ethereum

Blockchains are meant to be persistent: posted transactions are immutable and cannot be changed. When a theft takes place, there are limited options for reversing the disputed transaction, and this has led to significant losses in the blockchain ecosystem. In this paper we propose reversible versions of ERC-20 and ERC-721, the most widely used token standards. With these new standards, a transaction is eligible for reversal for a short period of time after it has been posted on chain. After the dispute period has elapsed, the transaction can no longer be reversed. Within the short dispute period, a sender can request to reverse a transaction by convincing a decentralized set of judges to first freeze the disputed assets, and then later convincing them to reverse the transaction. Supporting reversibility in the context of ERC-20 and ERC-721 raises many interesting technical challenges. This paper explores these challenges and proposes a design for our ERC-20R and ERC-721R standards, the reversible versions of ERC-20 and ERC-721. We also provide a prototype implementation. Our goal is to initiate a deeper conversation about reversibility in the hope of reducing some of the losses in the blockchain ecosystem

Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing

Given a really low-resolution input image of a face (say 16x16 or 8x8 pixels), the goal of this paper is to reconstruct a high-resolution version thereof. This, by itself, is an ill-posed problem, as the high-frequency information is missing in the low-resolution input and needs to be hallucinated, based on prior knowledge about the image content. Rather than relying on a generic face prior, in this paper, we explore the use of a set of exemplars, i.e. other high-resolution images of the same person. These guide the neural network as we condition the output on them. Multiple exemplars work better than a single one. To combine the information from multiple exemplars effectively, we introduce a pixel-wise weight generation module. Besides standard face super-resolution, our method allows to perform subtle face editing simply by replacing the exemplars with another set with different facial features. A user study is conducted and shows the super-resolved images can hardly be distinguished from real images on the CelebA dataset. A qualitative comparison indicates our model outperforms methods proposed in the literature on the CelebA and WebFace dataset.Comment: accepted in ACCV 202

Hydrogen Sulfide Prodrugs—A review

Abstract Hydrogen sulfide (H2S) is recognized as one of three gasotransmitters together with nitric oxide (NO) and carbon monoxide (CO). As a signaling molecule, H2S plays an important role in physiology and shows great potential in pharmaceutical applications. Along this line, there is a need for the development of H2S prodrugs for various reasons. In this review, we summarize different H2

Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics

The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict household' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.Comment: The paper was presented at 2022 Annual Meeting of Transportation Research Boar