Search

Automated market makers (AMMs) are a form of decentralized cryptocurrency exchanges and considered a prime example of Decentralized Finance (DeFi) applications. Their popularity and high trading activity have resulted in millions of on-chain transactions leading to serious scalability issues. In this paper, we address the on-chain storage overhead problem of AMMs by utilizing a new sidechain architecture as a layer 2 solution, building a system called ammBoost. Our system reduces the amount of on-chain transactions, boosts throughput, and supports blockchain pruning. We devise several techniques to enable layer 2 processing for AMMs while preserving correctness and security of the underlying AMM. We also build a proof-of-concept of ammBoost for a Uniswap-inspired use case to empirically evaluate its performance. Our experiments show that ammBoost decreases the gas cost by 94.53% and the chain growth by at least 80%, and that it can support up to 500x of the daily traffic volume observed for Uniswap in practice.

Cardiovascular diseases (CVDs) and congenital heart diseases (CHD) pose significant global health challenges. Fluoroscopy-guided endovascular interventions, though effective, are accompanied by ionizing radiation concerns, especially in pediatric cases. Magnetic resonance imaging (MRI) emerges as a radiation-free alternative, offering superior soft tissue visualization and functional insights. However, the lack of compatible instruments remains a hurdle. We present two novel catheter systems, a tendon-driven steerable catheter and an active tracking Tiger-shaped catheter, fabricated using a unique fiber drawing technique. These catheters, showcasing mechanical properties similar to commercial counterparts, have undergone rigorous in-vitro and in-vivo testing, yielding promising outcomes. This innovative approach has the potential to streamline medical device development, thus enhancing patient care in MR-guided interventions.

Cryptocurrencies and blockchain technology provide an innovative model for reshaping digital services. Driven by the movement toward Web 3.0, recent systems started to provide distributed services, such as computation outsourcing or file storage, on top of the currency exchange medium. By allowing anyone to join and collect payments for serving others, these systems create decentralized markets for trading digital resources. Yet, there is still a big gap between the promise of these markets and their practical viability. Existing initiatives are still early-stage and have already encountered security and efficiency obstacles. At the same time, existing work around promising ideas, specifically sidechains, fall short in exploiting their full potential in addressing these problems. To bridge this gap, we propose chainBoost, a secure performance booster for decentralized resource markets. It expedites service related operations, reduces the blockchain size, and supports flexible service-payment exchange modalities at low overhead. At its core, chainBoost employs a sidechain, that has a (security and semantic) mutual-dependence with the mainchain, to which the system offloads heavy/frequent operations. To enable it, we develop a novel sidechain architecture composed of temporary and permanent blocks, a block suppression mechanism to prune the sidechain, a syncing protocol to permit arbitrary data exchange between the two chains, and an autorecovery protocol to support robustness and resilience. We analyze the security of chainBoost, and implement a proof-of-concept prototype for a distributed file storage market as a use case. For a market handling around 2000 transactions per round, our experiments show up to 11x improvement in throughput and 94% reduction in confirmation time. They also show that chainBoost can reduce the main blockchain size by around 90%., Comment: To appear in IEEE EuroS&P 2024

Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for prediction is crucial in advancing fair and trustworthy AI. While several promising methods for removing attribute bias in neural networks have been proposed, their limitations remain under-explored. To that end, in this work, we mathematically and empirically reveal the limitation of existing attribute bias removal methods in presence of strong bias and propose a new method that can mitigate this limitation. Specifically, we first derive a general non-vacuous information-theoretical upper bound on the performance of any attribute bias removal method in terms of the bias strength, revealing that they are effective only when the inherent bias in the dataset is relatively weak. Next, we derive a necessary condition for the existence of any method that can remove attribute bias regardless of the bias strength. Inspired by this condition, we then propose a new method using an adversarial objective that directly filters out protected attributes in the input space while maximally preserving all other attributes, without requiring any specific target label. The proposed method achieves state-of-the-art performance in both strong and moderate bias settings. We provide extensive experiments on synthetic, image, and census datasets, to verify the derived theoretical bound and its consequences in practice, and evaluate the effectiveness of the proposed method in removing strong attribute bias., Comment: 35 pages, 18 figures, 32 tables. This work is an extended version of our paper (arXiv:2310.04955). Code will be released at https://github.com/jiazhi412/strong_attribute_bias

Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI. While several promising methods for removing attribute bias in neural networks have been proposed, their limitations remain under-explored. In this work, we mathematically and empirically reveal an important limitation of attribute bias removal methods in presence of strong bias. Specifically, we derive a general non-vacuous information-theoretical upper bound on the performance of any attribute bias removal method in terms of the bias strength. We provide extensive experiments on synthetic, image, and census datasets to verify the theoretical bound and its consequences in practice. Our findings show that existing attribute bias removal methods are effective only when the inherent bias in the dataset is relatively weak, thus cautioning against the use of these methods in smaller datasets where strong attribute bias can occur, and advocating the need for methods that can overcome this limitation., Comment: 15 pages, 4 figures, 3 tables. To appear in Algorithmic Fairness through the Lens of Time Workshop at NeurIPS 2023

Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling. To resolve this restriction, weakly supervised CRL methods were introduced. To evaluate CRL performance, four existing datasets, Pendulum, Flow, CelebA(BEARD) and CelebA(SMILE), are utilized. However, existing CRL datasets are limited to simple graphs with few generative factors. Thus we propose two new datasets with a larger number of diverse generative factors and more sophisticated causal graphs. In addition, current real datasets, CelebA(BEARD) and CelebA(SMILE), the originally proposed causal graphs are not aligned with the dataset distributions. Thus, we propose modifications to them.