Fairness and Efficiency in DAG-based Cryptocurrencies

Bitcoin is a decentralised digital currency that serves as an alternative to existing transaction systems based on an external central authority for security. Although Bitcoin has many desirable properties, one of its fundamental shortcomings is its inability to process transactions at high rates. To address this challenge, many subsequent protocols either modify the rules of block acceptance (longest chain rule) and reward, or alter the graphical structure of the public ledger from a tree to a directed acyclic graph (DAG). Motivated by these approaches, we introduce a new general framework that captures ledger growth for a large class of DAG-based implementations. With this in hand, and by assuming honest miner behaviour, we (experimentally) explore how different DAG-based protocols perform in terms of fairness, i.e., if the block reward of a miner is proportional to their hash power, as well as efficiency, i.e. what proportion of user transactions a ledger deems valid after a certain length of time. Our results demonstrate fundamental structural limits on how well DAG-based ledger protocols cope with a high transaction load. More specifically, we show that even in a scenario where every miner on the system is honest in terms of when they publish blocks, what they point to, and what transactions each block contains, fairness and efficiency of the ledger can break down at specific hash rates if miners have differing levels of connectivity to the P2P network sustaining the protocol

Predicting personal traits from facial images using convolutional neural networks augmented with facial landmark information

We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture for estimating facial attributes and show that they indeed provide an impressive baseline performance. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence. We empirically analyse the performance of our method, showing consistent improvement over the baseline across traits.Microsoft Researc

Fairness and efficiency in DAG-based cryptocurrencies

Bitcoin is a decentralised digital currency that serves as an alternative to existing transaction systems based on an external central authority for security. Although Bitcoin has many desirable properties, one of its fundamental shortcomings is its inability to process transactions at high rates. To address this challenge, many subsequent protocols either modify the rules of block acceptance (longest chain rule) and reward, or alter the graphical structure of the public ledger from a tree to a directed acyclic graph (DAG). Motivated by these approaches, we introduce a new general framework that captures ledger growth for a large class of DAG-based implementations. With this in hand, and by assuming honest miner behaviour, we (experimentally) explore how different DAG-based protocols perform in terms of fairness, as well as efficiency. To do so, we isolate different parameters of the network (such as k, the number of pointers to previous blocks) and study their effect on those performance metrics. Our results demonstrate how the DAG-based ledger protocols described by our framework cope with a high transaction load. More specifically, we show that even in a scenario where every miner on the system is honest in terms of when they publish blocks, what they point to, and what transactions each block contains, fairness and efficiency of this kind of ledgers can break down at specific hash rates if miners have differing levels of connectivity to the P2P network sustaining the protocol

Urban washout: how strong is the rural-background effect?

Objective: To test predictors of practice location of fully qualified Monash University Bachelor of Medicine, Bachelor of Surgery (MBBS) graduates. Design: Cohort survey, 2011. Setting: Australia. Participants: Rural (n=67/129) and urban (n=86/191) background doctors starting at Monash University 1992-1999. Approximately 60% female, 77% married/partnered, 79% Australian-born, mean age 34 years, 31% general practitioners, 72% fully qualified and 80% training/practising in major cities. Main outcome measures: First and current practice location once fully qualified. Intended practice location in 5-10 years. Results: Logistic regression found that rural versus urban background was a significant predictor of rural (outside major city) first practice location (odds ratio (OR) 5.0, 95% confidence interval (CI) 1.3-19.2) and rural current practice location (OR 5.6, 95% CI 1.5-21.2) for fully qualified doctors. General practitioner versus other medical specialists significantly predicted first (OR 7.2, 95% CI 2.1-25.2) or current (OR 3.6, 95% CI 1.1-11.9) rural practice location. Preference for a rural practice location in 5-10 years was predicted by rural background (OR 4.4, 95% CI 1.6-11.8) and positive intention towards rural practice upon completing MBBS (OR 4.6, 95% CI 1.7-12.6). Surveyed in 2011, 28% of those who also responded to the 2006 survey shifted their preferred future practice location from rural to urban communities versus 13% shifting from urban to rural (McNemar-Bowker test, P=0.02). Conclusion: The majority of fully qualified Monash MBBS graduates practicing in rural communities have rural backgrounds. The rural-background effect diminished over time and may need continued support during training and full practice