Approximation of high quantiles from intermediate quantiles

Motivated by applications requiring quantile estimates for very small probabilities of exceedance, this article addresses estimation of high quantiles for probabilities bounded by powers of sample size with exponents below -1. As regularity assumption, an alternative to the Generalised Pareto tail limit is explored for this purpose. Motivation for the alternative regularity assumption is provided, and it is shown to be equivalent to a limit relation for the logarithm of survival function, the log-GW tail limit, which generalises the GW (Generalised Weibull) tail limit, a generalisation of the Weibull tail limit. The domain of attraction is described, and convergence results are presented for quantile approximation and for a simple quantile estimator based on the log-GW tail. Simulations are presented, and advantages and limitations of log-GW-based estimation of high quantiles are indicated

Interdisciplinary perspectives on aid and local ownership in projects

development projects;development aid;organization theory;local level;aid institutions;intercultural communication;project implementation

Refusing the Burden of Computation: Edge Computing and Sustainable ICT

This paper asks what we can learn from edge computing about the commitment of Big Tech to diminish its ecological footprint. The text starts with the COVID-19 pandemic being framed as opportunity for more sustainability and unpacks edge computing as one of the elements proposed as a solution, next to working from home. It interrogates the discourse behind these solutions, one of technological fixes that allow ‘business as usual’ to continue, undisturbed by government regulations, outsourcing the burden of environmental responsibility to citizens. The paper draws parallels between edge computing, Big Tech’s approach to sustainability and the history of the Sustainable ICT discourse and proposes that to truly diminish ICT’s footprint, a refusal of the burden of computation and digital enclosure (vendor lock-in) is needed, by collectively building and financing network services

A pluriverse of local worlds: A review of Computing within Limits related terminology and practices

Green capitalism is shaping public discourse on how to best deal with the climate crisis, yet doesn’t challenge the ‘business as usual’ of free market capitalism that caused the crisis in the first place. Small scale practices challenging ’business as usual’ aren’t part of public discourse because they are small scale, less visible, often hard to access, easily appropriated by and seemingly unable to compete with the powerful lobby of large corporations. With Big Tech having an increasingly negative impact on the environment, and simultaneously shaping the discourse on how to best tackle the climate crisis, it is important to give voice and visibility to these alternatives. There is a rich diversity of practices and views on how network infrastructures’ impact could be lowered. This study aims to make them visible through a mapping of the different terms currently in circulation used by communities of practice, with the aim of teasing out the diverse thinking informing the infrastructures that are developed, maintained and repaired. The mapping will be based on a review of relevant literature and the results from a survey conducted on Mastodon, an open source decentralized social network with a user base that includes many developers and activists working on sustainability and social justice in relation to computing. The mapping aims to celebrate differences and also show what common ground this pluriverse of small scale community practices share

Deep neural networks with voice entry estimation heuristics for voice separation in symbolic music representations

In this study we explore the use of deep feedforward neural networks for voice separation in symbolic music representations. We experiment with different network architectures, varying the number and size of the hidden layers, and with dropout. We integrate two voice entry estimation heuristics that estimate the entry points of the individual voices in the polyphonic fabric into the models. These heuristics serve to reduce error propagation at the beginning of a piece, which, as we have shown in previous work, can seriously hamper model performance. The models are evaluated on the 48 fugues from Johann Sebastian Bach’s The Well-Tempered Clavier and his 30 inventions—a dataset that we curated and make publicly available. We find that a model with two hidden layers yields the best results. Using more layers does not lead to a significant performance improvement. Furthermore, we find that our voice entry estimation heuristics are highly effective in the reduction of error propagation, improving performance significantly. Our best-performing model outperforms our previous models, where the difference is significant, and, depending on the evaluation metric, performs close to or better than the reported state of the art

A machine learning approach to voice separation in lute tablature

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THE CANADA- U.S. TRADE DISPUTES ON DAIRY AND POULTRY: A GOOD EXAMPLE

International Relations/Trade,