A fair distribution of oil and gas revenues for Newfoundland and Labrador: a feasibility study

This study aimed to investigate a way to a sustainable future of Newfoundland and Labrador through the introduction of a Sovereign Wealth Fund (SWF) using the province’s oil and gas resources. The theoretical frameworks for this study are the capital approach of weak sustainability, environmental justice, and resource curse. With these frameworks, a comparative case study analysis has been adopted to investigate cases of two jurisdictions that are already successfully operating SWFs funded by oil and gas revenue to build more sustainable societies by sustaining their economic, environmental, human, and social capitals. Based on the case studies, this feasibility study examined the following questions: 1) What impacts did the Norwegian SWF have on the sustainability of Norway? 2) What impacts did the Alaskan SWF have on the sustainability of Alaska? 3) How does the oil and gas industry affect Newfoundland and Labrador's sustainability and what improvements should be made? 4) Will Newfoundland and Labrador be able to ensure sustainability with their oil and gas revenue? The study concludes that introducing a SWF could help to ensure the sustainability of Newfoundland and Labrador, with several supporting policies, such as diversified funding sources, building a framework that can benefit local people, and achieving social consensus

Cost-Age-Time Data Organized Garbage Collection

NAND flash based solid state drives (SSDs) require out-of-place updating due to the characteristics of flash memories. In addition, due to the mismatched granularity between read/write and erase operations, a cleaning policy involving garbage collection and wear leveling has to perform data migration incurring high overhead. Another challenge is that flash devices can tolerate a limited number of erases. This paper proposes the Cost-Age-Time Data Organized Garbage Collection (CATDOG) scheme, which clusters data based on their update frequencies to reduce the overhead of data migration, trades off between endurance and performance, and efficiently erases multiple blocks to reduce garbage collection latency. To the best of our knowledge, this is the first paper to provide a holistic discussion on the effects of combining all three factors. Our simulation study shows that CATDOG achieves a maximum of 3.54 times higher throughput performance and 1.18 times greater endurance than a selected baseline for a heavy write workload

Passive set-position modulation approach for haptics with slow, variable, and asynchronous update

We consider the following problem in haptics: information update from the virtual world is slow w.r.t. the local servo-loop rate of the haptic device, and the information transmission/update between the haptic device and the virtual world is of variable rate and/or asyn-chronous. For this, we propose a novel control framework, that, by relying on our recently proposed passive set-position modulation (PSPM) and discrete-time passive non-iterative integrators, enables us to enforce two-port hybrid (i.e. continuous-discrete) passivity for such slow and variable/asynchronous haptics as well as to separate the virtual world simulation design from the device’s servo-loop tuning. Relevant experimental results are also presented.

Read-only Prompt Optimization for Vision-Language Few-shot Learning

In recent years, prompt tuning has proven effective in adapting pre-trained vision-language models to downstream tasks. These methods aim to adapt the pre-trained models by introducing learnable prompts while keeping pre-trained weights frozen. However, learnable prompts can affect the internal representation within the self-attention module, which may negatively impact performance variance and generalization, especially in data-deficient settings. To address these issues, we propose a novel approach, Read-only Prompt Optimization (RPO). RPO leverages masked attention to prevent the internal representation shift in the pre-trained model. Further, to facilitate the optimization of RPO, the read-only prompts are initialized based on special tokens of the pre-trained model. Our extensive experiments demonstrate that RPO outperforms CLIP and CoCoOp in base-to-new generalization and domain generalization while displaying better robustness. Also, the proposed method achieves better generalization on extremely data-deficient settings, while improving parameter efficiency and computational overhead. Code is available at https://github.com/mlvlab/RPO.Comment: Accepted at ICCV202

Interferometer Response to Geontropic Fluctuations

We model vacuum fluctuations in quantum gravity with a scalar field, characterized by a high occupation number, coupled to the metric. The occupation number of the scalar is given by a thermal density matrix, whose form is motivated by fluctuations in the vacuum energy, which have been shown to be conformal near a light-sheet horizon. For the experimental measurement of interest in an interferometer, the size of the energy fluctuations is fixed by the area of a surface bounding the volume of spacetime being interrogated by an interferometer. We compute the interferometer response to these "geontropic" scalar-metric fluctuations, and apply our results to current and future interferometer measurements, such as LIGO and the proposed GQuEST experiment.Comment: 17 pages, 6 figure