Go/No-Go Ratios Modulate Inhibition-Related Brain Activity: An Event-Related Potential Study

(1) Background: Response inhibition refers to the conscious ability to suppress behavioral responses, which is crucial for effective cognitive control. Currently, research on response inhibition remains controversial, and the neurobiological mechanisms associated with response inhibition are still being explored. The Go/No-Go task is a widely used paradigm that can be used to effectively assess response inhibition capability. While many studies have utilized equal numbers of Go and No-Go trials, how different ratios affect response inhibition remains unknown; (2) Methods: This study investigated the impact of different ratios of Go and No-Go conditions on response inhibition using the Go/No-Go task combined with event-related potential (ERP) techniques; (3) Results: The results showed that as the proportion of Go trials decreased, behavioral performance in Go trials significantly improved in terms of response time, while error rates in No-Go trials gradually decreased. Additionally, the NoGo-P3 component at the central average electrodes (Cz, C1, C2, FCz, FC1, FC2, PCz, PC1, and PC2) exhibited reduced amplitude and latency; (4) Conclusions: These findings indicate that different ratios in Go/No-Go tasks influence response inhibition, with the brain adjusting processing capabilities and rates for response inhibition. This effect may be related to the brain's predictive mechanism model

Bidding for Highly Available Services with Low Price in Spot Instance Market

ABSTRACT Amazon EC2 has built the Spot Instance Marketplace and offers a new type of virtual machine instances called as spot instances. These instances are less expensive but considered failure-prone. Despite the underlying hardware status, if the bidding price is lower than the market price, such an instance will be terminated. Distributed systems can be built from the spot instances to reduce the cost while still tolerating instance failures. For example, embarrassingly parallel jobs can use the spot instances by re-executing failed tasks. The bidding framework for such jobs simply selects the spot price as the bid. However, highly available services like lock service or storage service cannot use the similar techniques for availability consideration. The spot instance failure model is different to that of normal instances (fixed failure probability in traditional distributed model). This makes the bidding strategy more complex to keep service availability for such systems. We formalize this problem and propose an availability and cost aware bidding framework. Experiment results show that our bidding framework can reduce the costs of a distributed lock service and a distributed storage service by 81.23% and 85.32% respectively while still keeping availability level the same as it is by using on-demand instances