Fill’er Up: Automating Hometown News Releases

Everyone wants to see their name in print when a special award or honor is won. And such releases bring praise to the news staff. But preparing these individualized news articles for just one or two papers is time-consuming and detracts from writing other releases that have mass market appeal. Assigning students to write the articles and seek addresses for papers requires a lot of supervision. To address these needs, a form was created on the World Wide Web to generate automatic news stories for award winners. The form allows the recipient of the award to share in the responsibility for accurate dissemination

Attenuation of kainic acid-induced epilepsy by butyrate is associated with inhibition of glial activation

Purpose: To investigate the function and potential therapeutic relevance of butyrate in epilepsy using rat models of kainic acid (KA)-induced epilepsy.Methods: The neurotoxin KA was applied to rats and rat astrocytes to establish models of epilepsy in vivo and in vitro. Multiple parameters, including behavioural seizure scores, were evaluated in rats and rat astrocytes treated with KA alone or in combination with butyrate. Western blot was performed to examine the levels of phosphorylated extracellular signal-related kinase (p-ERK), proinflammatory cytokine (IL-1ß), and glial fibrillary acidic protein (GFAP).Results: Significant increases were observed in the seizure-related proteins p-ERK and GFAP and in the proinflammatory cytokine IL-1ß in KA-treated rats and rat astrocytes (p < 0.05). Butyrate treatment attenuated KA-induced epileptic behaviour in rats and significantly reduced the expression of p-ERK, GFAP, and IL-1ß in a dose-dependent manner (p < 0.05).Conclusion: Butyrate has potential as a treatment for epilepsy by inhibiting the activation of p-ERK, astrogliosis, and inflammation, which were induced by KA in rats and rat astrocytes.Keywords: Kainic acid, Epilepsy, Butyrate, Glial activation, Astrogliosi

Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond

A global effort has been initiated to reduce the worldwide greenhouse gas (GHG) emissions, primarily carbon emissions, by half by 2030 and reach net-zero by 2050. The development of 6G must also be compliant with this goal. Unfortunately, developing a sustainable and net-zero emission systems to meet the users' fast growing demands on mobile services, especially smart services and applications, may be much more challenging than expected. Particularly, despite the energy efficiency improvement in both hardware and software designs, the overall energy consumption and carbon emission of mobile networks are still increasing at a tremendous speed. The growing penetration of resource-demanding AI algorithms and solutions further exacerbate this challenge. In this article, we identify the major emission sources and introduce an evaluation framework for analyzing the lifecycle of network AI implementations. A novel joint dynamic energy trading and task allocation optimization framework, called DETA, has been introduced to reduce the overall carbon emissions. We consider a federated edge intelligence-based network AI system as a case study to verify the effectiveness of our proposed solution. Experimental results based on a hardware prototype suggest that our proposed solution can reduce carbon emissions of network AI systems by up to 74.9%. Finally, open problems and future directions are discussed

Time-sensitive Learning for Heterogeneous Federated Edge Intelligence

Real-time machine learning has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. We investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow slow-performance edge servers to be removed from participating in model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number. We develop an analytical expression to characterize the impact of staleness effect of asynchronous coordination and straggler effect of FL on the time consumption of TS-FL-ASC. Experimental results show that TS-FL-SC and TS-FL-ASC can provide up to 63% and 28% of reduction, in the overall model training time, respectively.Comment: IEEE Link: https://ieeexplore.ieee.org/document/1001820