2,223 research outputs found
DRLCap: Runtime GPU Frequency Capping with Deep Reinforcement Learning
Power and energy consumption is the limiting factor of modern computing systems. As the GPU becomes a mainstream computing device, power management for GPUs becomes increasingly important. Current works focus on GPU kernel-level power management, with challenges in portability due to architecture-specific considerations. We present DRLCap , a general runtime power management framework intended to support power management across various GPU architectures. It periodically monitors system-level information to dynamically detect program phase changes and model the workload and GPU system behavior. This elimination from kernel-specific constraints enhances adaptability and responsiveness. The framework leverages dynamic GPU frequency capping, which is the most widely used power knob, to control the power consumption. DRLCap employs deep reinforcement learning (DRL) to adapt to the changing of program phases by automatically adjusting its power policy through online learning, aiming to reduce the GPU power consumption without significantly compromising the application performance. We evaluate DRLCap on three NVIDIA and one AMD GPU architectures. Experimental results show that DRLCap improves prior GPU power optimization strategies by a large margin. On average, it reduces the GPU energy consumption by 22% with less than 3% performance slowdown on NVIDIA GPUs. This translates to a 20% improvement in the energy efficiency measured by the energy-delay product (EDP) over the NVIDIA default GPU power management strategy. For the AMD GPU architecture, DRLCap saves energy consumption by 10%, on average, with a 4% percentage loss, and improves energy efficiency by 8%
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution
Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners
Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives
With the dramatic advances in deep learning technology, machine learning research is focusing on improving the interpretability of model predictions as well as prediction performance in both basic and applied research. While deep learning models have much higher prediction performance than conventional machine learning models, the specific prediction process is still difficult to interpret and/or explain. This is known as the black-boxing of machine learning models and is recognized as a particularly important problem in a wide range of research fields, including manufacturing, commerce, robotics, and other industries where the use of such technology has become commonplace, as well as the medical field, where mistakes are not tolerated.Focusing on natural language processing tasks, we consider interpretability as the presentation of the contribution of a prediction to an input word in a recurrent neural network. In interpreting predictions from deep learning models, much work has been done mainly on visualization of importance mainly based on attention weights and gradients for the inference results. However, it has become clear in recent years that there are not negligible problems with these mechanisms of attention mechanisms and gradients-based techniques. The first is that the attention weight learns which parts to focus on, but depending on the task or problem setting, the relationship with the importance of the gradient may be strong or weak, and these may not always be strongly related. Furthermore, it is often unclear how to integrate both interpretations. From another perspective, there are several unclear aspects regarding the appropriate application of the effects of attention mechanisms to real-world problems with large datasets, as well as the properties and characteristics of the applied effects. This dissertation discusses both basic and applied research on how attention mechanisms improve the performance and interpretability of machine learning models.From the basic research perspective, we proposed a new learning method that focuses on the vulnerability of the attention mechanism to perturbations, which contributes significantly to prediction performance and interpretability. Deep learning models are known to respond to small perturbations that humans cannot perceive and may exhibit unintended behaviors and predictions. Attention mechanisms used to interpret predictions are no exception. This is a very serious problem because current deep learning models rely heavily on this mechanism. We focused on training techniques using adversarial perturbations, i.e., perturbations that dares to deceive the attention mechanism. We demonstrated that such an adversarial training technique makes the perturbation-sensitive attention mechanism robust and enables the presentation of highly interpretable predictive evidence. By further extending the proposed technique to semi-supervised learning, a general-purpose learning model with a more robust and interpretable attention mechanism was achieved.From the applied research perspective, we investigated the effectiveness of the deep learning models with attention mechanisms validated in the basic research, are in real-world applications. Since deep learning models with attention mechanisms have mainly been evaluated using basic tasks in natural language processing and computer vision, their performance when used as core components of applications and services has often been unclear. We confirm the effectiveness of the proposed framework with an attention mechanism by focusing on the real world of applications, particularly in the field of computational advertising, where the amount of data is large, and the interpretation of predictions is necessary. The proposed frameworks are new attempts to support operations by predicting the nature of digital advertisements with high serving effectiveness, and their effectiveness has been confirmed using large-scale ad-serving data.In light of the above, the research summarized in this dissertation focuses on the attention mechanism, which has been the focus of much attention in recent years, and discusses its potential for both basic research in terms of improving prediction performance and interpretability, and applied research in terms of evaluating it for real-world applications using large data sets beyond the laboratory environment. The dissertation also concludes with a summary of the implications of these findings for subsequent research and future prospects in the field.博士(工学)法政大学 (Hosei University
Motivational support intervention to reduce smoking and increase physical activity in smokers not ready to quit: the TARS RCT.
BACKGROUND: Physical activity can support smoking cessation for smokers wanting to quit, but there have been no studies on supporting smokers wanting only to reduce. More broadly, the effect of motivational support for such smokers is unclear. OBJECTIVES: The objectives were to determine if motivational support to increase physical activity and reduce smoking for smokers not wanting to immediately quit helps reduce smoking and increase abstinence and physical activity, and to determine if this intervention is cost-effective. DESIGN: This was a multicentred, two-arm, parallel-group, randomised (1 : 1) controlled superiority trial with accompanying trial-based and model-based economic evaluations, and a process evaluation. SETTING AND PARTICIPANTS: Participants from health and other community settings in four English cities received either the intervention (n = 457) or usual support (n = 458). INTERVENTION: The intervention consisted of up to eight face-to-face or telephone behavioural support sessions to reduce smoking and increase physical activity. MAIN OUTCOME MEASURES: The main outcome measures were carbon monoxide-verified 6- and 12-month floating prolonged abstinence (primary outcome), self-reported number of cigarettes smoked per day, number of quit attempts and carbon monoxide-verified abstinence at 3 and 9 months. Furthermore, self-reported (3 and 9 months) and accelerometer-recorded (3 months) physical activity data were gathered. Process items, intervention costs and cost-effectiveness were also assessed. RESULTS: The average age of the sample was 49.8 years, and participants were predominantly from areas with socioeconomic deprivation and were moderately heavy smokers. The intervention was delivered with good fidelity. Few participants achieved carbon monoxide-verified 6-month prolonged abstinence [nine (2.0%) in the intervention group and four (0.9%) in the control group; adjusted odds ratio 2.30 (95% confidence interval 0.70 to 7.56)] or 12-month prolonged abstinence [six (1.3%) in the intervention group and one (0.2%) in the control group; adjusted odds ratio 6.33 (95% confidence interval 0.76 to 53.10)]. At 3 months, the intervention participants smoked fewer cigarettes than the control participants (21.1 vs. 26.8 per day). Intervention participants were more likely to reduce cigarettes by ≥ 50% by 3 months [18.9% vs. 10.5%; adjusted odds ratio 1.98 (95% confidence interval 1.35 to 2.90] and 9 months [14.4% vs. 10.0%; adjusted odds ratio 1.52 (95% confidence interval 1.01 to 2.29)], and reported more moderate-to-vigorous physical activity at 3 months [adjusted weekly mean difference of 81.61 minutes (95% confidence interval 28.75 to 134.47 minutes)], but not at 9 months. Increased physical activity did not mediate intervention effects on smoking. The intervention positively influenced most smoking and physical activity beliefs, with some intervention effects mediating changes in smoking and physical activity outcomes. The average intervention cost was estimated to be £239.18 per person, with an overall additional cost of £173.50 (95% confidence interval -£353.82 to £513.77) when considering intervention and health-care costs. The 1.1% absolute between-group difference in carbon monoxide-verified 6-month prolonged abstinence provided a small gain in lifetime quality-adjusted life-years (0.006), and a minimal saving in lifetime health-care costs (net saving £236). CONCLUSIONS: There was no evidence that behavioural support for smoking reduction and increased physical activity led to meaningful increases in prolonged abstinence among smokers with no immediate plans to quit smoking. The intervention is not cost-effective. LIMITATIONS: Prolonged abstinence rates were much lower than expected, meaning that the trial was underpowered to provide confidence that the intervention doubled prolonged abstinence. FUTURE WORK: Further research should explore the effects of the present intervention to support smokers who want to reduce prior to quitting, and/or extend the support available for prolonged reduction and abstinence. TRIAL REGISTRATION: This trial is registered as ISRCTN47776579. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 4. See the NIHR Journals Library website for further project information
“The future is blurry”: The (hydro)power relations of the Muskrat Falls Project
The Canadian Muskrat Falls hydroelectric project (MFP) has presented social, political, economic and wellbeing challenges to the province of Newfoundland and Labrador for over a decade. Despite significant public discussion on the economic issues associated with MFP, the lived experience of Inuit from the affected area has received less attention. This research aims to share Inuit perspectives in Rigolet, Nunatsiavut, the community anticipated to be most affected by the project, to inform health and social responses by government and grassroots organizations. Through a sociological approach guided by Indigenous research methodologies, this research employed culturally responsive and creative methods including semi-structured interviews, surveys, and participatory photography. The research found that participants positioned the MFP within the social and historical context of a previous (1960s-70s) hydroelectric project, the Upper Churchill Falls project, which shapes their contemporary questions and concerns. Participants also associate implementation of MFP with colonialism, as they feel they have not been adequately consulted or informed, a continuation of colonial hierarchies of knowledge. Rigolet residents also expressed uncertainty about the social, cultural, and health impacts of potential methylmercury contamination and wider environmental changes the project may cause. The power relations associated with the hydroelectric project has resulted in a ‘silencing’ of concerns over time, with some participants changing their diet because of contamination concerns for traditional foods critical to local diets, cultural practices, and connections to the land. Results of this study have important implications for public health and health risk communication strategies, as traditional foods and associated land-based activities are known to benefit Inuit physical, mental, and cultural health and wellbeing. Overall, the dissertation demonstrates how the MFP fits within a settler colonial structure within Canada, especially as Indigenous communities have been and continue to be sites for resource extraction. This system of exploitation contrasts with Inuit perspectives on the role and importance of the land and environment in social life and relationships. The research makes several recommendations for improving health risk communications, including the importance of: improved health risk communication; the delivery of clear scientific data; facilitating access to traditional foods; supporting safe ice and water travel; and improved consultation and environmental assessment processes
Economic and Social Consequences of the COVID-19 Pandemic in Energy Sector
The purpose of the Special Issue was to collect the results of research and experience on the consequences of the COVID-19 pandemic for the energy sector and the energy market, broadly understood, that were visible after a year. In particular, the impact of COVID-19 on the energy sector in the EU, including Poland, and the US was examined. The topics concerned various issues, e.g., the situation of energy companies, including those listed on the stock exchange, mining companies, and those dealing with renewable energy. The topics related to the development of electromobility, managerial competences, energy expenditure of local government units, sustainable development of energy, and energy poverty during a pandemic were also discussed
West of England e-scooter trial evaluation final report
The e-scooter rental trial in the West of England started in October 2020 and is part of an England-wide programme of e-scooter trials in cities and towns overseen by the Department for Transport (DfT). There are two operating areas in the West of England. One covers a combined area within Bristol City and South Gloucestershire (referred to as Bristol). The other covers Bath city centre. The trial provides two rental options: Hop-on Hop-Off (HOHO) and Long-Term Rental (LTR). The Department for Transport has completed a national evaluation to understand the operation and impact of the e-scooter trials across all 32 trial locations. The evaluation has shown the West of England trial has had the most rides by a significant margin. The national evaluation provides useful insights to national trends with some comparisons provided between areas. Given the significance of the e-scooter operations in the West of England, and a desire to learn from the trial to inform longer term policy, the Combined Authority commissioned a local evaluation within Bristol and Bath. It adds significantly to insights over and above those emerging from the national evaluation. Trial operator data and other datasets have been analysed in-depth. Several primary data sets have also been collected.In the West of England, a significant number of users have adopted e-scooters into their way of life. People are using them to get to work/college/university and they support leisure and shopping. A high proportion of users are between the ages of 18 and 35, and the majority of users are male. Take-up has been high due to their ease of use and time saving around Bristol and Bath. E-scooters are replacing trips from all types of transport. The trial has reduced travel related carbon emissions. Data on e-scooter safety is not robust enough to draw firm conclusions, but e-scooter riding may be riskier than cycling. E-scooter users thought that better infrastructure is needed. A lower proportion of e-scooter riders wear a cycle helmet than cycle riders. People dislike e-scooters obstructing the footway and some people fear e-scooters being ridden. These two issues particularly impact blind or partially sighted people. Those responsible for running the trial recognise the importance of well parked e-scooters. The trial has benefitted from strong collaboration between the e-scooter operator, local councils, police, fire service, and the Combined Authority
Impressions in Recommender Systems: Present and Future
Impressions are a novel data source providing researchers and practitioners with more details about user interactions and their context. In particular, an impression contain the items shown on screen to users, alongside users' interactions toward such items. In recent years, interest in impressions has thrived, and more papers use impressions in recommender systems. Despite this, the literature does not contain a comprehensive review of the current topics and future directions. This work summarizes impressions in recommender systems under three perspectives: recommendation models, datasets with impressions, and evaluation methodologies. Then, we propose several future directions with an emphasis on novel approaches. This work is part of an ongoing review of impressions in recommender systems
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