2,468 research outputs found

    DRLCap: Runtime GPU Frequency Capping with Deep Reinforcement Learning

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    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

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution

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    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

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    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

    Annual Report 2022

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    Motivational support intervention to reduce smoking and increase physical activity in smokers not ready to quit: the TARS RCT.

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    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

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    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

    Are Digital and Traditional Financial Services Taxed the Same? A Comprehensive Assessment of Tax Policies in Nine African Countries

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    This background report looks at tax implications for those providing and using digital financial services (DFS), and gives general observations as to whether DFS in Africa are taxed the same as traditional financial services (TFS). There is no categorical answer to this question. It varies country by country, depending on the specific arrangements in their legal and tax framework. Therefore, a country-specific approach is necessary. This report analyses key legislative, tax and regulatory policy instruments to compare the tax framework in nine African countries – Burundi, Côte d’Ivoire, Ghana, Kenya, Rwanda, South Sudan, Tanzania, Uganda and Zimbabwe. The country studies illustrate the diverse experience across the nine African economies, and the tension between the need for greater mobilisation of domestic resources and the desire to see rapid roll-out of digital infrastructure and services. The cross-country assessment highlights areas where the tax situation is different for DFS providers and users, compared to traditional financial institutions and actors. We present a number of preliminary considerations and lessons learned. These can help to shape an optimal tax environment, reduce friction, enhance beneficial competition in the financial services market, and minimise any negative consequences for DFS providers and users that arise within the taxation framework in all countries studied

    INCENTIVES: THEORY IN NETWORK GAMES AND AN APPLICATION FOR COVID-19 VACCINATION

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    Ph.D

    Milwaukee’s Electric Scooter Program: A Review and Analysis of a Municipal Pilot Study of a Shared Micromobility Program

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    Cities are implementing shared micromobility to provide on-demand transportation options. Shared micromobility services have gained popularity in recent yeas as an efficient and sustainable mode of transportation in urban areas such as Milwaukee. Privately-owned modes, such as electric scooters, are being integrated into urban planning and local policies as a convenient option for short-distance travel. The introduction of technology supported, shared micromobility services has improved transport equity by filling in network gaps. Cities look at how e-scooters conveniently fit into a multimodal transportation plan and how they serve a positive public purpose without negatively impacting the public right of way. This paper reviews how cities can safely implement new transport mobility options with the introduction of electric scooters. With the growing need for sustainable, accessible, and efficient transportation options, electric scooters have emerged as an option. This paper examines the various aspects of electric scooter pilot studies implemented in different cities across the United States: Milwaukee, Portland, Seattle, and Baltimore. To ensure the successful integration of electric scooters into existing transportation systems, conducting temporary pilot studies and having proper planning is essential. This paper reviews the many challenges that city officials faced during implementation, exploring the safety concerns, benefits, strategies and how cities created a harmonious framework of the terms and conditions to benefit the residents and the environment
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