scholarly journals Improving the accuracy of energy predictive models for multicore CPUs by combining utilization and performance events model variables

2021 ◽  
Vol 151 ◽  
pp. 38-51
Author(s):  
Arsalan Shahid ◽  
Muhammad Fahad ◽  
Ravi Reddy Manumachu ◽  
Alexey Lastovetsky
Keyword(s):  
Predictive Models ◽  
Performance Events ◽  
And Performance ◽  
Multicore Cpus

Film and Performance: Intermedial Intersections

2021 ◽  
pp. 11-22
Author(s):  
Stephen Barber
Keyword(s):  
Digital Media ◽  
Live Performance ◽  
Moving Image ◽  
Image Projection ◽  
Media Events ◽  
Performance Events ◽  
And Performance

Film and performance have always been closely interconnected, from the origins of cinematic projection in 1895. This essay, with a theoretical focus, explores how film and moving-image forms work to transform performance when they intersect with it, and vice versa. It examines how film serves to mediate and ‘reframe’ the experience and the time of live performance events, notably through the incorporation of moving-image elements into the space of performance, and through particular forms of projection and audience perception. It also probes how conceptions of intermediality can be traced specifically through the intersection of film and performance. The essay spans the entirety of moving image culture, beginning with an account of the connections between film and performance in the work of the German innovators of moving-image projection, the Skladanowsky Brothers, and ending with an examination of the work of the contemporary Lebanese filmmaker and performance artist, Rabih Mroué, whose work resonates with early cinema’s performative strategies but focuses also on current digital media events such as the dangerous ‘performative’ public filming with iPhones of government snipers in the streets of Syria.

Mitterer, Wolfgang (1958--)

2018 ◽  
Author(s):  
Aaron Cassidy
Keyword(s):  
Chamber Music ◽  
Performing Arts ◽  
Electroacoustic Music ◽  
Live Electronics ◽  
Performance Events ◽  
And Performance ◽  
Orchestra Music ◽  
And Function ◽  
Granular Layers ◽  
The University

Wolfgang Mitterer (1958--) is an Austrian composer and organist noted for his work with live electronics and improvisation. Born on 6 June, 1958 in Lienz, East Tyrol, Mitterer studied organ and composition at the University of Music and the Performing Arts Vienna, followed by a year-long residency at the studio for electroacoustic music (EMS) in Stockholm. An exceptionally prolific composer, Mitterer’s output spans a staggeringly broad range of approaches to music making, including works for tape, chamber music of various formations, experimental pop songs (Sopop), works for large orchestra, music for theatre and opera, music for film, and sprawling site-specific installations and performance events (turmbau zu babel, for example, is scored for 4,200 singers, 22 drums, 48 brass players, and 8-channel tape). His works list includes over 200 entries and demonstrates a particularly catholic, pluralistic, non-dogmatic approach to instrumentation, duration, venue, scale, and function. Despite this diversity, Mitterer’s work maintains several important central tendencies: stylistically, the music is often characterized by layers of crackles, twitches, clicks, and pops (both electronic and acoustic), with a rustling, flickering, chirping, gestural energy. These more fragmented, granular layers are quite often combined with gradual, elongated, atmospheric, and lyrical material, though generally a sense of instability and unpredictability remains.

scholarly journals A Comparative Study of Methods for Measurement of Energy of Computing

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2204 ◽  
Author(s):  
Muhammad Fahad ◽  
Arsalan Shahid ◽  
Ravi Reddy Manumachu ◽  
Alexey Lastovetsky
Keyword(s):  
Predictive Models ◽  
Ground Truth ◽  
System Level ◽  
Average Error ◽  
Level Energy ◽  
Physical Measurements ◽  
Wide Range ◽  
External Power ◽  
On Chip ◽  
Multicore Cpus

Energy of computing is a serious environmental concern and mitigating it is an important technological challenge. Accurate measurement of energy consumption during an application execution is key to application-level energy minimization techniques. There are three popular approaches to providing it: (a) System-level physical measurements using external power meters; (b) Measurements using on-chip power sensors and (c) Energy predictive models. In this work, we present a comprehensive study comparing the accuracy of state-of-the-art on-chip power sensors and energy predictive models against system-level physical measurements using external power meters, which we consider to be the ground truth. We show that the average error of the dynamic energy profiles obtained using on-chip power sensors can be as high as 73% and the maximum reaches 300% for two scientific applications, matrix-matrix multiplication and 2D fast Fourier transform for a wide range of problem sizes. The applications are executed on three modern Intel multicore CPUs, two Nvidia GPUs and an Intel Xeon Phi accelerator. The average error of the energy predictive models employing performance monitoring counters (PMCs) as predictor variables can be as high as 32% and the maximum reaches 100% for a diverse set of seventeen benchmarks executed on two Intel multicore CPUs (one Haswell and the other Skylake). We also demonstrate that using inaccurate energy measurements provided by on-chip sensors for dynamic energy optimization can result in significant energy losses up to 84%. We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy. Finally, we present the lessons learned, our recommendations for the use of on-chip sensors and energy predictive models and future directions.

The Gospel of Mark, Orality Studies and Performance Criticism

2018 ◽  
Vol 25 (3-4) ◽  
pp. 350-393
Author(s):  
Pieter J.J. Botha
Keyword(s):  
New Testament ◽  
Gospel Of Mark ◽  
Critical Studies ◽  
Jesus Movement ◽  
Greco Roman ◽  
Performance Criticism ◽  
The New Testament ◽  
Performance Events ◽  
History Of ◽  
And Performance

Abstract Orality/aurality is recognised by a growing number of scholars as a significant aspect of the context of New Testament texts. As part of the exploration of the oral features of New Testament texts some are turning to Greco-Roman storytelling and oratory, informed by performance studies. A selection of these explorations are discussed to introduce scholarship that attempts to identify various elements of performance events in the early church as a basis for re-thinking our ways of studying and our interpretations of the New Testament writings in their original context. The obstacles to such efforts are considerable, but some significant gains have been made. Focusing on research on the Gospel of Mark, this discussion shows how performance critical studies allow new insights into the origins of the Gospels, leading to interesting new and meaningful perspectives on the history of the early Jesus movement with specific attention to the role telling and presenting the Markan story played.

Theatre Ecology: Environments and Performance Events (review)

Modern Drama ◽  
2009 ◽  
Vol 52 (2) ◽  
pp. 247-249
Author(s):  
Theresa J. May
Keyword(s):  
Performance Events ◽  
And Performance

Represent 255: language, style and the construction of identity in Tanzanian English hip-hop

2020 ◽  
Vol 6 (s4) ◽  
Author(s):  
Nikitta Dede Adjirakor
Keyword(s):  
Hip Hop ◽  
Symbolic Meaning ◽  
Urban Identity ◽  
Popular Imagination ◽  
Construction Of Identity ◽  
Widespread Adoption ◽  
Sustained Use ◽  
Performance Events ◽  
English Speaking ◽  
And Performance

Abstract From its encouraged and sustained use during colonial times, through to the creation of the Tanzanian state, the Swahili language has been consistently constructed as one of the key facets of Tanzanian identity. After the emergence of hip-hop in Tanzania, the shift from English to Swahili was instrumental to its widespread adoption, with English gaining a symbolic meaning as a status marker as well as a language for international positioning. This article argues that recently, a rising number of hip-hop artists style themselves as purely English-speaking artists to construct a Tanzanian identity that challenges the dominant positioning of Swahili. To this end, I explore through selected texts how English is used to construct a cosmopolitan niche and urban identity that serves as a counternarrative to the dominance of Swahili in the popular imagination. Through hip-hop songs, groups and performance events, I show how English is used to evoke experiences of belonging that are positioned as authentic narratives that juxtapose rather than contradict a Tanzanian identity.

scholarly journals Combined Residential-Location and Transportation Models

1979 ◽  
Vol 11 (11) ◽  
pp. 1241-1265 ◽  
Author(s):  
M Los
Keyword(s):  
Predictive Models ◽  
Housing Stock ◽  
Residential Location ◽  
Transportation System ◽  
Equilibrium Models ◽  
Modal Choice ◽  
Mathematical Programs ◽  
And Performance ◽  
The Impact ◽  
Solution Methodology

Predictive models which combine residential location with transportation are presented in this paper. These models predict residential location and the use and performance of the transportation system for exogenously given total housing stock and employment locations. One can use them to determine the impact on the transportation system of changes in the supply of housing or reciprocally to determine the impact on housing choices of new investments in the transportation system. They are formulated as mathematical programs generalizing the Herbert–Stevens model of residential location to include previously proposed equilibrium models of trip assignment, trip distribution, and modal choice. A solution methodology is proposed for prediction and calibration.

scholarly journals Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Luis Fregoso-Aparicio ◽  
Julieta Noguez ◽  
Luis Montesinos ◽  
José A. García-García
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Deep Learning ◽  
Predictive Models ◽  
Glucose Levels ◽  
Learning Techniques ◽  
Complementary Techniques ◽  
And Performance ◽  
The One

AbstractDiabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models.

scholarly journals Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period

2020 ◽  
Vol 11 (2) ◽  
pp. 36-50
Author(s):  
Aljaž Ferencek ◽  
Davorin Kofjač ◽  
Andrej Škraba ◽  
Blaž Sašek ◽  
Mirjana Kljajić Borštnar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Predictive Models ◽  
Discrete Event ◽  
Quality Data ◽  
Call Rate ◽  
Learning Models ◽  
Data Set ◽  
Warranty Period ◽  
And Performance

AbstractBackground: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models.Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate.Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance.Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results.Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data.

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