6,157 research outputs found
Annotated bibliography of community music research review, AHRC connected communities programme
This research review, consisting of a 90-entry annotated bibliography, was produced as part of an AHRC Connected Communities programme project entitled Community Music, its History and Current Practice, its Constructions of ‘Community’, Digital Turns and Future Soundings. It supports a 2,500 word report written with this same title for the AHRC
Commissioning of the CMS High Level Trigger
The CMS experiment will collect data from the proton-proton collisions
delivered by the Large Hadron Collider (LHC) at a centre-of-mass energy up to
14 TeV. The CMS trigger system is designed to cope with unprecedented
luminosities and LHC bunch-crossing rates up to 40 MHz. The unique CMS trigger
architecture only employs two trigger levels. The Level-1 trigger is
implemented using custom electronics, while the High Level Trigger (HLT) is
based on software algorithms running on a large cluster of commercial
processors, the Event Filter Farm. We present the major functionalities of the
CMS High Level Trigger system as of the starting of LHC beams operations in
September 2008. The validation of the HLT system in the online environment with
Monte Carlo simulated data and its commissioning during cosmic rays data taking
campaigns are discussed in detail. We conclude with the description of the HLT
operations with the first circulating LHC beams before the incident occurred
the 19th September 2008
Search based software engineering: Trends, techniques and applications
© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.
This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E
Symbolic Discovery of Optimization Algorithms
We present a method to formulate algorithm discovery as program search, and
apply it to discover optimization algorithms for deep neural network training.
We leverage efficient search techniques to explore an infinite and sparse
program space. To bridge the large generalization gap between proxy and target
tasks, we also introduce program selection and simplification strategies. Our
method discovers a simple and effective optimization algorithm,
(\textit{Evo\textbf{L}\textbf{i}\textbf{o}\textbf{n}tum}).
It is more memory-efficient than Adam as it only keeps track of the momentum.
Different from adaptive optimizers, its update has the same magnitude for each
parameter calculated through the sign operation. We compare Lion with widely
used optimizers, such as Adam and Adafactor, for training a variety of models
on different tasks. On image classification, Lion boosts the accuracy of ViT by
up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On
vision-language contrastive learning, we achieve 88.3% and
91.1% accuracy on ImageNet, surpassing the previous best
results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms
Adam by achieving a better FID score and reducing the training compute by up to
2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion
exhibits a similar or better performance compared to Adam. Our analysis of Lion
reveals that its performance gain grows with the training batch size. It also
requires a smaller learning rate than Adam due to the larger norm of the update
produced by the sign function. Additionally, we examine the limitations of Lion
and identify scenarios where its improvements are small or not statistically
significant. The implementation of Lion is publicly available.Comment: 30 pages, update the tuning instruction
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