260 research outputs found
effective Rehearsal Methods, Conducting Gestures and Techniques of Pictures at an Exhibition
Includes bibliographical references (pages 1-1)After the Romantic era, which materialized the fundamental concepts of aesthetics, the patriotism among the public was induced from the emancipation of serfs during mid-19th century, and there were Russian musicians who attempted to express the unique color of Russia in music and free their music from the influence of German music. Amidst such movements, a group of Russian nationalist composers known as ???the Five??? was formed, and among this group, Mussorgsky became the leading figure. One of his well-known piano pieces, Pictures at an Exhibition was the most noted program music. It featured realism escaping from the nationalistic elements and romanticism, which were the main interest of composers at the time.
Many composers have arranged Mussorgsky???s piece, but one of the most played is the orchestration by Ravel. In this paper, I will discuss effective rehearsal methods and conducting techniques for the Ravel orchestration. This paper not only analyses musical structures and meaning intended by the composer, but also identifies potential issues that can occur during rehearsal and discusses whether these issues occurred or not Mussorgsky has added his own unique creative and rich imagination to the piece rather than simply describing the image the picture. This paper will describe how the conductor can express the composer???s intentions through various conducting techniques. Additionally, this study will also describe how issues resolved during rehearsal are reflected in actual performance, and how players were able to establish an open communication channel based on the actual performance held during the Master???s Graduation Recital, presented on December 5, 2014. The rehearsal methods and conducting techniques presented in this paper resulted in a great performance which fully reflected the intentions of the conductor, and it also produced a persuasive interpretation of Pictures at an Exhibition
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure
Self-Feedback DETR for Temporal Action Detection
Temporal Action Detection (TAD) is challenging but fundamental for real-world
video applications. Recently, DETR-based models have been devised for TAD but
have not performed well yet. In this paper, we point out the problem in the
self-attention of DETR for TAD; the attention modules focus on a few key
elements, called temporal collapse problem. It degrades the capability of the
encoder and decoder since their self-attention modules play no role. To solve
the problem, we propose a novel framework, Self-DETR, which utilizes
cross-attention maps of the decoder to reactivate self-attention modules. We
recover the relationship between encoder features by simple matrix
multiplication of the cross-attention map and its transpose. Likewise, we also
get the information within decoder queries. By guiding collapsed self-attention
maps with the guidance map calculated, we settle down the temporal collapse of
self-attention modules in the encoder and decoder. Our extensive experiments
demonstrate that Self-DETR resolves the temporal collapse problem by keeping
high diversity of attention over all layers.Comment: Accepted to ICCV 202
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 12 pages, 2 figure
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