248 research outputs found
Research on Batch Scheduling in Cloud Computing
In the existing cloud computing environment, batch scheduling strategies mainly focus on the management of resources allocation. This paper provides the task scheduling algorithm based on service quality which fully considers priority and scheduling deadline. The improved algorithm combines the advantages of Min-min algorithm with higher throughput and linear programming with global optimization, considers not only all the tasks but also the high priority tasks. The experiment result shows that compared with the Min-min and DBCT the completed tasks of the improved algorithm increase about 10.6% and 22.0%, on the other hand the completed high priority tasks also increases approximately 20% and 40%
DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
Deep Neural Networks have exhibited considerable success in various visual
tasks. However, when applied to unseen test datasets, state-of-the-art models
often suffer performance degradation due to domain shifts. In this paper, we
introduce a novel approach for domain generalization from a novel perspective
of enhancing the robustness of channels in feature maps to domain shifts. We
observe that models trained on source domains contain a substantial number of
channels that exhibit unstable activations across different domains, which are
inclined to capture domain-specific features and behave abnormally when exposed
to unseen target domains. To address the issue, we propose a DomainDrop
framework to continuously enhance the channel robustness to domain shifts,
where a domain discriminator is used to identify and drop unstable channels in
feature maps of each network layer during forward propagation. We theoretically
prove that our framework could effectively lower the generalization bound.
Extensive experiments on several benchmarks indicate that our framework
achieves state-of-the-art performance compared to other competing methods. Our
code is available at https://github.com/lingeringlight/DomainDrop.Comment: Accepted by ICCV2023. The code is available at
https://github.com/lingeringlight/DomainDro
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
Domain generalization (DG) aims to learn a model that generalizes well to
unseen target domains utilizing multiple source domains without re-training.
Most existing DG works are based on convolutional neural networks (CNNs).
However, the local operation of the convolution kernel makes the model focus
too much on local representations (e.g., texture), which inherently causes the
model more prone to overfit to the source domains and hampers its
generalization ability. Recently, several MLP-based methods have achieved
promising results in supervised learning tasks by learning global interactions
among different patches of the image. Inspired by this, in this paper, we first
analyze the difference between CNN and MLP methods in DG and find that MLP
methods exhibit a better generalization ability because they can better capture
the global representations (e.g., structure) than CNN methods. Then, based on a
recent lightweight MLP method, we obtain a strong baseline that outperforms
most state-of-the-art CNN-based methods. The baseline can learn global
structure representations with a filter to suppress structure irrelevant
information in the frequency space. Moreover, we propose a dynAmic
LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture
features while preserving global structure features, thus enabling the filter
to remove structure-irrelevant information sufficiently. Extensive experiments
on four benchmarks have demonstrated that our method can achieve great
performance improvement with a small number of parameters compared to SOTA
CNN-based DG methods. Our code is available at
https://github.com/lingeringlight/ALOFT/.Comment: Accepted by CVPR2023. The code is available at
https://github.com/lingeringlight/ALOFT
Coarse embeddings at infinity and generalized expanders at infinity
We introduce a notion of coarse embedding at infinity into Hilbert space for
metric spaces, which is a weakening of the notion of fibred coarse embedding
and a far generalization of Gromov's concept of coarse embedding. It turns out
that a residually finite group admits a coarse embedding into Hilbert space if
and only if one (or equivalently, every) box space of the group admits a coarse
embedding at infinity into Hilbert space. Moreover, we introduce a concept of
generalized expander at infinity and show that it is an obstruction to coarse
embeddability at infinity.Comment: 20 page
Demo: Reconfigurable Distributed Antennas and Reflecting Surface (RDARS)-aided Integrated Sensing and Communication System
Integrated sensing and communication (ISAC) system has been envisioned as a
promising technology to be applied in future applications requiring both
communication and high-accuracy sensing. Different from most research focusing
on theoretical analysis and optimization in the area of ISAC, we implement a
reconfigurable distributed antennas and reflecting surfaces (RDARS)-aided ISAC
system prototype to achieve the dual-functionalities with the communication
signal. A RDARS, composed of programmable elements capable of switching between
reflection mode and connected mode, is introduced to assist in uplink signal
transmission and sensing. The developed RDARS-aided ISAC prototype achieves
reliable user localization without compromising the communication rate,
showcasing its potential for future 6G systems.Comment: 2 pages, 3 figures. Accepted by IEEE/CIC International Conference on
Communications in China, Dalian, China, 202
Mechanism and Prevention of Agglomeration/Defluidization during Fluidized-Bed Reduction of Iron Ore
The mechanisms of agglomeration and defluidization and fluidization characteristic of iron oxide particles were investigated based on the theory of surface diffusion, interface reaction, surface nano/microeffect, and phase transformation. Moreover, a mathematical model was developed to predict the high-temperature defluidization behavior by the force-balance and plastic-viscous flow mechanism, and the fluidization phase diagram was obtained. On these bases, a control method of defluidization and its inhibition mechanism were proposed. As a result, the theoretical system of agglomeration/defluidization in the gas-solid fluidization was developed, and thus afforded theory support and technological bases for the solution of defluidization in industrial fluidized-bed reactors
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