788 research outputs found
Genetic algorithms and simulated annealing for robustness analysis
Genetic algorithms (GAs) and simulated annealing (SA) have been promoted as useful, general tools for nonlinear optimization. This paper explores their use in robustness analysis with real parameter variations, a known NP hard problem which would appear to be ideally suited to demonstrate the power of GAs and SA. Numerical experiment results show convincingly that they turn out to be poorer than existing branch and bound (B&B) approaches. While this may appear to shed doubt on some of the hype surrounding these stochastic optimization techniques, we find that they do have attractive features, which are also demonstrated in this study. For example, both GAs and SA are almost trivial to understand and program, so they require essentially no expertise, in sharp contrast to the B&B methods. They may be suitable for problems where programming effort is much more important than running time or the quality of the answer. Robustness analysis for engineering problems is not the best candidate in this respect, but it does provide an interesting test case for the evaluation of GAs and SA. A simple hill climbing algorithm is also studied for comparison
Stochastic Controlled Averaging for Federated Learning with Communication Compression
Communication compression, a technique aiming to reduce the information
volume to be transmitted over the air, has gained great interests in Federated
Learning (FL) for the potential of alleviating its communication overhead.
However, communication compression brings forth new challenges in FL due to the
interplay of compression-incurred information distortion and inherent
characteristics of FL such as partial participation and data heterogeneity.
Despite the recent development, the performance of compressed FL approaches has
not been fully exploited. The existing approaches either cannot accommodate
arbitrary data heterogeneity or partial participation, or require stringent
conditions on compression.
In this paper, we revisit the seminal stochastic controlled averaging method
by proposing an equivalent but more efficient/simplified formulation with
halved uplink communication costs. Building upon this implementation, we
propose two compressed FL algorithms, SCALLION and SCAFCOM, to support unbiased
and biased compression, respectively. Both the proposed methods outperform the
existing compressed FL methods in terms of communication and computation
complexities. Moreover, SCALLION and SCAFCOM accommodates arbitrary data
heterogeneity and do not make any additional assumptions on compression errors.
Experiments show that SCALLION and SCAFCOM can match the performance of
corresponding full-precision FL approaches with substantially reduced uplink
communication, and outperform recent compressed FL methods under the same
communication budget.Comment: 45 pages, 4 figure
Data Arrangement With Rotation Transformation for Fully Polarimetric Synthetic Aperture Radar
This letter proposes a data arrangement for fully polarimetric synthetic aperture radar (PolSAR). It is an essential novel method in the use of the rotation transformation in data interpretation. The key point of the proposal is employing a single pixel-based and selective rotation transformation for each pixel before the speckle filtering. The experimental results with ALOS2-PALSAR2 data show that the proposed data arrangement has much higher performance in recognizing double-bounce scattering in the man-made target area. At the same time, it is effective in avoiding the overestimation of double-bounce and/or surface scattering in natural target areas
Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler
Digital twin, which enables emulation, evaluation, and optimization of
physical entities through synchronized digital replicas, has gained
increasingly attention as a promising technology for intricate wireless
networks. For 6G, numerous innovative wireless technologies and network
architectures have posed new challenges in establishing wireless network
digital twins. To tackle these challenges, artificial intelligence (AI),
particularly the flourishing generative AI, emerges as a potential solution. In
this article, we discuss emerging prerequisites for wireless network digital
twins considering the complicated network architecture, tremendous network
scale, extensive coverage, and diversified application scenarios in the 6G era.
We further explore the applications of generative AI, such as transformer and
diffusion model, to empower the 6G digital twin from multiple perspectives
including implementation, physical-digital synchronization, and slicing
capability. Subsequently, we propose a hierarchical generative AI-enabled
wireless network digital twin at both the message-level and policy-level, and
provide a typical use case with numerical results to validate the effectiveness
and efficiency. Finally, open research issues for wireless network digital
twins in the 6G era are discussed
Genetic algorithms and simulated annealing for robustness analysis
Genetic algorithms (GAs) and simulated annealing (SA) have been promoted as useful, general tools for nonlinear optimization. This paper explores their use in robustness analysis with real parameter variations, a known NP hard problem which would appear to be ideally suited to demonstrate the power of GAs and SA. Numerical experiment results show convincingly that they turn out to be poorer than existing branch and bound (B&B) approaches. While this may appear to shed doubt on some of the hype surrounding these stochastic optimization techniques, we find that they do have attractive features, which are also demonstrated in this study. For example, both GAs and SA are almost trivial to understand and program, so they require essentially no expertise, in sharp contrast to the B&B methods. They may be suitable for problems where programming effort is much more important than running time or the quality of the answer. Robustness analysis for engineering problems is not the best candidate in this respect, but it does provide an interesting test case for the evaluation of GAs and SA. A simple hill climbing algorithm is also studied for comparison
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