Structural Regular Multiple Criteria Linear Programming for Classification Problem

Classification problem has attracted an increasing amount of interest. Various classifiers have been proposed in the last decade, such as ANNs, LDA, and SVM. Regular Multiple Criteria Linear Programming (RMCLP) is an effective classification method, which was proposed by Shi and his colleagues and have been applied to handle different real-life data mining problems. In this paper, inspired by the application potential of RMCLP, we propose a novel Structural RMCLP (called SRMCLP) method for classification problem. Unlike RMCLP, SRMCLP is sensitive to the structure of the data distribution and can construct more reasonable classifiers by exploiting these prior data distribution information within classes. The corresponding optimization problem of SRMCLP can be solved by a standard quadratic programming. The effectiveness of the proposed method is demonstrated via experiments on synthetic and available benchmark datasets

s-LWSR: Super Lightweight Super-Resolution Network

Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods

Quantum diffusion of microcavity solitons

Coherently pumped (Kerr) solitons in an ideal optical microcavity are expected to undergo random quantum motion that determines fundamental performance limits in applications of the soliton microcombs. Here this random walk and its impact on Kerr soliton timing jitter are studied experimentally. The quantum limit is discerned by measuring the relative position of counter-propagating solitons. Their relative motion features weak interactions and also presents common-mode suppression of technical noise, which typically hides the quantum fluctuations. This is in contrast to co-propagating solitons, which are found to have relative timing jitter well below the quantum limit of a single soliton on account of strong correlation of their mutual motion. Good agreement is found between theory and experiment. The results establish the fundamental limits to timing jitter in soliton microcombs and provide new insights on multisoliton physics

Impact of spatio-temporal thermal decoherence on soliton microcombs in multimode microresonators

The phase noise of the soliton repetition rate is experimentally characterized in silica microresonators. In conjunction with dispersive wave quieting of pump technical noise, spatio-temporal fluctuations of distinct transverse modes set a limit to performance

Impact of spatio-temporal thermal decoherence on soliton microcombs in multimode microresonators

The phase noise of the soliton repetition rate is experimentally characterized in silica microresonators. In conjunction with dispersive wave quieting of pump technical noise, spatio-temporal fluctuations of distinct transverse modes set a limit to performance