Pushing the information states'acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces.Recent studies have indicated that maximal information states can b...Pushing the information states'acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces.Recent studies have indicated that maximal information states can be attained through engineered modes;however,partial intrusion is generally required.While non-invasive designs have been substantially explored across diverse physical scenarios,the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem,particularly in the context of multi-target scenarios.Here,we establish the feasibility of non-invasive information states'acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces.To illustrate the framework's efficacy,we demonstrate that efficient information states'acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system.Our work provides insightful perspectives for precise measurements inside dynamic complex systems.展开更多
In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simul...In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simultaneously cluster the high dimensional data into several subspaces and associate the low-dimensional subspaces with the corresponding points. In subspace clustering, it is a crucial step to construct an affinity matrix with block-diagonal form, in which the blocks correspond to different clusters. The distance-based methods and the representation-based methods are two major types of approaches for building an informative affinity matrix. In general, it is the difference between the density inside and outside the blocks that determines the efficiency and accuracy of the clustering. In this work, we introduce a well-known approach in statistic physics method, namely link prediction, to enhance subspace clustering by reinforcing the affinity matrix. More importantly, we introduce the idea to combine complex network theory with machine learning. By revealing the hidden links inside each block, we maximize the density of each block along the diagonal, while restrain the remaining non-blocks in the affinity matrix as sparse as possible. Our method has been shown to have a remarkably improved clustering accuracy comparing with the existing methods on well-known datasets.展开更多
基金Zhejiang University was sponsored by the National Natural Science Foundation of China(NNSFC)under grant nos.62071424,62201499,and 62027805。
文摘Pushing the information states'acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces.Recent studies have indicated that maximal information states can be attained through engineered modes;however,partial intrusion is generally required.While non-invasive designs have been substantially explored across diverse physical scenarios,the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem,particularly in the context of multi-target scenarios.Here,we establish the feasibility of non-invasive information states'acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces.To illustrate the framework's efficacy,we demonstrate that efficient information states'acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system.Our work provides insightful perspectives for precise measurements inside dynamic complex systems.
基金the National Natural Science Foundation of China (Grant Nos. 61433014 and 71601029).
文摘In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simultaneously cluster the high dimensional data into several subspaces and associate the low-dimensional subspaces with the corresponding points. In subspace clustering, it is a crucial step to construct an affinity matrix with block-diagonal form, in which the blocks correspond to different clusters. The distance-based methods and the representation-based methods are two major types of approaches for building an informative affinity matrix. In general, it is the difference between the density inside and outside the blocks that determines the efficiency and accuracy of the clustering. In this work, we introduce a well-known approach in statistic physics method, namely link prediction, to enhance subspace clustering by reinforcing the affinity matrix. More importantly, we introduce the idea to combine complex network theory with machine learning. By revealing the hidden links inside each block, we maximize the density of each block along the diagonal, while restrain the remaining non-blocks in the affinity matrix as sparse as possible. Our method has been shown to have a remarkably improved clustering accuracy comparing with the existing methods on well-known datasets.
