Aiming at the problems of redundant information accumulation, low computational efficiency, and fuzzy feature allocation in multiscale time-series prediction of traditional deep echo state network (DeepESN), this article proposed an interlayer sparse compression-based DeepESN model (ICS-DESN). The model uses the sparse sampling technology of deep fusion compressive sensing and the hierarchical dynamic feature extraction mechanism of DeepESN, introduces the adaptive compressed sampling module between the layers, and uses the Gaussian observation matrix to reduce the dimension of the high-dimensional state, which effectively inhibits the stacking of redundant information in the deep network, and explicitly allocates the multiscale temporal features. Through theoretical analysis, it is proven that ICS-DESN satisfies the stability condition of the echo state property (ESP) by constraining the weighted spectral radius of the reservoir. In the experiment, we used multiscenario time-series datasets, such as logistic chaotic systems, Lorenz attractors, sunspot data, NASDAQ stock index, ETTh1 dataset, and weather dataset to validate the effectiveness of the model. The results showed that compared with traditional comparison models, ICS-DESN significantly reduced prediction errors [mean squared error (MSE) and mean absolute error (MAE)], demonstrating higher computational efficiency and robustness. This research provides an efficient theoretical framework for complex time-series modeling and has potential application value in resource-constrained scenarios, such as edge computing.

In this paper, the problem of predefined time synchronization in fractional-order multi-modal memristor neural networks (FOMM-MNNs) is explored. To address the problem, a new predefined time stability theorem is proposed and its sufficient condition is derived through definite integral and inequality transformation. On the basis of this theory, an effective controller is designed, and specific sufficient conditions for predefined time synchronization in FOMM-MNNs networks are further proposed. In addition, the synchronous dynamics of the FOMM-MNNs drive-response system is investigated and analyzed in detail in the time domain. Finally, the correctness and validity of the theoretical conclusions are verified by numerical simulations, and the synchronization method is applied to the field of signal encryption and decryption, thus further verifying its practical application value.

The core of Video-Based Visible-Infrared Person Re-Identification (VVI-ReID) lies in learning modal-sharing features, namely ID-related feature representations, that are often mixed within modal-invariant features. Existing methods use weight sharing networks to learn frame-level modal-invariant features, but fail to consider modality invariance when computing sequence-level features, resulting in a significant gap between modalities. Moreover, these methods do not explicitly separate the ID-related features and modal-related features, which reduces the model’s discriminative ability and leads to interference in VVI-ReID. In this article, we propose a Discriminative ID-Related Representation Learning (DIRL) network for VVI-ReID. DIRL network consists of three key components, that is Two-Stream Backbone Module (TBM), Cross-Modality Interaction Module (CIM), and Feature Decoupling Module (FDM). More specifically, the TBM is first constructed to preliminarily capture frame-level modal-invariant features. Then, the CIM is designed to interact information between modals and aggregate temporal features simultaneously, thereby obtaining sequence-level modal-invariant features. Finally, the FDM is designed to explicitly separate modal-related features from ID-related ones within the modal-invariant features, thereby leaving only discriminative ID-related representations. Through extensive benchmark experiments, our method demonstrates superior performance over state-of-the-art approaches by significant margins. Our code will be available at https://github.com/JhSearch/DIRL .

SAR image classification is one of the research hotspots in the field of remote sensing. The performance of SAR image classification greatly depends on the learning of features and the design of classifiers. However, traditional SAR image classification methods either focus on learning (deep) features or focus on designing discriminative classifiers, while ignoring the correlation between them, resulting in the learned features and classifiers often not matching. To address the above issues, in this paper a novel classifier, termed entropy based low-rank dictionary pair learning (ELRDPL) method is first designed, which introduces the entropy theory and low-rank constraints into the objective function of dictionary learning for increasing the discriminative ability and decreasing the space occupation. Based on the constructed classifier, an entropy based deep lowrank dictionary pair learning network (EDLRDPL Net) is then proposed, which performs joint learning of deep features and discriminative dictionaries by embedding the ELRDPL classifier into the deep neural network. Extensive experimental results on four SAR image classification datasets demonstrate the effectiveness of EDLRDPL Net.

Structured pruning has received ever-increasing attention as a method for compressing convolutional neural networks. However, most existing methods directly prune the network structure according to the statistical information of the parameters. Besides, these methods differentiate the pruning rates only in each pruning stage or even use the same pruning rate across all layers, rather than using learnable parameters. In this article, we propose a network redundancy elimination approach guided by the pruned model. Our proposed method can easily tackle multiple architectures and is scalable to the deeper neural networks because of the use of joint optimization during the pruning procedure. More specifically, we first construct a sparse self-representation for the filters or neurons of the well-trained model, which is useful for analyzing the relationship among filters. Then, we employ particle swarm optimization to learn pruning rates in a layerwise manner according to the performance of the pruned model, which can determine optimal pruning rates with the best performance of the pruned model. Under this criterion, the proposed pruning approach can remove more parameters without undermining the performance of the model. Experimental results demonstrate the effectiveness of our proposed method on different datasets and different architectures. For example, it can reduce 58.1% FLOPs for ResNet50 on ImageNet with only a 1.6% top-five error increase and 44.1% FLOPs for FCN_ResNet50 on COCO2017 with a 3% error increase, outperforming most state-of-the-art methods.