Weakly supervised semantic segmentation (WSSS) based on image-level labels can reduce the expensive costs of annotating pixel-level labels, and it has achieved great progress by generating class activation maps (CAMs) as pseudo labels for training a segmentation model. However, it is challenging to generate high-quality CAMs due to the large supervision gap between classification and segmentation. To aid in mitigating the supervision gap, we firstly design a simple but effective Feature-To-Image Restoration (FTIR) branch to acquire supplementary pixel-wise supervisory information. Secondly, to effectively utilize FTIR supervision in addressing the supervision gap, we have developed an auxiliary learning-based network (ALNet) that incorporates the FTIR as an auxiliary task to support the primary WSSS task. By integrating them into a unified framework, the proposed network can leverage the FTIR supervision to enhance the performance of WSSS. Thirdly, we propose a semantic-aware multi-level feature progressive aggregation module (SMPA) to enhance the fusion of multi-level features, accommodating the varying feature requirements of the two distinct tasks at different levels of granularity. Finally, we also introduce a global reasoning augmentation module (GloRe) into our model to further strengthen global correlations between similar building objects. Extensive experiments on public HRSI building datasets, including the WHU dataset and the InriaAID dataset, demonstrate that with the assistance of the FTIR, the proposed ALNet with the SMPA and the GloRe can produce more integral and accurate CAMs for weakly supervised building extraction in HRSIs, and achieve state-of-the-art performance that significantly surpasses other WSSS methods.

In order to solve the problem that the traffic flow prediction model only focuses on the time dimension, which results in the model’s low accuracy. In this paper, the three most influential weather features are extracted from the weather data through the principal component analysis (PCA). Weather features are extracted by PCA algorithm. The combined input of weather and time characteristics is compared with the input of time characteristics to obtain the simulation results. Experimental simulation results show that the RMSE and MAPE of the model decreased to 11.9844% and 7.1398 respectively when the extracted weather characteristics were used as the input of LSTM. The construction of the model can optimize the travel experience and facilitate the traffic department to grasp the traffic flow.

The problem of finding key players in a graph, also known as network dismantling, or network disintegration, aims to find an optimal removal sequence of nodes (edges, substructures) through a certain algorithm, ultimately causing functional indicators such as the largest connected component (GCC) or network pair connectivity in the graph to rapidly decline. As a typical NP-hard problem on graphs, recent methods based on reinforcement learning and graph representation learning have effectively solved such problems. However, existing reinforcement-learning-based key-player-identification algorithms often need to remove too many nodes in order to achieve the optimal effect when removing the remaining network until no connected edges remain. The use of a minimum number of nodes while maintaining or surpassing the performance of existing methods is a worthwhile research problem. To this end, a novel algorithm called MiniKey was proposed to tackle such challenges, which employs a specific deep Q-network architecture for reinforcement learning, a novel reward-shaping mechanism based on network functional indicators, and the graph-embedding technique GraphSage to transform network nodes into latent representations. Additionally, a technique dubbed ‘virtual node technology’ is integrated to grasp the overarching feature representation of the whole network. This innovative algorithm can be effectively trained on small-scale simulated graphs while also being scalable to large-scale real-world networks. Importantly, experiments from both six simulated datasets and six real-world datasets demonstrates that MiniKey can achieve optimal performance, striking a perfect balance between the effectiveness of key node identification and the minimization of the number of nodes that is utilized, which holds potential for real-world applications such as curbing misinformation spread in social networks, optimizing traffic in transportation systems, and identifying key targets in biological networks for targeted interventions.

Network dismantling is one of the most challenging problems in complex systems. This problem encompasses a broad array of practical applications. Previous works mainly focus on the metrics such as the number of nodes in the Giant Connected Component (GCC), average pairwise connectivity, etc. This paper introduces a novel metric, the accumulated 2-core size, for assessing network dismantling. Due to the NP-hard computational complexity of this problem, we propose SmartCore, an end-to-end model for minimizing the accumulated 2-core size by leveraging reinforcement learning and graph neural networks. Extensive experiments across synthetic and real-world datasets demonstrate SmartCore’s superiority over existing methods in terms of both accuracy and speed, suggesting that SmartCore should be a better choice for the network dismantling problem in practice.

The log-ratio (LR) operator is well suited for change detection in synthetic aperture radar (SAR) amplitude or intensity images. In applying the LR operator to change detection in multi-temporal SAR images, a crucial problem is how to develop precise models for the LR statistics. In this study, we first derive analytically the probability density function (PDF) of the LR operator. Subsequently, the PDF of the LR statistics is parameterized by three parameters, i.e., the number of looks, the coherence magnitude, and the true intensity ratio. Then, the maximum-likelihood (ML) estimates of parameters in the LR PDF are also derived. As an example, the proposed statistical model and corresponding ML estimation are used in an operational application, i.e., determining the constant false alarm rate (CFAR) detection thresholds for small target detection between SAR images. The effectiveness of the proposed model and corresponding ML estimation are verified by applying them to measured multi-temporal SAR images, and comparing the results to the well-known generalized Gaussian (GG) distribution; the usefulness of the proposed LR PDF for small target detection is also shown.

In the fields of machine learning and artificial intelligence, the processing of time-series data has been a continuous concern and a significant algorithm for intelligent applications. Traditional deep-learning-based methods seem to have reached performance ceilings in certain specific areas, such as online character recognition. This paper proposes an algorithmic framework to break this deadlock by classifying time-series data by evaluating the similarities among handwriting samples using multidimensional Dynamic Time Warping (mDTW) distances. A simplified hierarchical clustering algorithm is employed as a classifier for character recognition. Moreover, this work achieves joint modeling with current mainstream temporal models, enabling the mDTW model to integrate modeling results from methods like RNN or Transformer, therefore further enhancing the accuracy of related algorithms. A series of experiments were conducted on a public database, and the results indicate that our method overcomes the bottleneck of current deep-learning-based methods in the field of online handwriting character recognition. More importantly, compared to deep -learning-based methods, the proposed method has a simpler structure and higher interpretability. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art models in handwriting character recognition, achieving a top-1 accuracy of 98.5% and a top-3 accuracy of 99.3%, thus confirming its effectiveness in overcoming the limitations of traditional deep-learning models in temporal sequence processing.