The Crater Detection (CD) presents challenges due to complex backgrounds, tiny scales, and dense distribution of small craters. To address these problems, we propose a new DEtection TRansformer (DETR) variant network for crater detection called Crater-DETR. First, we design the Correspond Regional Attention Upsample (CRAU) and Pooling (CRAP) operators through cross-attention computing to address the problem of foreground-background confusion caused by the feature loss of small craters. Then, to address the weak supervision due to the fixed number query selection and to introduce dense supervision, we propose the Dense Auxiliary Head Supervise (DAHS) training. Next, Automatic DeNoising (ADN) training is proposed to solve the problem of sparse positive queries in the Decoder to improve the decoding capability. Finally, we propose a Small Object Stable Intersection over Union (SOSIoU) Loss to optimize the training process since the matching process is more unstable in small craters compared to other sizes of craters. Experimental results show that Crater-DETR achieves a precision of 88.13% on DACD dataset, yielding state-of-the-art performance. Furthermore, our method also achieved the best performance on ISDD and AI-TOD datasets, verifying its robustness.

Brain–computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance on extensive preprocessing. In this study, we introduce new hybrid architectures to enhance MI classification using data augmentation and a limited number of EEG channels. The first model combines a shallow convolutional neural network and a gated recurrent unit (CNN-GRU), while the second incorporates a convolutional neural network with a bidirectional gated recurrent unit (CNN-Bi-GRU). Evaluated using the publicly available PhysioNet dataset, the CNN-GRU classifier achieved peak mean accuracy rates of 99.71%, 99.73%, 99.61%, and 99.86% for tasks involving left fist (LF), right fist (RF), both fists (LRF), and both feet (BF), respectively. The experimental results provide compelling evidence that our proposed models outperform current state-of-the-art methods, underscoring their efficiency on small-scale EEG datasets. The CNN-GRU and CNN-Bi-GRU architectures exhibit superior predictive reliability, offering a faster, cost-effective solution for user-adaptable MI-BCI applications.

Humans gradually learn a sequence of cross-domain tasks and seldom experience catastrophic forgetting. In contrast, deep neural networks achieve good performance only in specific tasks within a single domain. To equip the network with lifelong learning capabilities, we propose a Cross-Domain Lifelong Learning (CDLL) framework that fully explores task similarities. Specifically, we employ a Dual Siamese Network (DSN) to learn the essential similarity features of tasks across different domains. To further understand similarity information across domains, we introduce a Domain-Invariant Feature Enhancement Module (DFEM) to better extract domain-invariant features. Moreover, we propose a Spatial Attention Network (SAN) that assigns different weights to various tasks based on the learned similarity features. Ultimately, to maximize the use of model parameters for learning new tasks, we propose a Structural Sparsity Loss (SSL) that can make the SAN as sparse as possible while ensuring accuracy. Experimental results show that our method effectively reduces catastrophic forgetting compared with state-of-the-art methods when continuously learning multiple tasks across different domains. It is worth noting that the proposed method scarcely forgets old knowledge while consistently enhancing the performance of learned tasks, more closely aligning with human learning.

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) have promising applications in neurorehabilitation for individuals who have lost mobility and control over parts of their body due to brain injuries, such as stroke patients. Accurately classifying MI tasks is essential for effective BCI performance, but this task remains challenging due to the complex and non-stationary nature of EEG signals. This study aims to improve the classification of left and right-hand MI tasks by utilizing high-resolution time-frequency features extracted from EEG signals, enhanced with deep learning-based data augmentation techniques. We propose a novel deep learning framework named the Generalized Wavelet Transform-based Deep Convolutional Network (GDC-Net), which integrates multiple components. First, EEG signals recorded from the C3, C4, and Cz channels are transformed into detailed time-frequency representations using the Generalized Morse Wavelet Transform (GMWT). The selected features are then expanded using a Deep Convolutional Generative Adversarial Network (DCGAN) to generate additional synthetic data and address data scarcity. Finally, the augmented feature maps data are subsequently fed into a hybrid CNN-LSTM architecture, enabling both spatial and temporal feature learning for improved classification. The proposed approach is evaluated on the BCI Competition IV dataset 2b. Experimental results showed that the mean classification accuracy and Kappa value are 89.24% and 0.784, respectively, making them the highest compared to the state-of-the-art algorithms. The integration of GMWT and DCGAN significantly enhances feature quality and model generalization, thereby improving classification performance. These findings demonstrate that GDC-Net delivers superior MI classification performance by effectively capturing high-resolution time-frequency dynamics and enhancing data diversity. This approach holds strong potential for advancing MI-based BCI applications, especially in assistive and rehabilitation technologies.

Craters are key features of a planet’s surface, often referred to as the ‘living fossils’ of the solar system’s history. However, due to significant variations in the distribution of craters across different planets, current crater detection methods tend to suffer from catastrophic forgetting when learning new craters, which significantly increases training costs. To address this challenge, we propose a new Continual Crater Detection (CCD) method that can learn new craters without suffering from catastrophic forgetting, while continually improving the model’s performance on previously learned craters. First, we introduce Adaptive Instance Similarity Distillation (ISD), which effectively leverages the similarity information between the craters the model has already learned and new craters. This ensures the stability and plasticity of the model during continual learning, and performs well even when category boundaries are ambiguous. Next, we present a Global Similarity Distillation (GSD) loss function, which dynamically adjusts the mutual information weights between old and new information at different scales in the backbone network, maximizing the extraction of similarity features. Finally, we propose an energy-based Gate R-CNN to further mitigate catastrophic forgetting. To validate the effectiveness of our method, we create a Continual Crater Detection Dataset (CCDD) containing 1,500 crater images across four different scenarios. Our experimental results show that, in continual learning settings, our method achieves both stability and plasticity. More importantly, it can prevent forgetting while improving performance on old tasks.

Online advertising recommendation is the economic engine of the digital ecosystem, yet traditional multi-stage cascading systems suffer from objective misalignment and error propagation. Conversely, emerging generative models often struggle with the strict latency and revenue alignment requirements of industrial applications. To address these issues, we propose GPR (Generative Pre-trained Recommender). Instead of the complex retrieval and pre-ranking cascade, GPR employs a unified generative framework to directly generate candidate sets, which are then refined by a lightweight fine-ranking stage for precise business alignment. Unlike prior works, GPR introduces three key technical innovations to ensure industrial feasibility: (1) Unified Representation: We utilize RQ-KMeans+ to map heterogeneous user behaviors and ad content into a shared semantic ID space; (2) Decoupled Architecture: We design a Heterogeneous Hierarchical Decoder (HHD) to decouple user intent modeling from ad generation, enabling efficient long-horizon inference; (3) Value-Aligned Generation: We propose a Value-Guided Trie-Constrained Beam Search and a hierarchical training strategy (MTP + HEPO) to strictly enforce business constraints and maximize commercial value. GPR has been fully deployed in Tencent Weixin Channels, serving over 500 million daily active users. Extensive online A/B testing demonstrates that GPR delivers substantial gains in GMV and CTCVR over highly optimized cascading baselines, validating the generative paradigm as a superior solution for large-scale advertising systems.