Single-modal change detection methods based on optical or Synthetic Aperture Radar (SAR) images face challenges such as degradation due to adverse weather or noise interference. In contrast, multimodal change detection struggles with significant domain gaps between different modalities. Inspired by the SAM2 model’s temporal memory mechanism for video segmentation, this paper introduces the concept of memory into change detection and proposes a novel approach called Memory-based Multimodal Change Detection (MMCD). By treating change detection as a temporal problem and modeling remote sensing images as video sequences, the proposed method integrates historical optical images with current SAR images to enhance detection accuracy. Additionally, a difference map enhancement module is introduced to mitigate false changes caused by modality discrepancies. Experimental results show that this approach achieves state-of-the-art performance in multimodal change detection, demonstrating the effectiveness of the proposed method.

The change detection aims to identify temporal changes in land cover. In emergency disaster scenarios, acquiring postchange optical images is often difficult due to factors such as adverse weather and illumination conditions. In contrast, the SAR-based change detection is robust to these environmental factors but is prone to speckle noise and often lacks clear semantic interpretation. These challenges highlight the importance of multimodal approaches that integrate the complementary information from different data sources. To address the domain gap between optical and SAR data, we propose change detection prompt (CDPrompt), an automatic prompt-learning framework that leverages in-domain change information as prompts to suppress fake changes caused by the domain gap between the two modalities. CDPrompt incorporates a modality-specific domain tuning module (DTM) to inject the domain knowledge into the segment anything model (SAM), enabling efficient adaptation to multimodal data with minimal labels and training costs. A low-level enhancement module (LwEM) further refines spatial details using historical optical images, while a consistency loss enhances the learning of domain-invariant features between prechange optical and SAR images. To support evaluation in disaster scenarios with missing modalities, we extend the DFC25 dataset and introduce the first disaster-oriented multimodal change detection dataset, DFC25-Extended, comprising DFC25-OS-S and DFC25-O-SO. Extensive experiments on the Onera Satellite Change Detection (OSCD) and DFC25-Extended datasets demonstrate the superior performance and practical value of CDPrompt. The code and dataset will be publicly available at:https://github.com/zhanglimeng13/CDPrompt

In the field of physical education, the specialized teaching of physical education in colleges and universities is gradually becoming the focus. This study aims to build an evaluation index system of specialized physical education teaching effect in colleges and universities, so as to accurately reflect the current actual situation of physical education teaching in colleges and universities. By clarifying the principles that should be followed in the construction of an evaluation index system and listening to experts' opinions, it is ensured that the constructed evaluation system can not only fully reflect the teaching effect, but also objectively evaluate the teaching quality. Finally, a comprehensive evaluation system is put forward and implemented, so that colleges and universities can more clearly understand the advantages and disadvantages of physical education, and then formulate targeted improvement measures. This will not only help to improve the quality of physical education in colleges and universities, but also improve students' physical literacy and physical and mental health.

The remote sensing community has shown increasingly interest in self-supervised learning for its ability to learn representations without labeled data. These representations can be easily adapted to downstream tasks through pre-training and fine-tuning. Recently, Masked Autoencoders (MAE) achieve better semantic representation by masking out a significant portion of the input image. However, the original design of MAE for RGB natural images may not be optimal for remote sensing (RS) images, which exhibit considerable variation between modalities like SAR and optical. To address this, we propose a 3D mask that enhances feature extraction along the vertical dimension. After fine-tuning, our 3DMAE model outperforms state-of-the-art contrastive and MAE-based models on BigEarthNet-MM classification and significantly reduces input data volume by at least 50% with the vertical mask, resulting in a more efficient model. Generalization experiments show a 5.9% F1-score improvement when applied to the SEN12MS dataset, which has diverse data distributions.

Abstract With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network ( Hybrid-CTEN ), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.

Spiking neural network (SNN), as the third generation of artificial neural networks, has been widely adopted in vision and audio tasks. Nowadays, many neuromorphic platforms support SNN simulation and adopt Network-on-Chips (NoC) architecture for multi-cores interconnection. However, a large volume and run-time communication on the interconnection has a significant effect on performance of the platform. In this paper, we propose a toolchain called SNEAP (Spiking NEural network mAPping toolchain) for mapping SNNs to neuromorphic platforms with multi-cores, which aims to reduce the energy and latency brought by spike communication on the interconnection.

Object detection has long been an important issue in the discipline of scene understanding. Existing researches mainly focus on the object itself, ignoring its surrounding environment. In fact, the surrounding environment provides abundant information to help detectors classify and locate objects. This paper proposes CRPDet, viz. CenterRepp Detector, a framework for object detection. The main function of CRPDet is accomplished by the CenterRepp module, which takes into account the surrounding environment by predicting the distribution of the central representative points. CenterRepp converts labeled object frames into the mean and standard variance of the sampling points' distribution. This helps increase the receptive field of objects, breaking the limitation of object frames. CenterRepp defines a position-fixed center point with significant weights, avoiding to sample all points in the surroundings. Experiments on the COCO test-dev detection benchmark demonstrates that our proposed CRPDet has comparable performance with state-of-the-art detectors, achieving 39.4 mAP with 51 FPS tested under single size input.

Determining appropriate statistical distributions for modeling text corpora is important for accurate estimation of numerical characteristics. Based on the validity of the test on a claim that the data conforms to Poisson distribution we propose Poisson decomposition model (PDM), a statistical model for modeling count data of text corpora, which can straightly capture each document's multidimensional numerical characteristics on topics. In PDM, each topic is represented as a parameter vector with multidimensional Poisson distribution, which can be easily normalized to multinomial term probabilities and each document is represented as measurements on topics and thereby reduced to a measurement vector on topics. We use gradient descent methods and sampling algorithm for parameter estimation. We carry out extensive experiments on the topics produced by our models. The results demonstrate our approach can extract more coherent topics and is competitive in document clustering by using the PDM-based features, compared to PLSI and LDA.