When discussing the Aerial-Ground Person Re-Identification (AGPReID) task, we face the main challenge of the significant appearance variations caused by different viewpoints, making identity matching difficult. To address this issue, previous methods attempt to reduce the differences between viewpoints by critical attributes and decoupling the viewpoints. While these methods can mitigate viewpoint differences to some extent, they still face two main issues: (1) difficulty in handling viewpoint diversity and (2) neglect of the contribution of local features. To effectively address these challenges, we design and implement the Self-Calibrating and Adaptive Prompt (SeCap) method for the AGPReID task. The core of this framework relies on the Prompt Re-Calibration Module (PRM), which adaptively re-calibrates prompts based on the input. Combined with the Local Feature Refinement Module (LFRM), SeCap can extract view-invariant features from local features for AGPReID. Meanwhile, given the current scarcity of datasets in the AGPReID field, we further contribute two real-world Large-Scale Aerial-Ground Person Re-Identification datasets, LAGPeR and G2APS-ReID. The former is collected and annotated by us independently, covering 4, 231 unique identities and containing 63, 841 high-quality images; the latter is reconstructed from the person search dataset G2APS. Through extensive experiments on AGPReID datasets, we demonstrate that SeCap is a feasible and effective solution for the AGPReID task. The datasets and source code available on https://github.com/wangshining681/SeCap-AgpreID.

ABSTRACT Flight trajectory prediction is crucial for enabling the air traffic management system to anticipate future flight conflicts, allowing for the timely adoption of precautionary traffic control measures. To improve the prediction accuracy, some leading‐edge approaches leverage multi‐agent social neural networks or spatio‐temporal graph neural networks to mine the interactions among flights. However, these methods typically focus on learning the temporal states of individual flights driven by multi‐flight interactions, while underexploring the dynamic evolution of correlations among different or same flights at varying positions or times, resulting in suboptimal prediction performance. To address this gap, we propose a dynamic spatio‐temporal graph attention neural network (DST‐GAT) with an encoder‐decoder structure. DST‐GAT first draws on trajectory pattern mining to outline the multi‐flight interaction range, then respectively employs two types of spatio‐temporal graph edge encoders to capture the spatio‐temporal dynamics of multi‐flight interactions as well as the hyper‐temporal dynamics of individual flight flying trends. Extensive experiments on Beijing Terminal Manoeuvring Area operation data demonstrate that DST‐GAT outperforms state‐of‐the‐art methods in terms of prediction accuracy, highlighting its promising ability to exploit the intricate spatio‐temporal correlations among air traffic.

The Visible-Infrared Person Re-identification (VI ReID) aims to achieve cross-modality re-identification by matching pedestrian images from visible and infrared illumination. A crucial challenge in this task is mitigating the impact of modality divergence to enable the VI ReID model to learn cross-modality correspondence. Regarding this challenge, existing methods primarily focus on eliminating the information gap between different modalities by extracting modality-invariant information or supplementing inputs with specific information from another modality. However, these methods may overly focus on bridging the information gap, a challenging issue that could potentially overshadow the inherent complexities of cross-modality ReID itself. Based on this insight, we propose a straightforward yet effective strategy to empower the VI ReID model with sufficient flexibility to adapt diverse modality inputs to achieve cross-modality ReID effectively. Specifically, we introduce a Modality-aware and Instance-aware Visual Prompts (MIP) network, leveraging transformer architecture with customized visual prompts. In our MIP, a set of modality-aware prompts is designed to enable our model to dynamically adapt diverse modality inputs and effectively extract information for identification, thereby alleviating the interference of modality divergence. Besides, we also propose the instance-aware prompts, which are responsible for guiding the model to adapt individual pedestrians and capture discriminative clues for accurate identification. Through extensive experiments on four mainstream VI ReID datasets, the effectiveness of our designed modules is evaluated. Furthermore, our proposed MIP network outperforms most current state-of-the-art methods.

Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. However, the existing experimental techniques for detecting Kcr sites are cost-ineffective, to a great need for new computational methods to address this problem. We here describe Adapt-Kcr, an advanced deep learning model that utilizes adaptive embedding and is based on a convolutional neural network together with a bidirectional long short-term memory network and attention architecture. On the independent testing set, Adapt-Kcr outperformed the current state-of-the-art Kcr prediction model, with an improvement of 3.2% in accuracy and 1.9% in the area under the receiver operating characteristic curve. Compared to other Kcr models, Adapt-Kcr additionally had a more robust ability to distinguish between crotonylation and other lysine modifications. Another model (Adapt-ST) was trained to predict phosphorylation sites in SARS-CoV-2, and outperformed the equivalent state-of-the-art phosphorylation site prediction model. These results indicate that self-adaptive embedding features perform better than handcrafted features in capturing discriminative information; when used in attention architecture, this could be an effective way of identifying protein Kcr sites. Together, our Adapt framework (including learning embedding features and attention architecture) has a strong potential for prediction of other protein posttranslational modification sites.