Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking

RGB-T tracking, a vital downstream task of object tracking, has made remarkable progress in recent years. Yet, it remains hindered by two major challenges: 1) the trade-off between performance and efficiency; 2) the scarcity of training data. To address the latter challenge, some recent methods employ prompts to fine-tune pre-trained RGB tracking models and leverage upstream knowledge in a parameter-efficient manner. However, these methods inadequately explore modality-independent patterns and disregard the dynamic reliability of different modalities in open scenarios. We propose M3PT, a novel RGB-T prompt tracking method that leverages middle fusion and multi-modal and multi-stage visual prompts to overcome these challenges. We pioneer the use of the middle fusion framework for RGB-T tracking, which achieves a balance between performance and efficiency. Furthermore, we incorporate the pre-trained RGB tracking model into the framework and utilize multiple flexible prompt strategies to adapt the pre-trained model to the comprehensive exploration of uni-modal patterns and the improved modeling of fusion-modal features, harnessing the potential of prompt learning in RGB-T tracking. Our method outperforms the state-of-the-art methods on four challenging benchmarks, while attaining 46.1 fps inference speed.

Localization of Dummy Data Injection Attacks in Power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. Finally, the research proposes a DDIA localization method based on spatio-temporal graph neural networks. The accuracy and effectiveness of the DDIA model are rigorously demonstrated through comprehensive analytical cases.

Data Discovery using Natural Language Questions via a Self-Supervised Approach

Data discovery systems help users identify relevant data among large table collections. Users express their discovery needs with a program or a set of keywords. Users may express complex queries using programs but it requires expertise. Keyword search is accessible to a larger audience but limits the types of queries supported. An interesting approach is learned discovery systems which find tables given natural language questions. Unfortunately, these systems require a training dataset for each table collection. And because collecting training data is expensive, this limits their adoption. In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, S2LD, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on wellknown benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems. The code is open-sourced at https://github.com/TheDataStation/open_table_discovery.