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The evaluation of thermal comfort for railway passengers holds considerable importance, not only in reducing energy consumption but also in enhancing the passengers' experience. This paper presents a Transformer-based multi-task learning network (TransMTL) designed for railway passenger thermal comfort evaluation using EEG. We utilized manual features to extract temporal and frequency information, while a Transformer encoder distilled spatial information. The multi-task learning structure enhances model robustness by leveraging thermal comfort task correlations. We conducted experiments during winter and summer with high-speed railway passengers, establishing a comprehensive EEG dataset. The results demonstrated that our proposed EEG-TransMTL model outperformed classical machine learning and deep learning models in all four thermal comfort evaluation tasks, achieving accuracy rates of 65.00%, 66.70%, 80.38%, and 71.01%, respectively. We enhanced model interpretability by visualizing attention weights from the Transformer encoder, identifying key EEG channels. A simplified model utilizing only eight crucial channels also delivered notable performance. This research provides a practical and neuro-mechanism interpretable solution for thermal comfort evaluation. • The first EEG-based thermal comfort experiment in a train cabin was carried out. • A Transformer-based Multi-task Learning Network is proposed. • Our proposed method outperforms six baseline models in four tasks. • The crucial EEG channels are visualized and enhance the model's interpretability. [ABSTRACT FROM AUTHOR]

Multivariate time series classification is a crucial task with applications in broad areas such as finance, medicine, and engineering. Transformer is promising for time series classification, but as a generic approach, they have limited capability to effectively capture the distinctive characteristics inherent in time series data and adapt to diverse architectural requirements. This paper proposes a novel dynamic transformer-based architecture called Dyformer to address the above limitations of traditional transformers in multivariate time series classification. Dyformer incorporates hierarchical pooling to decompose time series into subsequences with different frequency components. Then, it employs Dyformer modules to achieve adaptive learning strategies for different frequency components based on a dynamic architecture. Furthermore, we introduce feature-map-wise attention mechanisms to capture multi-scale temporal dependencies and a joint loss function to facilitate model training. To evaluate the performance of Dyformer, we conducted extensive experiments using 30 benchmark datasets. The results unequivocally demonstrate that our model consistently outperforms a multitude of state-of-the-art methods and baseline approaches. Our model also copes well with limited training samples when pre-trained. • We present a transformer-based dynamic architecture to achieve adaptive learning strategies for different frequency components of the time series data. • We design a hierarchical pooling layer to decompose time series into subsequences representing different frequency components to facilitate time series classification. • We implement two types of gates for allowing the architecture to dynamically adapt the layer activation to realize a flexible structure. [ABSTRACT FROM AUTHOR]

With the popularity of online shopping and the rise of various brands, logo detection is gradually coming to the forefront of researchers' minds. However, accurately detecting multiscale, similar, diverse and shape-shifting logos poses a challenge for this technology. The Swin transformer has created a milestone as a high-performance deep learning method across modalities, domains and tasks in various tasks of computer vision. We improve the Swin transformer for better image feature extraction and enhance the robustness for its application to logo detection. In this paper, we propose the PMST as the backbone of logo detection, and design a bypass-parallelizable shift module and a miniature window tandem shift strategy to further enhance image feature fusion and transfer between windows. The PMST achieves a 79.2 box AP on the LogoData dataset, surpassing most detection models. According to existing known methods, in logo detection, the PMST is the first backbone to put transformer to the detection. The results achieved state-of-the-art on the FlickrLogos-32 and FoodLogoDet-1500 datasets. It also achieves excellent efficiency on the ImageNet-1K, OpenBrands-80 and MS-COCO datasets. The code is available at https://github.com/blowhen/PMST. [ABSTRACT FROM AUTHOR]