Test-Time Compensated Representation Learning for Extreme Traffic Forecasting

Traffic forecasting is a challenging task due to the complex spatio-temporal correlations among traffic series. In this paper, we identify an underexplored problem in multivariate traffic series prediction: extreme events. Road congestion and rush hours can result in low correlation in vehicle speeds at various intersections during adjacent time periods. Existing methods generally predict future series based on recent observations and entirely discard training data during the testing phase, rendering them unreliable for forecasting highly nonlinear multivariate time series. To tackle this issue, we propose a test-time compensated representation learning framework comprising a spatio-temporal decomposed data bank and a multi-head spatial transformer model (CompFormer). The former component explicitly separates all training data along the temporal dimension according to periodicity characteristics, while the latter component establishes a connection between recent observations and historical series in the data bank through a spatial attention matrix. This enables the CompFormer to transfer robust features to overcome anomalous events while using fewer computational resources. Our modules can be flexibly integrated with existing forecasting methods through end-to-end training, and we demonstrate their effectiveness on the METR-LA and PEMS-BAY benchmarks. Extensive experimental results show that our method is particularly important in extreme events, and can achieve significant improvements over six strong baselines, with an overall improvement of up to 28.2%.Comment: 13 pages, 10 figures, 5 table

CiteTracker: Correlating Image and Text for Visual Tracking

Existing visual tracking methods typically take an image patch as the reference of the target to perform tracking. However, a single image patch cannot provide a complete and precise concept of the target object as images are limited in their ability to abstract and can be ambiguous, which makes it difficult to track targets with drastic variations. In this paper, we propose the CiteTracker to enhance target modeling and inference in visual tracking by connecting images and text. Specifically, we develop a text generation module to convert the target image patch into a descriptive text containing its class and attribute information, providing a comprehensive reference point for the target. In addition, a dynamic description module is designed to adapt to target variations for more effective target representation. We then associate the target description and the search image using an attention-based correlation module to generate the correlated features for target state reference. Extensive experiments on five diverse datasets are conducted to evaluate the proposed algorithm and the favorable performance against the state-of-the-art methods demonstrates the effectiveness of the proposed tracking method.Comment: accepted by ICCV 202

Strip-MLP: Efficient Token Interaction for Vision MLP

Token interaction operation is one of the core modules in MLP-based models to exchange and aggregate information between different spatial locations. However, the power of token interaction on the spatial dimension is highly dependent on the spatial resolution of the feature maps, which limits the model's expressive ability, especially in deep layers where the feature are down-sampled to a small spatial size. To address this issue, we present a novel method called \textbf{Strip-MLP} to enrich the token interaction power in three ways. Firstly, we introduce a new MLP paradigm called Strip MLP layer that allows the token to interact with other tokens in a cross-strip manner, enabling the tokens in a row (or column) to contribute to the information aggregations in adjacent but different strips of rows (or columns). Secondly, a \textbf{C}ascade \textbf{G}roup \textbf{S}trip \textbf{M}ixing \textbf{M}odule (CGSMM) is proposed to overcome the performance degradation caused by small spatial feature size. The module allows tokens to interact more effectively in the manners of within-patch and cross-patch, which is independent to the feature spatial size. Finally, based on the Strip MLP layer, we propose a novel \textbf{L}ocal \textbf{S}trip \textbf{M}ixing \textbf{M}odule (LSMM) to boost the token interaction power in the local region. Extensive experiments demonstrate that Strip-MLP significantly improves the performance of MLP-based models on small datasets and obtains comparable or even better results on ImageNet. In particular, Strip-MLP models achieve higher average Top-1 accuracy than existing MLP-based models by +2.44\% on Caltech-101 and +2.16\% on CIFAR-100. The source codes will be available at~\href{https://github.com/Med-Process/Strip_MLP{https://github.com/Med-Process/Strip\_MLP}

Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric

Combining the Color and Event cameras (also called Dynamic Vision Sensors, DVS) for robust object tracking is a newly emerging research topic in recent years. Existing color-event tracking framework usually contains multiple scattered modules which may lead to low efficiency and high computational complexity, including feature extraction, fusion, matching, interactive learning, etc. In this paper, we propose a single-stage backbone network for Color-Event Unified Tracking (CEUTrack), which achieves the above functions simultaneously. Given the event points and RGB frames, we first transform the points into voxels and crop the template and search regions for both modalities, respectively. Then, these regions are projected into tokens and parallelly fed into the unified Transformer backbone network. The output features will be fed into a tracking head for target object localization. Our proposed CEUTrack is simple, effective, and efficient, which achieves over 75 FPS and new SOTA performance. To better validate the effectiveness of our model and address the data deficiency of this task, we also propose a generic and large-scale benchmark dataset for color-event tracking, termed COESOT, which contains 90 categories and 1354 video sequences. Additionally, a new evaluation metric named BOC is proposed in our evaluation toolkit to evaluate the prominence with respect to the baseline methods. We hope the newly proposed method, dataset, and evaluation metric provide a better platform for color-event-based tracking. The dataset, toolkit, and source code will be released on: \url{https://github.com/Event-AHU/COESOT}

Picroside I inhibits asthma phenotypes by regulating Tbet/ GATA-3 ratio and Th1/Th2 balance in a murine model of asthma

Purpose: To examine the anti-asthmatic activity of picroside I in murine asthma model, and to elucidate the mechanism(s) involved.Methods: The study involved systematic sensitization of acclimatized BALB/c mice with ovalbumin (OVA), and subsequent exposure to aerosol allergens. The effect of picroside I on associated IgE formation was determined. All assays were performed using standard protocols. Protein expression was assessed using western blotting.Results: Picroside I inhibited allergic airway inflammation, AHR, and the production of OVA-associated IgE and Th2 cytokines. Moreover, it altered the T-bet/GATA3 ratio by suppressing the phosphorylation of STAT6 in a dose-dependent manner.Conclusion: These results indicate that the anti-asthmatic effect of picroside I occurs via a mechanism involving inhibition of Th2 cytokines by suppression of the expressions of pSTAT6 and GATA-3, and upregulation of the expression of T-bet. Thus, picroside I is a promising agent for the management of asthma.Keywords: Picroside, Asthma, Allergic response, IgE, GATA-3, pSTAT

An experimental live chimeric porcine circovirus 1-2a vaccine decreases porcine circovirus 2b viremia when administered intramuscularly or orally in a porcine circovirus 2b and porcine reproductive and respiratory syndrome virus dual-challenge model

Commercially available inactivated vaccines against porcine circovirus type 2 (PCV2) have been shown to be effective in reducing PCV2 viremia. Live-attenuated, orally administered vaccines are widely used in the swine industry for several pathogens because of their ease of use yet they are not currently available for PCV2 and efficacy. The aims of this study were to determine the efficacy of a live-attenuated chimeric PCV2vaccine inadual-challengemodelusingPCV2bandporcine reproductiveandrespiratorysyndrome virus (PRRSV) and to compare intramuscular (IM) and oral (PO) routes of vaccination. Eighty-three 2-week-old pigs were randomized into 12 treatment groups: four vaccinated IM, four vaccinated PO and four non-vaccinated (control) groups. Vaccination was performed at 3 weeks of age using a PCV1-2a live-attenuated vaccine followed by no challenge, or challenge with PCV2b, PRRSV or a combination of PCV2b and PRRSV at 7 weeks of age. IM administration of the vaccine elicited an anti-PCV2 antibody response between 14 and 28 days post vaccination, 21/28 of the pigs being seropositive prior to challenge. In contrast, the anti-PCV2 antibody response in PO vaccinated pigs was delayed, only 1/27 of the pigs being seropositive at challenge. At 21 days post challenge, PCV2 DNA loads were reduced by 80.4%in the IMvaccinated groups and by 29.6% in the POvaccinated groups. PCV1-2a (vaccine) viremia was not identified in any of the pigs. Under the conditions of this study, the live attenuated PCV1-2a vaccine was safe and provided immune protection resulting in reduction of viremia. The IM route provided the most effective protection