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Autonomous driving is gradually moving from single-vehicle intelligence to internet of vehicles, where traffic participants can share the traffic flow information perceived by each other. When the sensing technology is combined with the internet of vehicles, a sensor network all over the road can provide a large-scale of traffic flow data, thus providing a basis for building a traffic digital twin model. The digital twin can enable the traffic system not only to use past and present information, but also to predict traffic conditions, providing more effective optimization for autonomous driving and intelligent transportation, so as to make long-term rational planning of the overall traffic state and enhance the level of traffic intelligence. The current mainstream traffic sensors, namely radar and camera, have their own advantages, and the fusion of these two sensors can provide more accurate traffic flow data for the generation of digital twin model. In this paper, an end-to-end digital twin system implementation approach is proposed for highway scenarios. Starting from a paired radar-camera sensing system, a single-site radar-camera fusion framework is proposed, and then using the definition of a unified coordinate system, the traffic flow data between multiple sites is combined to form a dynamic real-time traffic flow digital twin model. The effectiveness of the digital twin building is verified based on the real-world traffic data.

Highly pathogenic avian influenza (HPAI) subtype H5N1 clade 2.3.4.4b virus has spread globally, causing unprecedented large-scale avian influenza outbreaks since 2020. In 2021, we isolated 17 highly pathogenic avian influenza H5N1 viruses from wild birds in China. To determine virus origin, we genetically analyzed 1,529 clade 2.3.4.4b H5N1 viruses reported globally since October 2020 and found that they formed 35 genotypes. The 17 viruses belonged to genotypes G07, which originated from eastern Asia, and G10, which originated from Russia. The viruses were moderately pathogenic in mice but were highly lethal in ducks. The viruses were in the same antigenic cluster as the current vaccine strain (H5-Re14) used in China. In chickens, the H5/H7 trivalent vaccine provided complete protection against clade 2.3.4.4b H5N1 virus challenge. Our data indicate that vaccination is an effective strategy for preventing and controlling the globally prevalent clade 2.3.4.4b H5N1 virus.

This study focuses on Scene Text Recognition (STR), which plays a crucial role in various applications of artificial intelligence such as image retrieval, office automation, and intelligent transportation systems. Currently, pre-trained vision-language models have become the foundation for various downstream tasks. CLIP exhibits robustness in recognizing both regular (horizontal) and irregular (rotated, curved, blurred, or occluded) text in natural images. As research in scene text recognition requires substantial linguistic knowledge, we introduce the pre-trained vision-language model CLIP and the pre-trained language model Llama. Our approach builds upon CLIP’s image and text encoders, featuring two encoder–decoder branches: one visual branch and one cross-modal branch. The visual branch provides initial predictions based on image features, while the cross-modal branch refines these predictions by addressing the differences between image features and textual semantics. We incorporate the large language model Llama2-7B in the cross-modal branch to assist in correcting erroneous predictions generated by the decoder. To fully leverage the potential of both branches, we employ a dual prediction and refinement decoding scheme during inference, resulting in improved accuracy. Experimental results demonstrate that CLIP-Llama achieves state-of-the-art performance on 11 STR benchmark tests, showcasing its robust capabilities. We firmly believe that CLIP-Llama lays a solid and straightforward foundation for future research in scene text recognition based on vision-language models.

Few-shot Relation Classification identifies the relation between target entity pairs in unstructured natural language texts by training on a small number of labeled samples. Recent prototype network-based studies have focused on enhancing the prototype representation capability of models by incorporating external knowledge. However, the majority of these works constrain the representation of class prototypes implicitly through complex network structures, such as multi-attention mechanisms, graph neural networks, and contrastive learning, which constrict the model’s ability to generalize. In addition, most models with triplet loss disregard intra-class compactness during model training, thereby limiting the model’s ability to handle outlier samples with low semantic similarity. Therefore, this paper proposes a non-weighted prototype enhancement module that uses the feature-level similarity between prototypes and relation information as a gate to filter and complete features. Meanwhile, we design a class cluster loss that samples difficult positive and negative samples and explicitly constrains both intra-class compactness and inter-class separability to learn a metric space with high discriminability. Extensive experiments were done on the publicly available dataset FewRel 1.0 and 2.0, and the results show the effectiveness of the proposed model.

During the transportation of oil and gas pipelines, there are many potential factors that can lead to pipeline leakage with serious consequences, making automatic and real-time pipeline leakage detection urgent. In response to the inconvenience of manual detection, constant false alarm rate (CFAR) detection technique in radar target detection theory is introduced for pipeline leakage detection based on acoustic signals. In this paper, an automatic pipeline leakage detection algorithm based on an improved CFAR detector is proposed. The improved CFAR detection is executed after pre-processing the acoustic signals so as to adaptively set the detection threshold to achieve the purpose of automatic detection of pipeline leakage incidents. A simulated leakage test of a real pipeline is used for validation, and the proposed method achieves detection accuracies of 84.6%, 97.7%, and 98% for different leakage diameter settings, i.e., 5 mm, 7 mm, and 10 mm leak hole diameters, respectively, with an overall accuracy of 94.1%, while the false alarm rates are 3.3%, 0.7%, and 0, respectively, as well as an overall of 1.2%. The results of experimental data based on real scenarios demonstrate the effectiveness of the proposed method.

Objective This meta-analysis aimed to evaluate the dose-response relationship between body mass index (BMI) and the risk of chemotherapy-induced peripheral neuropathy (CIPN) in cancer patients. Methods We conducted a dose-response meta-analysis of 10 studies involving 6,841 cancer patients. Studies reporting BMI and CIPN outcomes were selected. The relationship between BMI and CIPN was assessed using random-effects models and restricted cubic splines to model the dose-response association. Results Pooled analysis revealed a significant association between higher BMI and increased risk of CIPN, with an odds ratio (OR) of 1.55 (95% CI, 1.20–1.99). A dose-response analysis demonstrated a clear linear relationship between BMI and the risk of CIPN. For every 5 kg/m 2 increase in BMI, the relative risk of CIPN increased by approximately 15%. Subgroup analyses showed stronger associations in breast cancer patients and those treated with taxane or platinum-based regimens. Sensitivity analyses confirmed the robustness of the results, and mild publication bias was observed. Conclusions Higher BMI is significantly associated with an increased risk of CIPN, with a dose-dependent effect. Weight management interventions, such as dietary modifications and physical activity, may reduce CIPN risk, particularly in patients with elevated BMI undergoing chemotherapy with neurotoxic regimens.

VQA (Visual Question Answering) is a multi-model task. Given a picture and a question related to the image, it will determine the correct answer. The attention mechanism has become a de facto component of almost all VQA models. Most recent VQA approaches use dot-product to calculate the intra-modality and inter-modality attention between visual and language features. In this paper, the BAN (Bilinear Attention Network) method was used to calculate attention. We propose a deep multimodality bilinear attention network (DMBA-NET) framework with two basic attention units (BAN-GA and BAN-SA) to construct inter-modality and intra-modality relations. The two basic attention units are the core of the whole network framework and can be cascaded in depth. In addition, we encode the question based on the dynamic word vector of BERT(Bidirectional Encoder Representations from Transformers), then use self-attention to process the question features further. Then we sum them with the features obtained by BAN-GA and BAN-SA before the final classification. Without using the Visual Genome datasets for augmentation, the accuracy of our model reaches 70.85% on the test-std dataset of VQA 2.0.

It is becoming harder to tell rumors from non-rumors as social media becomes a key news source, which invites malicious manipulation that could do harm to the public’s health or cause financial loss. When faced with situations when the session structure of comment sections is deliberately disrupted, traditional models do not handle them adequately. In order to do this, we provide a novel rumor detection architecture that combines dual comparison learning, adversarial training, and attention filters. We suggest the attention filter module to achieve the filtering of some dangerous comments as well as the filtering of some useless comments, allowing the nodes to enter the GAT graph neural network with greater structural information. The adversarial training module (ADV) simulates the occurrence of malicious comments through perturbation, giving the comments some defense against malicious comments. It also serves as a hard negative sample to aid double contrast learning (DCL), which aims to learn the differences between various comments, and incorporates the final loss in the form of a loss function to strengthen the model. According to experimental findings, our AGAD (Attention Graph Adversarial Dual Contrast Learning) model outperforms other cutting-edge algorithms on a number of rumor detection tasks. The code is available at https://github.com/icezhangGG/AGAD.git .

As an important remote sensing technology, millimeter-wave radar is used for environmental sensing in many fields due to its advantages of all-day, all-weather operation. With the increasing use of radars, inter-radar interference becomes increasingly critical. Severe mutual interference degrades radar signal quality and affects the performance of post-processing, e.g., synthetic aperture radar (SAR) imaging and target tracking. Aiming to deal with mutual interference, we propose an interference mitigation method based on variational mode decomposition (VMD). With the characteristics that the target is a single-frequency sine wave and the interference is a broadband signal, VMD is used for decomposing the radar received signal and separating the target from the interference. Interference mitigation is then implemented in each decomposed mode, and an interference-free signal is obtained through the reconstruction process. Simulation results of multi-target scenarios demonstrate that the proposed method outperforms existing decomposition-based methods. This conclusion is also confirmed by the experimental results on real data.