Cross-domain named entity recognition (NER) tasks encourage NER models to transfer knowledge from data-rich source domains to sparsely labeled target domains. Previous works adopt the paradigms of pre-training on the source domain followed by fine-tuning on the target domain. However, these works ignore that general labeled NER source domain data can be easily retrieved in the real world, and soliciting more source domains could bring more benefits. Unfortunately, previous paradigms cannot efficiently transfer knowledge from multiple source domains. In this work, to transfer multiple source domains' knowledge, we decouple the NER task into the pipeline tasks of mention detection and entity typing, where the mention detection unifies the training object across domains, thus providing the entity typing with higher-quality entity mentions. Additionally, we request multiple general source domain models to suggest the potential named entities for sentences in the target domain explicitly, and transfer their knowledge to the target domain models through the knowledge progressive networks implicitly. Furthermore, we propose two methods to analyze in which source domain knowledge transfer occurs, thus helping us judge which source domain brings the greatest benefit. In our experiment, we develop a Chinese cross-domain NER dataset. Our model improved the F1 score by an average of 12.50% across 8 Chinese and English datasets compared to models without source domain data.

The Pulmonary Function Test (PFT) is a widely utilized and rigorous classification test for evaluating lung function, serving as a comprehensive diagnostic tool for lung conditions. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder (VAE) with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then stacked to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data from 137 subjects were utilized for the training phase. Initially, the model underwent validation through a leave-one-out cross-validation process. During this validation, the model achieved an accuracy and sensitivity of 0.96 and 1.00, respectively, with an f1-score of 0.98 when identifying the normal subjects. To assess pipeline reliability and feasibility, we tested it on 9 newly recruited subjects, with accurate ventilation mode predictions for 8 out of 9. In addition, we included 2D EIT results for comparison and conducted ablation experiments to validate the effectiveness of the VAE. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.

Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the feature space are optimized by minimizing the misfit between measured and simulated data. A dataset of 3D heads characterized by permittivity and conductivity is constructed to train the VAE. Numerical examples show that our method increases structural similarity by 14% and speeds up the solution process by over 3 orders of magnitude using only 4.8% number of the unknowns compared to the voxel-based method. This high-resolution imaging of electrical properties leads to more accurate stroke diagnosis and offers new insights into the study of the human brain.

Image-to-text tasks, such as open-ended image captioning and controllable image description, have received extensive attention for decades.Here, we further advance this line of work by presenting Visual Spatial Description (VSD), a new perspective for image-to-text toward spatial semantics.Given an image and two objects inside it, VSD aims to produce one description focusing on the spatial perspective between the two objects.Accordingly, we manually annotate a dataset to facilitate the investigation of the newly-introduced task and build several benchmark encoder-decoder models by using VL-BART and VL-T5 as backbones.In addition, we investigate pipeline and joint end-to-end architectures for incorporating visual spatial relationship classification (VSRC) information into our model.Finally, we conduct experiments on our benchmark dataset to evaluate all our models.Results show that our models are impressive, providing accurate and human-like spatial-oriented text descriptions.Meanwhile, VSRC has great potential for VSD, and the joint end-to-end architecture is the better choice for their integration.We make the dataset and codes public for research purposes.

Fei Li, ZhiChao Lin, Meishan Zhang, Donghong Ji. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently.The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling.The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem.In this work, we propose an end-to-end neural model to tackle the task jointly.Concretely, we exploit a graphbased method, regarding frame semantic parsing as a graph construction problem.All predicates and roles are treated as graph nodes, and their relations are taken as graph edges.Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.

We propose Spectrum Aware Virtual Coordinate (SAViC) for multi hop cognitive radio network (CRN) to facilitate geographic routing. The proposed virtual coordinates (VC) of any two secondary users reflect both geographic distance and opportunistic spectrum availability between them. As a result, geographic routing is able to detour the area affected by licensed users or cut through the area with more available spectrum. According to different spectrum occupation patterns of primary user, two versions of SAViC are designed based on the channel utility and primary user’s sojourning time respectively. Simulation shows the proposed virtual coordinate facilitates geographic routing to achieve high success rate of path construction. When duty cycle on the licensed channel is heterogeneous in the network, channel utility based virtual coordinate supports geographic routing to outperform a state-of-the-art geographic routing protocol by 40% on packet delivery ratio. When the channel utility is identical on each secondary node, and the sojourning time of primary users for secondary users are different from each other, SAViC based on primary user’s sojourning time achieves significantly shorter delay than other virtual coordinates.