Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer

Conversation structure is useful for both understanding the nature of conversation dynamics and for providing features for many downstream applications such as summarization of conversations. In this work, we define the problem of conversation structure modeling as identifying the parent utterance(s) to which each utterance in the conversation responds to. Previous work usually took a pair of utterances to decide whether one utterance is the parent of the other. We believe the entire ancestral history is a very important information source to make accurate prediction. Therefore, we design a novel masking mechanism to guide the ancestor flow, and leverage the transformer model to aggregate all ancestors to predict parent utterances. Our experiments are performed on the Reddit dataset (Zhang, Culbertson, and Paritosh 2017) and the Ubuntu IRC dataset (Kummerfeld et al. 2019). In addition, we also report experiments on a new larger corpus from the Reddit platform and release this dataset. We show that the proposed model, that takes into account the ancestral history of the conversation, significantly outperforms several strong baselines including the BERT model on all datasetsComment: AAAI 202

Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.Comment: Pattern Recognitio

Downregulation of desmuslin in primary vein incompetence

ObjectivePrimary vein incompetence is one of the most common diseases of the peripheral veins, but its pathogenesis is unknown. These veins present obvious congenital defects, and examination of gene expression profiles of the incompetent vein specimens may provide important clues. The aim of this study was to screen for genes affecting the primary vein incompetence phenotype and test the differential expression of certain genes.MethodsWe compared gene expression profiles of valvular areas from incompetent and normal great saphenous veins at the saphenofemoral junctions by fluorescent differential display reverse-transcription polymerase chain reaction (FDD RT-PCR). Differentially expressed complimentary DNAs (cDNAs) were confirmed by Northern blotting and semi-quantitative RT-PCR. Similarity of the cDNAs sequences to GenBank sequences was determined. Gene expression status was then determined by Western blot analysis and immunohistochemical techniques.ResultsThere were >30 differentially expressed cDNA bands. Sequence analysis revealed that a cDNA fragment obviously downregulated in incompetent great saphenous vein was a portion of the messenger RNA (mRNA) encoding desmuslin, a newly discovered intermittent filament protein. Northern blotting and semi-quantitative RT-PCR analysis revealed a similar mRNA expression profile of the desmuslin gene in other samples. Western blotting and immunohistochemical techniques localized the desmuslin protein mainly in the cytoplasm of venous smooth muscle cells. The amount of desmuslin was greatly decreased in the smooth muscle cells of incompetent veins.ConclusionsThe expression of many genes is altered in primary vein incompetence. Up- or downregulation of these genes may be involved in the pathogenesis of this disease. Desmuslin expression is downregulated in the abnormal veins. Its effect on the integrity of smooth muscle cells might be related to malformation of the vein wall. Further studies are needed to investigate other differentially expressed cDNAs and the exact role of desmuslin in this disease.Clinical RelevancePrimary vein incompetence is a frequent and refractory disease of the peripheral veins. Exploring its pathogenesis may enhance our comprehension and management of this disease. We used reliable techniques to detect disease-related genes and confirmed downregulation of desmuslin in abnormal veins. Alteration of these genes might be used as disease markers or gene therapy targets

LCReg: Long-Tailed Image Classification with Latent Categories based Recognition

In this work, we tackle the challenging problem of long-tailed image recognition. Previous long-tailed recognition approaches mainly focus on data augmentation or re-balancing strategies for the tail classes to give them more attention during model training. However, these methods are limited by the small number of training images for the tail classes, which results in poor feature representations. To address this issue, we propose the Latent Categories based long-tail Recognition (LCReg) method. Our hypothesis is that common latent features shared by head and tail classes can be used to improve feature representation. Specifically, we learn a set of class-agnostic latent features shared by both head and tail classes, and then use semantic data augmentation on the latent features to implicitly increase the diversity of the training sample. We conduct extensive experiments on five long-tailed image recognition datasets, and the results show that our proposed method significantly improves the baselines.Comment: accepted by Pattern Recognition. arXiv admin note: substantial text overlap with arXiv:2206.0101

Feature Boosting Network For 3D Pose Estimation

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.Comment: Accepted to T-PAMI. DOI: 10.1109/TPAMI.2019.289442