Local well-posedness and small Deborah limit of a molecule-based QQ-tensor system

In this paper, we consider a hydrodynamic $Q$-tensor system for nematic liquid crystal flow, which is derived from Doi-Onsager molecular theory by the Bingham closure. We first prove the existence and uniqueness of local strong solution. Furthermore, by taking Deborah number goes to zero and using the Hilbert expansion method, we present a rigorous derivation from the molecule-based $Q$-tensor theory to the Ericksen-Leslie theory.Comment: 44 page

Aberrant resting-state brain activity in Huntington's disease: A voxel-based meta-analysis

IntroductionFunctional neuroimaging could provide abundant information of underling pathophysiological mechanisms of the clinical triad including motor, cognitive and psychiatric impairment in Huntington's Disease (HD).MethodsWe performed a voxel-based meta-analysis using anisotropic effect size-signed differential mapping (AES-SDM) method.Results6 studies (78 symptomatic HD, 102 premanifest HD and 131 healthy controls) were included in total. Altered resting-state brain activity was primarily detected in the bilateral medial part of superior frontal gyrus, bilateral anterior cingulate/paracingulate gyrus, left insula, left striatum, right cortico-spinal projections area, right inferior temporal gyrus area, right thalamus, right cerebellum and right gyrus rectus area. Premanifest and symptomatic HD patients showed different alterative pattern in the subgroup analyses.DiscussionThe robust and consistent abnormalities in the specific brain regions identified in the current study could help to understand the pathophysiology of HD and explore reliable neuroimaging biomarkers for monitoring disease progression, or even predicting the onset of premanifest HD patients

Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts

Transformer-based pre-trained models have achieved great improvements in semantic matching. However, existing models still suffer from insufficient ability to capture subtle differences. The modification, addition and deletion of words in sentence pairs may make it difficult for the model to predict their relationship. To alleviate this problem, we propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences in sentence pairs by separately modeling affinity and difference semantics. Based on dual-path modeling framework we design the Dual Path Modeling Network (DPM-Net) to recognize semantic relations. And we conduct extensive experiments on 10 well-studied semantic matching and robustness test datasets, and the experimental results show that our proposed method achieves consistent improvements over baselines.Comment: ICASSP 2023. arXiv admin note: text overlap with arXiv:2210.0345

Multiscale Superpixel Structured Difference Graph Convolutional Network for VL Representation

Within the multimodal field, the key to integrating vision and language lies in establishing a good alignment strategy. Recently, benefiting from the success of self-supervised learning, significant progress has been made in multimodal semantic representation based on pre-trained models for vision and language. However, there is still room for improvement in visual semantic representation. The lack of spatial semantic coherence and vulnerability to noise makes it challenging for current pixel or patch-based methods to accurately extract complex scene boundaries. To this end, this paper develops superpixel as a comprehensive compact representation of learnable image data, which effectively reduces the number of visual primitives for subsequent processing by clustering perceptually similar pixels. To mine more precise topological relations, we propose a Multiscale Difference Graph Convolutional Network (MDGCN). It parses the entire image as a fine-to-coarse hierarchical structure of constituent visual patterns, and captures multiscale features by progressively merging adjacent superpixels as graph nodes. Moreover, we predict the differences between adjacent nodes through the graph structure, facilitating key information aggregation of graph nodes to reason actual semantic relations. Afterward, we design a multi-level fusion rule in a bottom-up manner to avoid understanding deviation by learning complementary spatial information at different regional scales. Our proposed method can be well applied to multiple downstream task learning. Extensive experiments demonstrate that our method is competitive with other state-of-the-art methods in visual reasoning. Our code will be released upon publication