DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion

Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference. To address this task, this paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions and weakens that from occlusion. First, to enhance token representation, we design an expanded convolution head with enlarged kernels, which captures more local valid context and mitigates the influence of surrounding occlusion. To concentrate on the visible human parts, we propose a novel dynamic multi-head human-mask guided attention mechanism through integrating multiple masks, which can prevent the de-occluded regions from assimilating to the background. Besides, a region upsampling strategy is utilized to alleviate the impact of occlusion on interpolated images. During model learning, an amodal loss is developed to further emphasize the recovery effect of human regions, which also refines the model's convergence. Extensive experiments on the AHP dataset demonstrate its superior performance compared to recent state-of-the-art methods

A systematic and comprehensive analysis of T cell exhaustion related to therapy in lung adenocarcinoma tumor microenvironment

Background: T cell exhaustion (TEX) is an important immune escape mechanism, and an in-depth understanding of it can help improve cancer immunotherapy. However, the prognostic role of TEX in malignant lung adenocarcinoma (LUAD) remains unclear.Methods: Through TCGA and GEO datasets, we enrolled a total of 498 LUAD patients. The patients in TCGA-LUAD were unsupervised clustered into four clusters according to TEX signaling pathway. WGCNA analysis, survival random forest analysis and lasso regression analysis were used to select five differentially expressed genes among different clusters to construct a TEX risk model. The risk model was subsequently validated with GEO31210. By analyzing signaling pathways, immune cells and immune checkpoints using GSEA, GSVA and Cibersortx, the relationship between TEX risk score and these variables was evaluated. In addition, we further analyzed the expression of CCL20 at the level of single-cell RNA-seq and verified it in cell experiments.Results: According to TEX signaling pathway, people with better prognosis can be distinguished. The risk model constructed by CD109, CCL20, DKK1, TNS4, and TRIM29 genes could further accurately identify the population with poor prognosis. Subsequently, it was found that dendritic cells, CD44 and risk score were closely related. The final single-cell sequencing suggested that CCL2O is a potential therapeutic target of TEX, and the interaction between TEX and CD8 + T is closely related.Conclusion: The classification of T cell depletion plays a crucial role in the clinical decision-making of lung adenocarcinoma and needs to be further deepened

Infinite symmetric products of rational algebras and spaces

We show that the infinite symmetric product of a connected graded-commutative algebra over the rationals is naturally isomorphic to the free graded-commutative algebra on the positive degree subspace of the original algebra. In particular, the infinite symmetric product of a connected commutative (in the usual sense) graded algebra over the rationals is a polynomial algebra. Applied to topology, we obtain a quick proof of the Dold-Thom theorem in rational homotopy theory for connected spaces of finite type. We also show that finite symmetric products of certain simple free graded commutative algebras are free; this allows us to determine minimal Sullivan models for finite symmetric products of complex projective spaces.Comment: Minor revisions according to referee comments. Accepted to Comptes Rendus - Math\'ematique. 11 page

STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications.Comment: 36th AAAI Conference on Artificial Intelligence (AAAI 2022

DeDA: Deep Directed Accumulator

Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. These rim+ lesions exhibit a geometrically simple structure, where gradients at the lesion edge are radially oriented and a greater magnitude of gradients is observed in contrast to rim- (non rim+) lesions. However, recent studies have shown that the identification performance of such lesions remains unsatisfied due to the limited amount of data and high class imbalance. In this paper, we propose a simple yet effective image processing operation, deep directed accumulator (DeDA), that provides a new perspective for injecting domain-specific inductive biases (priors) into neural networks for rim+ lesion identification. Given a feature map and a set of sampling grids, DeDA creates and quantizes an accumulator space into finite intervals, and accumulates feature values accordingly. This DeDA operation is a generalized discrete Radon transform and can also be regarded as a symmetric operation to the grid sampling within the forward-backward neural network framework, the process of which is order-agnostic, and can be efficiently implemented with the native CUDA programming. Experimental results on a dataset with 177 rim+ and 3986 rim- lesions show that 10.1% of improvement in a partial (false positive rate<0.1) area under the receiver operating characteristic curve (pROC AUC) and 10.2% of improvement in an area under the precision recall curve (PR AUC) can be achieved respectively comparing to other state-of-the-art methods. The source code is available online at https://github.com/tinymilky/DeDAComment: 18 pages, 3 Tables and 4 figure

Effect of Danhong injection on heart failure in rats evaluated by metabolomics

BackgroundHeart failure (HF) is characterized by reduced ventricular filling or ejection function due to organic or non-organic cardiovascular diseases. Danhong injection (DHI) is a medicinal material used clinically to treat HF for many years in China. Although prior research has shown that Danhong injection can improve cardiac function and structure, the biological mechanism has yet to be determined.MethodsSerum metabolic analysis was conducted via ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UHPLC-QE/MS) to explore underlying protective mechanisms of DHI in the transverse aortic constriction (TAC)-induced heart failure. Multivariate statistical techniques were used in the research, such as unsupervised principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). MetaboAnalyst and Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed to pinpoint pertinent metabolic pathways.ResultsAfter DHI treatment, cardiac morphology and function as well as the metabolism in model rats were improved. We identified 17 differential metabolites and six metabolic pathways. Two biomarkers, PC(18:3(6Z,9Z,12Z)/24:0) and L-Phenylalanine, were identified for the first time as strong indicators for the significant effect of DHI.ConclusionThis study revealed that DHI could regulate potential biomarkers and correlated metabolic pathway, which highlighted therapeutic potential of DHI in managing HF