CORE: Cooperative Reconstruction for Multi-Agent Perception

This paper presents CORE, a conceptually simple, effective and communication-efficient model for multi-agent cooperative perception. It addresses the task from a novel perspective of cooperative reconstruction, based on two key insights: 1) cooperating agents together provide a more holistic observation of the environment, and 2) the holistic observation can serve as valuable supervision to explicitly guide the model learning how to reconstruct the ideal observation based on collaboration. CORE instantiates the idea with three major components: a compressor for each agent to create more compact feature representation for efficient broadcasting, a lightweight attentive collaboration component for cross-agent message aggregation, and a reconstruction module to reconstruct the observation based on aggregated feature representations. This learning-to-reconstruct idea is task-agnostic, and offers clear and reasonable supervision to inspire more effective collaboration, eventually promoting perception tasks. We validate CORE on OPV2V, a large-scale multi-agent percetion dataset, in two tasks, i.e., 3D object detection and semantic segmentation. Results demonstrate that the model achieves state-of-the-art performance on both tasks, and is more communication-efficient.Comment: Accepted to ICCV 2023; Code: https://github.com/zllxot/COR

Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution

Backprojection networks have achieved promising super-resolution performance for nature images but not well be explored in the remote sensing image super-resolution (RSISR) field due to the high computation costs. In this paper, we propose a Multi-granularity Backprojection Transformer termed MBT for RSISR. MBT incorporates the backprojection learning strategy into a Transformer framework. It consists of Scale-aware Backprojection-based Transformer Layers (SPTLs) for scale-aware low-resolution feature learning and Context-aware Backprojection-based Transformer Blocks (CPTBs) for hierarchical feature learning. A backprojection-based reconstruction module (PRM) is also introduced to enhance the hierarchical features for image reconstruction. MBT stands out by efficiently learning low-resolution features without excessive modules for high-resolution processing, resulting in lower computational resources. Experiment results on UCMerced and AID datasets demonstrate that MBT obtains state-of-the-art results compared to other leading methods

Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various methods constrain attention to a local window, enhancing its efficiency. Consequently, the receptive fields in a single attention layer are inadequate, leading to insufficient context modeling. Furthermore, while most transform-based approaches reuse shallow features through skip connections, relying solely on these connections treats shallow and deep features equally, impeding the model's ability to characterize them. To address these issues, we propose a novel transformer architecture called Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively enhances global cognition and understanding of the entire image, facilitating efficient integration of features cross-stages. The model incorporates cross-spatial pixel integration attention (CSPIA) to introduce contextual information into a local window, while cross-stage feature fusion attention (CSFFA) adaptively fuses features from the previous stage to improve feature expression in line with the requirements of the current stage. We conducted comprehensive experiments on multiple benchmark datasets, demonstrating the superior performance of our proposed SPIFFNet in terms of both quantitative metrics and visual quality when compared to state-of-the-art methods

Biliary Neuroendocrine Neoplasms: Clinical Profiles, Management, and Analysis of Prognostic Factors

Biliary neuroendocrine neoplasms (NENs) represent <1% of all NENs. The aim of this retrospective study is to present the clinical characteristics, management and prognosis profiles of 28 biliary NEN patients from a large tertiary center, and identify factors related to prognosis. Nine tumors originated from the gallbladder, two from the extrahepatic bile duct and 17 from the ampulla of Vater. One patient was classified as neuroendocrine tumor (NET) Grade 1, three patients were classified as NET Grade 2, 18 were graded neuroendocrine carcinoma (NEC) Grade 3 and six were classified as mixed adenoneuroendocrine carcinoma (MANEC). The overall survival rate and disease-free survival rate did not have statistically significant differences between tumors of different locations or different grading. Recurrence of disease correlated with poor prognosis (p < 0.001). Lymphovascular invasion and invasion beyond the submucosa were related to higher risk of local lymph node metastases. Multivariate analysis identified patient age (p = 0.021) and R0 resection margin (p = 0.027) as independent prognostic factors associated with overall survival. Our study included relatively large numbers of biliary tract NENs with intact follow-up information. Patients with biliary neuroendocrine tumors showed different clinical outcomes according to tumor locations and tumor grades. Achieving R0 resection is important for better prognosis

MoS₂ Nanosheets Vertically Grown on Carbonized Corn Stalks as Lithium-Ion Battery Anode

In this study, MoS₂ nanosheets are vertically grown on the inside and outside surfaces of the carbonized corn stalks (CCS) by a simple hydrothermal reaction. The vertically grown structure can not only improve the transmission rate of Li⁺ and electrons but also avoid the agglomeration of the nanosheets. Meanwhile, a new approach of biomass source application is presented. We use CCS instead of graphite powders, which can not only avoid the exploitation of graphite resources, but also be used as a matrix for MoS₂ growth to prevent the electrode from being further decomposed during long cycles and at high current densities. Meanwhile, lithium-ion batteries show remarkable electrochemical performance. They demonstrate a high specific capacity of 1409.5 mA g^–1 at 100 mA g^–1 in the initial cycle. After 250 cycles, the discharge capacity is still as high as 1230.9 mAh g^–1. Even at 4000 mA g^–1, they show a high specific capacity of 777.7 mAh g^–1. Furthermore, the MoS₂/CCS electrodes show long cycle life, and the specific capacity is still up to ∼500 mAh g^–1 at 5000 mA g^–1 after 1000 cycles

Pancreatic stromal Gremlin 1 expression during pancreatic tumorigenesis

© 2020 Chronic pancreatitis (CP) is a major risk factor of pancreatic ductal adenocarcinoma (PDAC). How CP promotes pancreatic oncogenesis remains unclear. A characteristic feature of PDAC is its prominent desmoplasia in the tumor microenvironment, composed of activated fibroblasts and macrophages. Macrophages can be characterized as M1 or M2, with tumor-inhibiting or -promoting functions, respectively. We reported that Gremlin 1 (GREM1), a key pro-fibrogenic factor, is upregulated in the stroma of CP. The current study aimed to investigate the expression of GREM1 and correlation between GREM1 and macrophages within the pancreas during chronic inflammation and the development of PDAC. By mRNA in situ hybridization, we detected GREM1 mRNA expression within α-smooth muscle actin (SMA)-positive fibroblasts of the pancreatic stroma. These designated FibroblastsGrem1+ marginally increased from CP to pancreatic intraepithelial neoplasia (PanIN) and PDAC. Within PDAC, FibroblastsGrem1+ increased with higher pathological tumor stages and in a majority of PDAC subtypes screened. Additionally, FibroblastsGrem1+ positively correlated with total macrophages (MacCD68+) and M2 macrophages (M2CD163+) in PDAC. To begin exploring potential molecular links between FibroblastsGrem1+ and macrophages in PDAC, we examined the expression of macrophage migration inhibitory factor (MIF), an endogenous counteracting molecule of GREM1 and an M1 macrophage promoting factor. By IHC staining of MIF, we found MIF to be expressed by tumor cells, positively correlated with GREM1; by IHC co-staining, we found MIF to be negatively correlated with M2CD163+ expression. Our findings suggest that GREM1 expression by activated fibroblasts may promote PDAC development, and GREM1/MIF may play an important role in macrophage phenotype

Unique Reversible Conversion-Type Mechanism Enhanced Cathode Performance in Amorphous Molybdenum Polysulfide

A unique reversible conversion-type mechanism is reported in the amorphous molybdenum polysulfide (a-MoS_5.7) cathode material. The lithiation products of metallic Mo and Li₂S₂ rather than Mo and Li₂S species have been detected. This process could yield a high discharge capacity of 746 mAh g^–1. Characterizations of the recovered molybdenum polysulfide after the delithiaiton process manifests the high reversibility of the unique conversion reaction, in contrast with the general irreversibility of the conventional conversion-type mechanism. As a result, the a-MoS_5.7 electrodes deliver high cycling stability with an energy-density retention of 1166 Wh kg^–1 after 100 cycles. These results provide a novel model for the design of high-capacity and long-life electrode materials