TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and some objects sharing similar visual appearance. In contrast, we propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Specifically, we first obtain rich high-level structured semantic concept information from large-scale vision data in a self-supervised learning manner, and use such information as a prior to discover potential semantic categories presented in target datasets. Secondly, the discovered high-level semantic categories are mapped to low-level pixel features by calculating the class activate map (CAM) with respect to certain discovered semantic representation. Lastly, the obtained CAMs serve as pseudo labels to train the segmentation module and produce the final semantic segmentation. Experimental results on multiple semantic segmentation benchmarks show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets under different semantic granularity levels, and outperforms all the current state-of-the-art bottom-up methods. Our code is available at \url{https://github.com/damo-cv/TransFGU}.Comment: Accepted by ECCV 2022, Oral, open-source

HLungDB: an integrated database of human lung cancer research

The human lung cancer database (HLungDB) is a database with the integration of the lung cancer-related genes, proteins and miRNAs together with the corresponding clinical information. The main purpose of this platform is to establish a network of lung cancer-related molecules and to facilitate the mechanistic study of lung carcinogenesis. The entries describing the relationships between molecules and human lung cancer in the current release were extracted manually from literatures. Currently, we have collected 2585 genes and 212 miRNA with the experimental evidences involved in the different stages of lung carcinogenesis through text mining. Furthermore, we have incorporated the results from analysis of transcription factor-binding motifs, the promoters and the SNP sites for each gene. Since epigenetic alterations also play an important role in lung carcinogenesis, genes with epigenetic regulation were also included. We hope HLungDB will enrich our knowledge about lung cancer biology and eventually lead to the development of novel therapeutic strategies. HLungDB can be freely accessed at http://www.megabionet.org/bio/hlung

Marginal increase of sunitinib exposure by grapefruit juice

Clinical Oncolog

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

International audienceIn recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region

Snap29 Is Dispensable for Self-Renewal Maintenance but Required for Proper Differentiation of Mouse Embryonic Stem Cells

Pluripotent embryonic stem cells (ESCs) can self-renew indefinitely and are able to differentiate into all three embryonic germ layers. Synaptosomal-associated protein 29 (Snap29) is implicated in numerous intracellular membrane trafficking pathways, including autophagy, which is involved in the maintenance of ESC pluripotency. However, the function of Snap29 in the self-renewal and differentiation of ESCs remains elusive. Here, we show that Snap29 depletion via CRISPR/Cas does not impair the self-renewal and expression of pluripotency-associated factors in mouse ESCs. However, Snap29 deficiency enhances the differentiation of ESCs into cardiomyocytes, as indicated by heart-like beating cells. Furthermore, transcriptome analysis reveals that Snap29 depletion significantly decreased the expression of numerous genes required for germ layer differentiation. Interestingly, Snap29 deficiency does not cause autophagy blockage in ESCs, which might be rescued by the SNAP family member Snap47. Our data show that Snap29 is dispensable for self-renewal maintenance, but required for the proper differentiation of mouse ESCs