USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model

Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects. Existing methods typically estimate the object likelihood with an additional objectness branch, but ignore the conflict in learning objectness and classification boundaries, which oppose each other on the semantic manifold and training objective. To address this issue, we propose a simple yet effective learning strategy, namely Decoupled Objectness Learning (DOL), which divides the learning of these two boundaries into suitable decoder layers. Moreover, detecting unknown objects comprehensively requires a large amount of annotations, but labeling all unknown objects is both difficult and expensive. Therefore, we propose to take advantage of the recent Large Vision Model (LVM), specifically the Segment Anything Model (SAM), to enhance the detection of unknown objects. Nevertheless, the output results of SAM contain noise, including backgrounds and fragments, so we introduce an Auxiliary Supervision Framework (ASF) that uses a pseudo-labeling and a soft-weighting strategies to alleviate the negative impact of noise. Extensive experiments on popular benchmarks, including Pascal VOC and MS COCO, demonstrate the effectiveness of our approach. Our proposed Unknown Sensitive Detector (USD) outperforms the recent state-of-the-art methods in terms of Unknown Recall, achieving significant improvements of 14.3\%, 15.5\%, and 8.9\% on the M-OWODB, and 27.1\%, 29.1\%, and 25.1\% on the S-OWODB

An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022

In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In the competition, we designed two kinds of models: Transformer-M-ViSNet which is an geometry-enhanced graph neural network for fully connected molecular graphs and Pretrained-3D-ViSNet which is a pretrained ViSNet by distilling geomeotric information from optimized structures. With an ensemble of 22 models, ViSNet Team achieved the MAE of 0.0723 eV on the test-challenge set, dramatically reducing the error by 39.75% compared with the best method in the last year competition

ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Additionally, ViSNet achieved the top winners of PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures

Dysbiosis of Gut Microbiota Promotes Hepatocellular Carcinoma Progression by Regulating the Immune Response

Background and Aim. Dysbiosis of gut microbiota is important in the development of hepatocellular carcinoma (HCC). However, little is known about whether and how dysbiosis impacts HCC progression. This cross-sectional study is aimed at evaluating microbiome dysbiosis, gut damage, and microbial translocation in different stages of HCC. Method. This study included 74 Chinese male patients with HCC. They were divided into early (n=19), intermediate (n=37), and terminal (n=18) groups, referred to as Barcelona Clinic Liver Cancer stage 0+A, B, and C+D, respectively. Paired fecal and plasma samples were collected. Microbial composition and profiles were analyzed by 16S rRNA gene sequencing. The levels of gut damage marker (regenerating islet-derived protein 3 alpha (REG3 alpha)) and microbial translocation markers (soluble CD14 (sCD14), lipopolysaccharide-binding protein (LBP), peptidoglycan recognition proteins (PGRPs)) were determined in plasma samples of patients by ELISA. Twenty plasma cytokine and chemokines were determined by Luminex. Results. In early, intermediate, and terminal groups, the abundance of the Bifidobacteriaceae family decreased significantly (3.52%, 1.55%, and 0.56%, respectively, P=0.003), while the abundance of the Enterococcaceae family increased significantly (1.6%, 2.9%, and 13.4%, respectively, P=0.022). Levels of REG3 alpha and sCD14 were markedly elevated only in the terminal group compared with the early (P=0.025 and P=0.048) and intermediate groups (P=0.023 and P=0.046). The level of LBP significantly increased in the intermediate (P=0.035) and terminal (P=0.025) groups compared with the early group. The PGRP levels were elevated only in the terminal group compared with the early group (P=0.018). The ratio of Enterococcaceae to Bifidobacteriaceae was significantly associated with the levels of REG3 alpha, LBP, sCD14, and PGRPs. With HCC progression, increased levels of inflammatory cytokines accompanied by a T cell-immunosuppressive response and microbial translocation were observed. Conclusion. Gut microbiota compositional and functional shift, together with elevated gut damage and microbial translocation, may promote HCC development by stimulating inflammatory response and suppressing T cell response.</p

Model Comparisons of Flow and Chemical Kinetic Mechanisms for Methane–Air Combustion for Engineering Applications

The reasonably accurate numerical simulation of methane–air combustion is important for engineering purposes. In the present work, the validations of sub-models were carried out on a laboratory-scale turbulent jet flame, Sandia Flame D, in comparison with experimental data. The eddy dissipation concept (EDC), which assumes that the molecular mixing and subsequent combustion occur in the fine structures, was used for the turbulence–chemistry interaction. The standard k-ε model (SKE) with the standard or the changed model constant C1ε, the realizable k-ε model (RKE), the shear-stress transport k-ω model (SST), and the Reynolds stress model (RSM) were compared with the detailed chemical kinetic mechanism of GRI-Mech 3.0. Different reaction treatments for the methane–air combustion were also validated with the available experimental data from the literature. In general, there were good agreements between predictions and measurements, which gave a good indication of the adequacy and accuracy of the method and its further applications for industry-scale turbulent combustion simulations. The differences between predictions and measured data might have come from the simplifications of the boundary settings, the turbulence model, the turbulence–reaction interaction, and the radiation heat transfer model. For engineering predictions of methane–air combustion, the mixture fraction probability density function (PDF) model for the partially premixed combustion with RSM is recommended due to its relatively low simulation expenses, acceptable accuracy predictions, and quantitatively good agreement with the experiments

Mechanical Properties of Longmaxi Black Organic-Rich Shale Samples from South China under Uniaxial and Triaxial Compression States

With the exploitation of shale gas booming all over the world, more and more studies are focused on the core technology, hydraulic fracturing, to improve commercial exploitation. Shale gas resources in China are enormous. In this research, a series of tests were carried out with samples of black organic-rich shale from the Lower Silurian Longmaxi formation, south China. Samples were drilled from different directions and were subjected to uniaxial and triaxial condition with various confining pressures, aiming at studying its rock mechanics properties, so as to provide basis for research and breakthrough of hydraulic fracturing technology. According to the results of the study, the development and distribution of shale’s bedding planes significantly impact its mechanical properties. Shale samples show obvious brittle characteristics under low confining pressure, and its mechanical behavior begins to transform from brittle to plastic characteristics with increasing confining pressure. Shale samples with different inclinations (β) have different sensitivities to the confining pressure. As a result, samples with 45° inclinations (β) are least sensitive. The strength of bedding planes is significantly lower than that of shale matrix, and tensile failure and shear failure generally tend to occur along the bedding planes. When hydraulic fracturing was conducted in shale formation with depth less than 2.25 km, corresponding to original in-situ of 60 MPa, cracks will preferably occur at first along the inclination (β) angle of 45° from the maximum principal stress, and the failure mode is most likely to be shear failure without volumetric strain. And, different modes of failure will occur at different locations in the reservoir, depending on the orientation of bedding inclined from the principle stress, which can probably explain the phenomenon why there are fractures along and cross the bedding planes during hydraulic fracturing treatment. When hydraulic fracturing was conducted in shale formation with depth greater than 2.25 km, hydraulic fractures may not crack along the bedding surfaces to some extent

Extensive Discussions of the Eddy Dissipation Concept Constants and Numerical Simulations of the Sandia Flame D

The indisputable wide use of the Eddy Dissipation Concept (EDC) implies that the resulting mean reaction rate is reasonably well modeled. To model turbulent combustions, an amount of EDC constants that differ from the original values was proposed. However, most of them were used without following the nature of the model or considering the effects of the modification. Starting with the energy cascade and the EDC models, the exact original primary and secondary constants are deduced in detail in this work. The mean reaction rate is then formulated from the primary constants or the secondary constants. Based on the physical meaning of fine structures, the limits of the EDC constants are presented and can be used to direct the EDC constant modifications. The effects of the secondary constant on the mean reaction rate are presented and the limiting turbulence Reynolds number used for the validity of EDC is discussed. To show the effects of the constants of the EDC model on the mean reaction rate, 20 combinations of the primary constants are used to simulate a laboratory-scale turbulent jet flame, i.e., Sandia Flame D. After a thorough and careful comparison with experiments, case 8, with a secondary constant of 6 and primary constants of 0.1357 and 0.11, can aptly reproduce this flame, except for in the over-predicted mean OH mass fraction

Developing Simplified Chinese Psychological Linguistic Analysis Dictionary for Microblog

The words that people use could reveal their emotional states, inten-tions, thinking styles, individual differences, etc. LIWC (Linguistic Inquiry and Word Count) has been widely used for psychological text analysis, and its dic-tionary is the core. The Traditional Chinese version of LIWC dictionary has been released, which is a translation of LIWC English dictionary. However, Simplified Chinese which is the world&#39;s most widely used language has subtle differences with Traditional Chinese. Furthermore, both English LIWC dictio-nary and Traditional Chinese version dictionary were both developed for rela-tively formal text. Microblog has become more and more popular in China no-wadays. Original LIWC dictionaries take less consideration on microblog popu-lar words, which makes it less applicable for text analysis on microblog. In this study, a Simplified Chinese LIWC dictionary is established according to LIWC categories. After translating Traditional Chinese dictionary into Simplified Chi-nese, five thousand words most frequently used in microblog are added into the dictionary. Four graduate students of psychology rated whether each word be-longed in a category. The reliability and validity of Simplified Chinese LIWC dictionary were tested by these four judges. This new dictionary could contri-bute to all the text analysis on microblog in future.</p

Effects of Cerium Doping on the Mechanical Properties and Energy-Releasing Behavior of High-Entropy Alloys

Energetic structural materials play an important role in improving the damage performance of future weapons. To improve the energy-releasing behavior, Al0.5NbZrTi1.5Ta0.8Cex high-entropy alloys were prepared by vacuum-arc melting. The results showed the presence of BCC and FCC phases in the alloy with dendritic-morphology-element segregation and there were significant dislocations in the alloys. The current study focused on the effects of cerium content on the dynamic compressive mechanical and energetic characteristics. Cerium doping enhanced the energy-releasing characteristics of high-entropy alloys. The severity of the reaction increased with the increase in the cerium content, while the dynamic compressive strength generally decreased with the increase in cerium content. The Al0.5NbZrTi1.5Ta0.8Ce0.25 showed excellent mechanical and energy-releasing characteristics. The ballistic experiments indicated that Al0.5NbZrTi1.5Ta0.8Ce0.25 can penetrate 6-millimeter A3 plates and ignite the cotton behind the target at a velocity of 729 m/s, making it an ideal energetic structural material