Can the Query-based Object Detector Be Designed with Fewer Stages?

Query-based object detectors have made significant advancements since the publication of DETR. However, most existing methods still rely on multi-stage encoders and decoders, or a combination of both. Despite achieving high accuracy, the multi-stage paradigm (typically consisting of 6 stages) suffers from issues such as heavy computational burden, prompting us to reconsider its necessity. In this paper, we explore multiple techniques to enhance query-based detectors and, based on these findings, propose a novel model called GOLO (Global Once and Local Once), which follows a two-stage decoding paradigm. Compared to other mainstream query-based models with multi-stage decoders, our model employs fewer decoder stages while still achieving considerable performance. Experimental results on the COCO dataset demonstrate the effectiveness of our approach

PuzzleFlex: kinematic motion of chains with loose joints

This paper presents a method of computing free motions of a planar assembly of rigid bodies connected by loose joints. Joints are modeled using local distance constraints, which are then linearized with respect to configuration space velocities, yielding a linear programming formulation that allows analysis of systems with thousands of rigid bodies. Potential applications include analysis of collections of modular robots, structural stability perturbation analysis, tolerance analysis for mechanical systems, and formation control of mobile robots.Comment: Accepted at the 2020 IEEE International Conference on Robotics and Automation (ICRA

Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria

Transformation of Inclusions in Solid GCr15 Bearing Steels During Heat Treatment

Laboratory heating experiments with a varied holding time of GCr15 bearing steels at 1498 K were performed to study the transformation of inclusions in solid GCr15 bearing steels during high temperature diffusion processes. Heating experiments at 1573 and 1648 K were also carried out to study the effect of these heating temperatures. Experimental results showed that inclusions transformed from Al2O3-CaO-(MgO) to Al2O3-CaS-(MgO-CaO) when the heat treatment was in the range of 1498 to 1648 K due to reactions between Al and S in the steel matrix and CaO in the inclusions. This is in good agreement with thermodynamic calculations. Moreover, the size of the inclusions hardly changed after heat treatment. The transformation rate of the inclusions depended strongly on both the heating temperature and the size of the inclusions. Kinetic analyses on the transformation of inclusions during heat treatment were performed based on a simplified analytical model. The mass transfer coefficients of CaO and CaS in inclusions were calculated, which ranged from 0.73 × 10−10 to 4.48 × 10−10 m/s

Dependence of the Clogging Possibility of the Submerged Entry Nozzle during Steel Continuous Casting Process on the Liquid Fraction of Non-Metallic Inclusions in the Molten Al-Killed Ca-Treated Steel

In the current study, the nozzle clogging behavior and inclusion composition in Al-killed Ca-treated steels were observed to investigate the relationship between the liquid fraction of non-metallic inclusions and the clogging possibility of the submerged entry nozzle. Clogging materials were mainly MgO-Al2O3 with less than 20% liquid phases, while most of the inclusions were full liquid CaO-Al2O3-MgO in tundish at the casting temperature. Thus, it was proposed that the nozzle clogging can be effectively avoided by modification of solid inclusions to partial liquid ones rather than full liquid ones. There was a critical value of liquid fraction of inclusions causing the nozzle clogging. A critical condition of the inclusion attachment on the nozzle wall was a function of cosθN−S+cosθI−S<0. With the increase of T.Ca content in steel, the evolution route of inclusions was solid MgO-Al2O3→liquid CaO-Al2O3-MgO→solid CaS and CaO. To avoid the clogging of the submerged entry nozzle (SEN) under the current casting condition, the appropriate T.Ca concentration range in Al-killed Ca-treated steels can be enlarged from the 100% liquid inclusion zone of 10–14 ppm to the 20% liquid inclusion zone of 4–38 ppm

Influence of Glutamic Acid on the Properties of Poly(xylitol glutamate sebacate) Bioelastomer

In order to further improve the biocompatibility of xylitol based poly(xylitol sebacate) (PXS) bioelastomer, a novel kind of amino acid based poly(xylitol glutamate sebacate) (PXGS) has been successfully prepared in this work by melt polycondensation of xylitol, N-Boc glutamic acid and sebacic acid. Differential scanning calorimetry (DSC) results indicated the glass-transition temperatures could be decreased by feeding N-Boc glutamic acid. In comparison to PXS, PXGS exhibited comparable tensile strength and much higher elongation at break at the same ratio of acid/xylitol. The introduction of glutamic acid increased the hydrophilicity and in vitro degradation rate of the bioelastomer. It was found that PXGS exhibited excellent properties, such as tensile properties, biodegradability and hydrophilicity, which could be easily tuned by altering the feeding monomer ratios. The amino groups in the PXGS polyester side chains are readily functionalized, thus the biomelastomers can be considered as potential biomaterials for biomedical application

Identifying Terrestrial Landscape Character Types in China

Landscape character assessment (LCA) is a widely used tool that integrates natural, cultural, and perceptual attributes to identify and portray landscape. In this study, we used the LCA method to identify the landscape characteristics of China at the national scale. Furthermore, we applied cultural and landscape structural factors along with spatial transmission to improve the identification system. First, we incorporated all the parameters in the assessment. We selected 15 landscape character factors from four factor types including nature, culture, spatial geographic co-ordinates, and landscape structure. These parameters were analysed using multilevel overlay and spatial connection tools in ArcGis 10.2, which resulted in 2307 landscape description units (LDUs). Second, the spatial structure properties of the LDUs were determined using a semivariogram and the moving window method in ArcGis 10.2 and Fragstats 4.2 software, respectively. Third, for visualisation, we applied the principal component analysis (PCA) using the SPSS software and elbow and k-means clustering methods using MATLAB to determine 110 landscape character types (LCTs) of China’s entire terrestrial landscape. Finally, we determined 1483 landscape character areas through semiautomatic segmentation and manual visual correction using eCognition. Based on the unique characteristics of the entire terrestrial landscape of China, a set of ideas and methods for the overall identification of LCTs was proposed. Our findings can be used to optimise territorial spatial planning and landscape protection and management, and promote multiscale land-use studies in China

Stepdown SLOPE for Controlled Feature Selection

Sorted L-One Penalized Estimation (SLOPE) has shown the nice theoretical property as well as empirical behavior recently on the false discovery rate (FDR) control of high-dimensional feature selection by adaptively imposing the non-increasing sequence of tuning parameters on the sorted L1 penalties. This paper goes beyond the previous concern limited to the FDR control by considering the stepdown-based SLOPE in order to control the probability of k or more false rejections (k-FWER) and the false discovery proportion (FDP). Two new SLOPEs, called k-SLOPE and F-SLOPE, are proposed to realize k-FWER and FDP control respectively, where the stepdown procedure is injected into the SLOPE scheme. For the proposed stepdown SLOPEs, we establish their theoretical guarantees on controlling k-FWER and FDP under the orthogonal design setting, and also provide an intuitive guideline for the choice of regularization parameter sequence in much general setting. Empirical evaluations on simulated data validate the effectiveness of our approaches on controlled feature selection and support our theoretical findings