Solving the Pose Ambiguity via a Simple Concentric Circle Constraint

Estimating the pose of objects with circle feature from images is a basic and important question in computer vision community. This paper is focused on the ambiguity problem in pose estimation of circle feature, and a new method is proposed based on the concentric circle constraint. The pose of a single circle feature, in general, can be determined from its projection in the image plane with a pre-calibrated camera. However, there are generally two possible sets of pose parameters. By introducing the concentric circle constraint, interference from the false solution can be excluded. On the basis of element at infinity in projective geometry and the Euclidean distance invariant, cases that concentric circles are coplanar and non-coplanar are discussed respectively. Experiments on these two cases are performed to validate the proposed method

The Effect of Consistency on Short-Term Memory for Scenes

Which is more detectable, the change of a consistent or an inconsistent object in a scene? This question has been debated for decades. We noted that the change of objects in scenes might simultaneously be accompanied with gist changes. In the present study we aimed to examine how the alteration of gist, as well as the consistency of the changed objects, modulated change detection. In Experiment 1, we manipulated the semantic content by either keeping or changing the consistency of the scene. Results showed that the changes of consistent and inconsistent scenes were equally detected. More importantly, the changes were more accurately detected when scene consistency changed than when the consistency remained unchanged, regardless of the consistency of the memory scenes. A phase-scrambled version of stimuli was adopted in Experiment 2 to decouple the possible confounding effect of low-level factors. The results of Experiment 2 demonstrated that the effect found in Experiment 1 was indeed due to the change of high-level semantic consistency rather than the change of low-level physical features. Together, the study suggests that the change of consistency plays an important role in scene short-term memory, which might be attributed to the sensitivity to the change of semantic content

\u3ci\u3eAuricularia auricula\u3c/i\u3e polysaccharides attenuate obesity in mice through gut commensal \u3ci\u3ePapillibacter cinnamivorans\u3c/i\u3e

Introduction: Auricularia auricula is a well-known traditional edible and medical fungus with high nutritional and pharmacological values, as well as metabolic and immunoregulatory properties. Nondigestible fermentable polysaccharides are identified as primary bioactive constituents of Auricularia auricula extracts. However, the exact mechanisms underlying the effects of Auricularia auricula polysaccharides (AAP) on obesity and related metabolic endpoints, including the role of the gut microbiota, remain insufficiently understood. Methods: The effects of AAP on obesity were assessed within high-fat diet (HFD)-based mice through obesity trait analysis and metabolomic profiling. To determine the mechanistic role of the gut microbiota in observed anti-obesogenic effects AAP, faecal microbiota transplantation (FMT) and pseudo-germ-free mice model treated with antibiotics were also applied, together with 16S rRNA genomic-derived taxonomic profiling. Results:High-fat diet (HFD) murine exposure to AAP thwarted weight gains, reduced fat depositing and enhanced glucose tolerance, together with upregulating thermogenesis proteomic biomarkers within adipose tissue. Serum metabolome indicated these effects were associated with changes in fatty acid metabolism. Intestine-dwelling microbial population assessments discovered that AAP selectively enhanced Papillibacter cinnamivorans, a commensal bacterium with reduced presence in HFD mice. Notably, HFD mice treated with oral formulations of P. cinnamivorans attenuated obesity, which was linked to decreased intestinal lipid transportation and hepatic thermogenesis. Mechanistically, it was demonstrated that P. cinnamivorans regulated intestinal lipids metabolism and liver thermogenesis by reducing the proinflammatory response and gut permeability in a JAK-STAT signaling-related manner. Conclusion: Datasets from the present study show that AAP thwarted dietary-driven obesity and metabolism-based disorders by regulating intestinal lipid transportation, a mechanism that is dependent on the gut commensal P. cinnamivorans. These results indicated AAP and P. cinnamivorans as newly identified pre- and probiotics that could serve as novel therapeutics against obesity

Ising Superconductivity and Quantum Phase Transition in Macro-Size Monolayer NbSe2

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have a range of unique physics properties and could be used in the development of electronics, photonics, spintronics and quantum computing devices. The mechanical exfoliation technique of micro-size TMD flakes has attracted particular interest due to its simplicity and cost effectiveness. However, for most applications, large area and high quality films are preferred. Furthermore, when the thickness of crystalline films is down to the 2D limit (monolayer), exotic properties can be expected due to the quantum confinement and symmetry breaking. In this paper, we have successfully prepared macro-size atomically flat monolayer NbSe2 films on bilayer graphene terminated surface of 6H-SiC(0001) substrates by molecular beam epitaxy (MBE) method. The films exhibit an onset superconducting critical transition temperature above 6 K, 2 times higher than that of mechanical exfoliated NbSe2 flakes. Simultaneously, the transport measurements at high magnetic fields reveal that the parallel characteristic field Bc// is at least 4.5 times higher than the paramagnetic limiting field, consistent with Zeeman-protected Ising superconductivity mechanism. Besides, by ultralow temperature electrical transport measurements, the monolayer NbSe2 film shows the signature of quantum Griffiths singularity when approaching the zero-temperature quantum critical point

Genetic Diversity and Ecological Niche Modelling of Wild Barley:Refugia, Large-Scale Post-LGM Range Expansion and Limited Mid-Future Climate Threats?

Describing genetic diversity in wild barley (Hordeum vulgare ssp. spontaneum) in geographic and environmental space in the context of current, past and potential future climates is important for conservation and for breeding the domesticated crop (Hordeum vulgare ssp. vulgare). Spatial genetic diversity in wild barley was revealed by both nuclear- (2,505 SNP, 24 nSSR) and chloroplast-derived (5 cpSSR) markers in 256 widely-sampled geo-referenced accessions. Results were compared with MaxEnt-modelled geographic distributions under current, past (Last Glacial Maximum, LGM) and mid-term future (anthropogenic scenario A2, the 2080s) climates. Comparisons suggest large-scale post-LGM range expansion in Central Asia and relatively small, but statistically significant, reductions in range-wide genetic diversity under future climate. Our analyses support the utility of ecological niche modelling for locating genetic diversity hotspots and determine priority geographic areas for wild barley conservation under anthropogenic climate change. Similar research on other cereal crop progenitors could play an important role in tailoring conservation and crop improvement strategies to support future human food security

DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation

In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines. For the latter, intermediate cues, such as 3D object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by CNN in the first stage. Object pose can then be solved by correspondence constraints constructed with these intermediate cues. In this paper, we focus on the postprocessing of a two-stage pipeline and propose to combine two learning concepts for estimating object pose under challenging scenes: projection grouping on one side, and correspondence learning on the other. We firstly employ a local-patch based method to predict projection heatmaps which denote the confidence distribution of projection of 3D bounding box’s corners. A projection grouping module is then proposed to remove redundant local maxima from each layer of heatmaps. Instead of directly feeding 2D⁻3D correspondences to the perspective-n-point (PnP) algorithm, multiple correspondence hypotheses are sampled from local maxima and its corresponding neighborhood and ranked by a correspondence⁻evaluation network. Finally, correspondences with higher confidence are selected to determine object pose. Extensive experiments on three public datasets demonstrate that the proposed framework outperforms several state of the art methods