Cubic vertex-transitive non-Cayley graphs of order 12p

A graph is said to be {\em vertex-transitive non-Cayley} if its full automorphism group acts transitively on its vertices and contains no subgroups acting regularly on its vertices. In this paper, a complete classification of cubic vertex-transitive non-Cayley graphs of order $12p$, where $p$ is a prime, is given. As a result, there are $11$ sporadic and one infinite family of such graphs, of which the sporadic ones occur when $p=5$, $7$ or $17$, and the infinite family exists if and only if $p\equiv1\ (\mod 4)$, and in this family there is a unique graph for a given order.Comment: This paper has been accepted for publication in SCIENCE CHINA Mathematic

Semantics-Aligned Representation Learning for Person Re-identification

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID. Code for our proposed method is available at: https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), code has been release

How Do U.S. and Chinese Biology Students Compare in Explaining Energy Consumption Issues?

This qualitative study investigates how biology majors explain energy consumption issues. In particular, we focus on two energy consumption activities that account for about two-thirds of global carbon dioxide emissions in 2011: burning fossil fuels for transportation and using electricity. We conducted in-depth clinical interviews with twenty U.S. students and twenty Chinese students. We compared these two groups of students in terms of two aspects of explanation: 1) naming scientific terms in the explanation, and 2) explaining an energy consumption issue. Regarding naming, we examined the frequency of naming different terms of scientific concepts and principles in students’ explanations. Regarding explaining, we developed a rubric that differentiates three levels of explaining: informal explanations that are based upon intuitive ideas (Level 1), school science explanations that are based on alternative conceptions about matter and energy (Level 2), and scientific explanations that demonstrate the scientific understanding of concepts/principles about matter and energy (Level 3). The results revealed that scientific terms appeared most frequently in scientific explanations (Level 3), but they also appeared in many school science explanations (Level 2) and in some informal explanations (Level 1). We further describe how scientific terms were used in explanations at different levels. We found although Chinese students named scientific terms more frequently and demonstrated a better performance in explaining, they still produced more informal explanations and school science explanations than scientific explanations. In general, the results suggest the importance of promoting students’ abilities to use scientific terms correctly and meaningfully in explaining real-world environmental events in both countries

Reduced glutamine synthetase activity alters the fecundity of female Bactrocera dorsalis (Hendel)

Glutamine synthetase (GS) is a key enzyme in glutamine synthesis and is associated with multiple physiological processes in insects, such as embryonic development, heat shock response, and fecundity regulation. However, little is known about the influence of GS on female fecundity in the oriental fruit fly, Bactrocera dorsalis. Based on the cloning of BdGSs, mitochondrial BdGSm and cytoplasmic BdGSc, we determined their expressions in the tissues of adult B. dorsalis. BdGSm was highly expressed in the fat body, while BdGSc was highly expressed in the head and midgut. Gene silencing by RNA interference against two BdGSs isoforms suppressed target gene expression at the transcriptional level, leading to a reduced ovarian size and lower egg production. The specific inhibitor L-methionine S-sulfoximine suppressed enzyme activity, but only the gene expression of BdGSm was suppressed. A similar phenotype of delayed ovarian development occurred in the inhibitor bioassay. Significantly lower expression of vitellogenin and vitellogenin receptor was observed when GS enzyme activity was suppressed. These data illustrate the effects of two GS genes on adult fecundity by regulating vitellogenin synthesis in different ways