BSG alignment of SDSS galaxy groups

We study the alignment signal between the distribution of brightest satellite galaxies (BSGs) and the major axis of their host groups using SDSS group catalog constructed by Yang et al. (2007). After correcting for the effect of group ellipticity, a statistically significant (~ 5\sigma) major-axis alignment is detected and the alignment angle is found to be 43.0 \pm 0.4 degrees. More massive and richer groups show stronger BSG alignment. The BSG alignment around blue BCGs is slightly stronger than that around red BCGs. And red BSGs have much stronger major-axis alignment than blue BSGs. Unlike BSGs, other satellites do not show very significant alignment with group major axis. We further explore the BSG alignment in semi-analytic model (SAM) constructed by Guo et al. (2011). We found general good agreement with observations: BSGs in SAM show strong major-axis alignment which depends on group mass and richness in the same way as observations; and none of other satellites exhibit prominent alignment. However, discrepancy also exists in that the SAM shows opposite BSG color dependence, which is most probably induced by the missing of large scale environment ingredient in SAM. The combination of two popular scenarios can explain the detected BSG alignment. The first one: satellites merged into the group preferentially along the surrounding filaments, which is strongly aligned with the major axis of the group. The second one: BSGs enter their host group more recently than other satellites, then will preserve more information about the assembling history and so the major-axis alignment. In SAM, we found positive evidence for the second scenario by the fact that BSGs merged into groups statistically more recently than other satellites. On the other hand, although is opposite in SAM, the BSG color dependence in observation might indicate the first scenario as well.Comment: 8 pages, 11 figures, ApJ accepte

Key Influencing Factors Affecting the Student Academic Performance and Student Satisfactions Ratings: Evidence from Undergraduate Students in China

This paper has developed a sound and practical method to evaluate the key teaching quality including the student academic performance and student satisfaction ratings. The method makes use of the existing data already readily available in a Chinese university, focusing on the identification of key influencing factors affecting the student academic performance and student satisfactions ratings. The data analyses have shown the university student academic performance is significantly affected student gender, age, previous academic performance, settlements and occupations of parents. There is significant difference in the student ratings for different genders and academic positions of teaching staff. The student performance and satisfaction ratings also significantly vary in different years of intakes and different Schools/programs. The student’s university academic performance can be accurately predicted using artificial neural networks with a prediction error of about 7%. This approach can help the university to improve the student academic performance and student satisfactions

A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding

Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of intent detection. More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). Scope Recognizer assignments scope information to each token, reducing the distraction of out-of-scope tokens. Result Attention Network effectively utilizes the bidirectional interaction between results of slot filling and intent detection, mitigating the error propagation problem. Experiments on two public datasets indicate that our model significantly improves SLU performance (5.4\% and 2.1\% on Overall accuracy) over the state-of-the-art baseline

The fabrication of electrochemical geophone based on FPCB process technology

The subject of the studies presented in this paper is the fabrication of electrochemical geophone, especially the electrochemical transducer with symmetrical four-electrode cell by FPCB process technology. The geophone assembled by transducer, dumbbell-shaped tube, highly-flexible membranes, electrolyte solution and signal-amplification circuit, is calibrated using a standard vibration platform, and the results show a good consistency of each geophone parameters. Coupled with low cost, the electrochemical geophone by FPCB shows a good potential application prospect

Multiscale Superpixel Structured Difference Graph Convolutional Network for VL Representation

Within the multimodal field, the key to integrating vision and language lies in establishing a good alignment strategy. Recently, benefiting from the success of self-supervised learning, significant progress has been made in multimodal semantic representation based on pre-trained models for vision and language. However, there is still room for improvement in visual semantic representation. The lack of spatial semantic coherence and vulnerability to noise makes it challenging for current pixel or patch-based methods to accurately extract complex scene boundaries. To this end, this paper develops superpixel as a comprehensive compact representation of learnable image data, which effectively reduces the number of visual primitives for subsequent processing by clustering perceptually similar pixels. To mine more precise topological relations, we propose a Multiscale Difference Graph Convolutional Network (MDGCN). It parses the entire image as a fine-to-coarse hierarchical structure of constituent visual patterns, and captures multiscale features by progressively merging adjacent superpixels as graph nodes. Moreover, we predict the differences between adjacent nodes through the graph structure, facilitating key information aggregation of graph nodes to reason actual semantic relations. Afterward, we design a multi-level fusion rule in a bottom-up manner to avoid understanding deviation by learning complementary spatial information at different regional scales. Our proposed method can be well applied to multiple downstream task learning. Extensive experiments demonstrate that our method is competitive with other state-of-the-art methods in visual reasoning. Our code will be released upon publication

microRNA 30A promotes autophagy in response to cancer therapy

microRNAs (miRNAs) are a class of small regulatory RNAs that regulate gene expression at the post-transcriptional level. miRNAs play important roles in the regulation of development, growth, and metastasis of cancer, and in determining the response of tumor cells to anticancer therapy. In recent years, they have also emerged as important regulators of autophagy, a lysosomal-mediated pathway that contributes to degradation of a cell's own components. Imatinib, a targeted competitive inhibitor of the BCR-ABL1 tyrosine kinase, has revolutionized the clinical treatment of chronic myelogenous leukemia (CML). We demonstrate that MIR30A-mediated autophagy enhances imatinib resistance against CML including primary stem and progenitor cells. MIR30A, but not MIR101, is a potent inhibitor of autophagy by selectively downregulating BECN1 and ATG5 expression in CML cells. MIR30A mimics, as well as knockdown of BECN1 and ATG5, increases intrinsic apoptotic pathways. In contrast, the antagomir-30A increases autophagy and inhibits intrinsic apoptotic pathways, confirming that autophagy serves to protect against apoptosis. Taken together, these data clarify some of the underlying molecular mechanisms of tyrosine kinase inhibitor-induced autophagy