A Survey on Soft Subspace Clustering

Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been extensively studied and well accepted by the scientific community, SSC algorithms are relatively new but gaining more attention in recent years due to better adaptability. In the paper, a comprehensive survey on existing SSC algorithms and the recent development are presented. The SSC algorithms are classified systematically into three main categories, namely, conventional SSC (CSSC), independent SSC (ISSC) and extended SSC (XSSC). The characteristics of these algorithms are highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201

Fuzzy Knowledge Distillation from High-Order TSK to Low-Order TSK

High-order Takagi-Sugeno-Kang (TSK) fuzzy classifiers possess powerful classification performance yet have fewer fuzzy rules, but always be impaired by its exponential growth training time and poorer interpretability owing to High-order polynomial used in consequent part of fuzzy rule, while Low-order TSK fuzzy classifiers run quickly with high interpretability, however they usually require more fuzzy rules and perform relatively not very well. Address this issue, a novel TSK fuzzy classifier embeded with knowledge distillation in deep learning called HTSK-LLM-DKD is proposed in this study. HTSK-LLM-DKD achieves the following distinctive characteristics: 1) It takes High-order TSK classifier as teacher model and Low-order TSK fuzzy classifier as student model, and leverages the proposed LLM-DKD (Least Learning Machine based Decoupling Knowledge Distillation) to distill the fuzzy dark knowledge from High-order TSK fuzzy classifier to Low-order TSK fuzzy classifier, which resulting in Low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to High-order TSK classifier, as well as high interpretability; specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it compute teacher/student soft labels by the softmax function with distillating temperature parameter; 3) By reformulating the Kullback-Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model. The advantages of HTSK-LLM-DKD are verified on the benchmarking UCI datasets and a real dataset Cleveland heart disease, in terms of classification performance and model interpretability

Precise Measurements of Branching Fractions for Ds+D_s^+ Meson Decays to Two Pseudoscalar Mesons

We measure the branching fractions for seven $D_{s}^{+}$ two-body decays to pseudo-scalar mesons, by analyzing data collected at $\sqrt{s}=4.178\sim4.226$ GeV with the BESIII detector at the BEPCII collider. The branching fractions are determined to be $\mathcal{B}(D_s^+\to K^+\eta^{\prime})=(2.68\pm0.17\pm0.17\pm0.08)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta^{\prime}\pi^+)=(37.8\pm0.4\pm2.1\pm1.2)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\eta)=(1.62\pm0.10\pm0.03\pm0.05)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta\pi^+)=(17.41\pm0.18\pm0.27\pm0.54)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+K_S^0)=(15.02\pm0.10\pm0.27\pm0.47)\times10^{-3}$, $\mathcal{B}(D_s^+\to K_S^0\pi^+)=(1.109\pm0.034\pm0.023\pm0.035)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\pi^0)=(0.748\pm0.049\pm0.018\pm0.023)\times10^{-3}$, where the first uncertainties are statistical, the second are systematic, and the third are from external input branching fraction of the normalization mode $D_s^+\to K^+K^-\pi^+$. Precision of our measurements is significantly improved compared with that of the current world average values

The Influence of Academic Emotions on Learning Effects: A Systematic Review

Academic emotions can have different influences on learning effects, but these have not been systematically studied. In this paper, we objectively evaluate the influence of various academic emotions on learning effects and studied the relationship between positive and negative academic emotions and learning effects by using five electronic databases, including WOS, EMBASE, PubMed, PsycINFO, and Google Scholar. According to established standards, a total of 14 articles from 506 articles were included in the analysis. We divided the 14 studies into nine intervention studies and five observational studies; five of the nine intervention studies found that students who used active learning materials performed better and had higher mental loads than those who used neutral learning materials. Positive academic emotions promoted the learning effect. Four of the five observational studies with high school, college, and postgraduate participants reported that regulating academic emotions can improve learning effects. In conclusion, this paper holds that positive academic emotions are better than negative academic emotions at improving academic performance. In future research, a new method combining multichannel video observation, physiological data, and facial expression data is proposed to capture learners’ learning behavior in various learning environments

Detection of Thrombin Based on Fluorescence Energy Transfer between Semiconducting Polymer Dots and BHQ-Labelled Aptamers

Carboxyl-functionalized semiconducting polymer dots (Pdots) were synthesized as an energy donor by the nanoprecipitation method. A black hole quenching dye (BHQ-labelled thrombin aptamers) was used as the energy acceptor, and fluorescence resonance energy transfer between the aptamers and Pdots was used for fluorescence quenching of the Pdots. The addition of thrombin restored the fluorescence intensity. Under the optimized experimental conditions, the fluorescence of the system was restored to the maximum when the concentration of thrombin reached 130 nM, with a linear range of 0–50 nM (R2 = 0.990) and a detection limit of 0.33 nM. This sensor was less disturbed by impurities, showing good specificity and signal response to thrombin, with good application in actual samples. The detection of human serum showed good linearity in the range of 0–30 nM (R2 = 0.997), with a detection limit of 0.56 nM and a recovery rate of 96.2–104.1%, indicating that this fluorescence sensor can be used for the detection of thrombin content in human serum

Erratum: Liu, Y.-Z.; Jiang, X.-K.; Cao, W.-F.; Sun, J.-Y.; Gao, F. Detection of Thrombin Based on Fluorescence Energy Transfer between Semiconducting Polymer Dots and BHQ-Labelled Aptamers. <em>Sensors</em> 2018, <em>18</em>, 589

The authors wish to make the following corrections to their paper [...