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

OCLN as a novel biomarker for prognosis and immune infiltrates in kidney renal clear cell carcinoma: an integrative computational and experimental characterization

BackgroundOccludin (OCLN) is an important tight junction protein and has been reported to be abnormally expressed in the development of malignant tumors. However, its biomarker and carcinogenic roles in kidney renal clear cell carcinoma (KIRC) are less investigated.MethodsThe Cancer Genome Atlas database and Human Protein Atlas database were used to analyze the expression of OCLN in KIRC. UALCAN database and methylation-specific PCR assay were used to evaluate the methylation level of OCLN in KIRC. Univariate and multivariate Cox regression analyses were performed to model the prognostic significance of OCLN in KIRC patient cohorts. The correlation between OCLN expression and the immune cell infiltration, immune-related function and immune checkpoints were explored. Finally, EdU, scratch assay and transwell experiments were conducted to validate the role of OCLN in KIRC development.ResultsThe expression of OCLN was significantly downregulated in KIRC, compared with normal renal tissues (p<0.001). Patients with low OCLN expression showed a worse prognosis and poorer clinicopathological characteristics. Functional enrichment analysis revealed that OCLN was mainly involved in biological processes such as immune response, immunoglobulin complex circulating and cytokine and chemokine receptor to mediate KIRC development. Immune-related analysis indicated that OCLN could potentially serve as a candidate target for KIRC immunotherapy. OCLN overexpression inhibited proliferation, migration and invasion of KIRC cells in vitro.ConclusionOCLN was validated as a candidate prognostic biomarker and therapeutic target of KIRC based both on computational and experimental approaches. More in vivo experiments will be conducted to decode its molecular mechanism in KIRC carcinogenesis in the future work

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

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 [...

Design and Optimization of UAV Aerial Recovery System Based on Cable-Driven Parallel Robot

Aerial recovery and redeployment can effectively increase the operating radius and the endurance of unmanned aerial vehicles (UAVs). However, the challenge lies in the effect of the aerodynamic force on the recovery system, and the existing road-based and sea-based UAV recovery methods are no longer applicable. Inspired by the predatory behavior of net-casting spiders, this study introduces a cable-driven parallel robot (CDPR) for UAV aerial recovery, which utilizes an end-effector camera to detect the UAV’s flight trajectory, and the CDPR dynamically adjusts its spatial position to intercept and recover the UAV. This paper establishes a comprehensive cable model, simultaneously considering the elasticity, mass, and aerodynamic force, and the static equilibrium equation for the CDPR is derived. The effects of the aerodynamic force and cable tension on the spatial configuration of the cable are analyzed. Numerical computations yield the CDPR’s end-effector position error and cable-driven power consumption at discrete spatial points, and the results show that the position error decreases but the power consumption increases with the increase in the cable tension lower limit (CTLL). To improve the comprehensive performance of the recovery system, a multi-objective optimization method is proposed, considering the error distribution, power consumption distribution, and safety distance. The optimized CTLL and interception space position coordinates are determined through simulation, and comparative analysis with the initial condition indicates an 83% reduction in error, a 62.3% decrease in power consumption, and a 1.2 m increase in safety distance. This paper proposes a new design for a UAV aerial recovery system, and the analysis lays the groundwork for future research

Respiratory Sound Classification by Applying Deep Neural Network with a Blocking Variable

Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten people’s lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies to detect respiratory diseases without the presence of a doctor. Thus, it is worthwhile to develop an automatic respiratory disease detection system. This paper proposes a deep neural network with a blocking variable, namely Blnet, to classify respiratory sound, which integrates the strength of the ResNet, GoogleNet, and the self-attention mechanism. To solve the non-IID data problem, a two-stage training process with the blocking variable was developed. In addition, the mix-up data augmentation within the clusters was used to address the imbalanced data problem. The performance of the Blnet was tested on the ICBHI 2017 data, and the model achieved 79.13% specificity and 66.31% sensitivity, with an average score of 72.72%, which is a 4.22% improvement in the average score and a 12.61% improvement in sensitivity over the state-of-the-art results