Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses

Inspired by human dancers who can evaluate the aesthetics of their own dance poses through mirror observation, this paper presents a corresponding mechanism for robots to improve their cognitive and autonomous abilities. Essentially, the proposed mechanism is a brain-like intelligent system that is symmetrical to the visual cognitive nervous system of the human brain. Specifically, a computable cognitive model of visual aesthetics is developed using the two important aesthetic cognitive neural models of the human brain, which is then applied in the automatic aesthetics evaluation of robotic dance poses. Three kinds of features (color, shape and orientation) are extracted in a manner similar to the visual feature elements extracted by human brains. After applying machine learning methods in different feature combinations, machine aesthetics models are built for automatic evaluation of robotic dance poses. The simulation results show that our approach can process visual information effectively by cognitive computation, and achieved a very good evaluation performance of automatic aesthetics

Multi-Criteria Decision-Making Method Using Heronian Mean Operators under a Bipolar Neutrosophic Environment

In real applications, most decisions are fuzzy decisions, and the decision results mainly depend on the choice of aggregation operators. In order to aggregate information more scientifically and reasonably, the Heronian mean operator was studied in this paper. Considering the advantages and limitations of the Heronian mean (HM) operator, four Heronian mean operators for bipolar neutrosophic number (BNN) are proposed: the BNN generalized weighted HM (BNNGWHM) operator, the BNN improved generalized weighted HM (BNNIGWHM) operator, the BNN generalized weighted geometry HM (BNNGWGHM) operator, and the BNN improved generalized weighted geometry HM (BNNIGWGHM) operator. Then, their propositions were examined. Furthermore, two multi-criteria decision methods based on the proposed BNNIGWHM and BNNIGWGHM operator are introduced under a BNN environment. Lastly, the effectiveness of the new methods was verified with an example

Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson’s correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing

Automatic Aesthetics Evaluation of Robotic Dance Poses Based on Hierarchical Processing Network.

Vision plays an important role in the aesthetic cognition of human beings. When creating dance choreography, human dancers, who always observe their own dance poses in a mirror, understand the aesthetics of those poses and aim to improve their dancing performance. In order to develop artificial intelligence, a robot should establish a similar mechanism to imitate the above human dance behaviour. Inspired by this, this paper designs a way for a robot to visually perceive its own dance poses and constructs a novel dataset of dance poses based on real NAO robots. On this basis, this paper proposes a hierarchical processing network-based approach to automatic aesthetics evaluation of robotic dance poses. The hierarchical processing network first extracts the primary visual features by using three parallel CNNs, then uses a synthesis CNN to achieve high-level association and comprehensive processing on the basis of multi-modal feature fusion, and finally makes an automatic aesthetics decision. Notably, the design of this hierarchical processing network is inspired by the research findings in neuroaesthetics. Experimental results show that our approach can achieve a high correct ratio of aesthetic evaluation at 82.3%, which is superior to the existing methods

Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select action Trust or Mistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select action Trust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose action Trust as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism

Endurance exercise accelerates myocardial tissue oxygenation recovery and reduces ischemia reperfusion injury in mice

Exercise training offers cardioprotection against ischemia and reperfusion (I/R) injury. However, few essential signals have been identified to underscore the protection from injury. In the present study, we hypothesized that exercise-induced acceleration of myocardial tissue oxygenation recovery contributes to this protection. C57BL/6 mice (4 weeks old) were trained on treadmills for 45 min/day at a treading rate of 15 m/min for 8 weeks. At the end of 8-week exercise training, mice underwent 30-min left anterior descending coronary artery occlusion followed by 60-min or 24-h reperfusion. Electron paramagnetic resonance oximetry was performed to measure myocardial tissue oxygenation. Western immunoblotting analyses, gene transfection, and myography were examined. The oximetry study demonstrated that exercise markedly shortened myocardial tissue oxygenation recovery time following reperfusion. Exercise training up-regulated Kir6.1 protein expression (a subunit of ATP-sensitive K(+)channel on vascular smooth muscle cells, VSMC sarc-K(ATP)) and protected the heart from I/R injury. In vivo gene transfer of dominant negative Kir6.1AAA prolonged the recovery time and enlarged infarct size. In addition, transfection of Kir6.1AAA increased the stiffness and reduced the relaxation capacity in the vasculature. Together, our study demonstrated that exercise training up-regulated Kir6.1, improved tissue oxygenation recovery, and protected the heart against I/R injury. This exercise-induced cardioprotective mechanism may provide a potential therapeutic intervention targeting VSMC sarc-K(ATP) channels and reperfusion recovery

MnmE, a Central tRNA-Modifying GTPase, Is Essential for the Growth, Pathogenicity, and Arginine Metabolism of Streptococcus suis Serotype 2

Streptococcus suis is an important pathogen in pigs and can also cause severe infections in humans. However, little is known about proteins associated with cell growth and pathogenicity of S. suis. In this study, a guanosine triphosphatase (GTPase) MnmE homolog was identified in a Chinese isolate (SC19) that drives a tRNA modification reaction. A mnmE deletion strain (ΔmnmE) and a complementation strain (CΔmnmE) were constructed to systematically decode the characteristics and functions of MnmE both in vitro and in vivo studies via proteomic analysis. Phenotypic analysis revealed that the ΔmnmE strain displayed deficient growth, attenuated pathogenicity, and perturbation of the arginine metabolic pathway mediated by the arginine deiminase system (ADS). Consistently, tandem mass tag -based quantitative proteomics analysis confirmed that 365 proteins were differentially expressed (174 up- and 191 down-regulated) between strains ΔmnmE and SC19. Many proteins associated with DNA replication, cell division, and virulence were down-regulated. Particularly, the core enzymes of the ADS were significantly down-regulated in strain ΔmnmE. These data also provide putative molecular mechanisms for MnmE in cell growth and survival in an acidic environment. Therefore, we propose that MnmE, by its function as a central tRNA-modifying GTPase, is essential for cell growth, pathogenicity, as well as arginine metabolism of S. suis

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

Element-Weighted Neutrosophic Correlation Coefficient and Its Application in Improving CAMShift Tracker in RGBD Video

Neutrosophic set (NS) is a new branch of philosophy to deal with the origin, nature, and scope of neutralities. Many kinds of correlation coefficients and similarity measures have been proposed in neutrosophic domain. In this work, by considering that there may exist different contributions for the neutrosophic elements of T (Truth), I (Indeterminacy), and F (Falsity), a method of element-weighted neutrosophic correlation coefficient is proposed, and it is applied for improving the CAMShift tracker in RGBD (RGB-Depth) video. The concept of object seeds is proposed, and it is employed for extracting object region and calculating the depth back-projection. Each candidate seed is represented in the single-valued neutrosophic set (SVNS) domain via three membership functions, T, I, and F. Then the element-weighted neutrosophic correlation coefficient is applied for selecting robust object seeds by fusing three kinds of criteria. Moreover, the proposed correlation coefficient is applied for estimating a robust back-projection by fusing the information in both color and depth domains. Finally, for the scale adaption problem, two alternatives in the neutrosophic domain are proposed, and the corresponding correlation coefficient between the proposed alternative and the ideal one is employed for the identification of the scale. When considering challenging factors like fast motion, blur, illumination variation, deformation, and camera jitter, the experimental results revealed that the improved CAMShift tracker performs well

Signal transduction systems involved in ischemic preconditioning and ATP-sensitive K+ channels

Brief episodes of ischemia followed by reperfusion render the heart more resistant to injury from a subsequent prolonged ischemia. This endogenous and highly protective phenomenon is known as ischemic preconditioning. Although the phenomenon has been demonstrated virtually in all species tested including nun, the mechanism of ischemic preconditioning is incompletely understood. Early studies showed that both adenosine A1 receptors and K ATP channels appeared to mediate the effects of preconditioning in most species, but not in the rat. Therefore, we first investigated the signal transduction mechanisms involved in ischemic preconditioning against post-ischemic contractile dysfunction in isolated Langendorff-perfused rat hearts. We found that ischemic preconditioning in the rat heart is due to stimulation of alpha1B-adrenoceptors by release of endogenous catecholamines, resulting in acfivation of a pertussis toxin-sensitive guanine nucleotide binding (G) protein which enhances protein kinase C (PKC) activity. The results support the hypothesis that G protein-dependent PKC activation may be the common mechanism in signal transduction pathways of ischemic preconditioning among species. Accumulating evidence suggests that ATP-sensitive K+ channel (KATP channel) activation plays an important protective role in ischemic preconditioning. We hypothesized that PKC stimulation in ischemic preconditioning may produce cardioprotection by modulating KATP channel function, and studied further the modulation of the KATP channel by PKC-mediated phosphorylation, in rabbit and human ventricular myocytes, using voltage clamp techniques. We found that PKC activation stimulates KATP-induced opening at reduced intracellular ATP concentrations by reducing KATP channel sensitivity to intracellular ATP. In order to determine whether membrane-bound PKC could explain previously-described adenosine-induced KATP channel activation in excised membrane patches, we studied KATP channel functio