Soluble Klotho (sKL) is closely related to insulin resistance, which is a major factor in the progression of diabetic cardiomyopathy (DCM). The purpose of this study was to investigate the role of ...sKL in the regulation of DCM and the mechanism involved. A mouse model of type 2 diabetes was induced by high‐fat diet and streptozotocin injection. An insulin‐resistant cardiac fibroblast model was established by high glucose and high insulin. KL gene overexpression was achieved in vivo and vitro through transfection with an adenovirus‐harboring KL‐cDNA. Gene overexpression was used to evaluate the role of sKL in the pathophysiologic characteristics of DCM. Insulin‐resistant cardiac fibroblasts reduced sKL expression and collagen deposition. Diabetic mice constructed by streptozotocin exhibited severe insulin resistance, inflammation, fibrosis, left ventricular dysfunction, and sKL downregulation. The overexpression of sKL mitigated insulin resistance and metabolic disturbance; inflammation, fibrosis, and upregulated collagen I/III content ratio in diabetic state were significantly reduced. Our findings were accompanied by notable moderation of cardiac function. Further, blunted phosphorylation of Akt was restored with sKL gene overexpression, and activated phosphorylation of extracellular signal‐regulated kinase 1/2 in DCM was reduced. Our results suggest that sKL protein overexpression exerts a defensive measure by ameliorating selective insulin resistance in mouse DCM, thus revealing its underlying mechanism for potential human DCM treatment.
In the context of recent deep clustering studies, discriminative models dominate the literature and report the most competitive performances. These models learn a deep discriminative neural network ...classifier in which the labels are latent. Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning. It is generally acknowledged that discriminative objective functions (e.g., those based on the mutual information or the KL divergence) are more flexible than generative approaches (e.g., K-means) in the sense that they make fewer assumptions about the data distributions and, typically, yield much better unsupervised deep learning results. On the surface, several recent discriminative models may seem unrelated to K-means. This study shows that these models are, in fact, equivalent to K-means under mild conditions and common posterior models and parameter regularization. We prove that, for the commonly used logistic regression posteriors, maximizing the <inline-formula><tex-math notation="LaTeX">L_2</tex-math> <mml:math><mml:msub><mml:mi>L</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="benayed-ieq1-2962683.gif"/> </inline-formula> regularized mutual information via an approximate alternating direction method (ADM) is equivalent to minimizing a soft and regularized K-means loss. Our theoretical analysis not only connects directly several recent state-of-the-art discriminative models to K-means, but also leads to a new soft and regularized deep K-means algorithm, which yields competitive performance on several image clustering benchmarks.
Data sparsity is a widespread problem of collaborative filtering (CF) recommendation algorithms. However, some common CF methods cannot adequately utilize all user rating information; they are only ...able to use a small part of the rating data, depending on the co-rated items, which leads to low prediction accuracy. To alleviate this problem, a novel K-medoids clustering recommendation algorithm based on probability distribution for CF is proposed. The proposed scheme makes full use of all rating information based on Kullback–Leibler (KL) divergence from the perspective of item rating probability distribution, and distinguishes different items efficiently when selecting the cluster centers. Meanwhile, the distance model breaks the symmetric mode of classic geometric distance methods (such as Euclidean distance) and considers the effects of different rating numbers between items to emphasize their asymmetric relationship. Experimental results on different datasets show that the proposed clustering algorithm outperforms other compared methods in various evaluation metrics; this approach enhances the prediction accuracy and effectively deals with the sparsity problem.
•An improved Kullback–Leibler (KL) divergence is introduced to calculate item similarity.•The optimum center of our algorithm is obtained by maximizing the contribution sum of distance.•An asymmetric mode is used to emphasize the asymmetric relationship between items.•Results show that our scheme improves the effectiveness of recommendation systems.
This article discusses a quantiser-based stabilisation problem for uncertain nonlinear systems with the aid of robust
$ \mathcal {K, KL} $
K
,
KL
sector. We confirmed that the quantised-feedback ...control steers the state trajectories of the uncertain nonlinear systems within the robust
$ \mathcal {K, KL} $
K
,
KL
sector in finite time by adjusting the quantisation parameters. Furthermore, control-Lyapunov functions are helpful to obtain sufficient conditions for ensuring the closed-loop system's asymptotic stability and the reachability of the robust
$ \mathcal {K, KL} $
K
,
KL
sector. Finally, a nonlinear electronic circuit is provided to test the effectiveness of the designed control law.
Design of Nonlinear Sectors With Comparison Functions Sachan, Ankit; Deveerasetty, Kranthi Kumar; Soni, Sandeep Kumar
IEEE transactions on circuits and systems. II, Express briefs,
2022-April, 2022-4-00, Letnik:
69, Številka:
4
Journal Article
Recenzirano
In this brief, a class of comparison functions is defined to design the nonlinear sectors for continuous/discrete-time analysis and ensure uniformity in the solution. The results of Matrosov's proof ...are incorporated to fragment the state-space to design both the continuous and the discrete-time <inline-formula> <tex-math notation="LaTeX">\mathcal {K},\mathcal {KL} </tex-math></inline-formula> sectors. Moreover, a mathematical relation is proposed between the sectors to define a discrete-time <inline-formula> <tex-math notation="LaTeX">\mathcal {K},\mathcal {KL} </tex-math></inline-formula> sector as a subset of the continuous-time <inline-formula> <tex-math notation="LaTeX">\mathcal {K},\mathcal {KL} </tex-math></inline-formula> sector. Taylor's series expansion relates the difference of the candidate-Lyapunov function to the derivative of the candidate-Lyapunov function for sampled-data, as well as higher-order derivatives. The validation of the result is verified by using the Van der Pol equation.
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for training. The ...construction of such emulators is by definition a small data problem which poses challenges to deep learning approaches that have been developed to operate in the big data regime. Even in cases where such models have been shown to have good predictive capability in high dimensions, they fail to address constraints in the data implied by the PDE model. This paper provides a methodology that incorporates the governing equations of the physical model in the loss/likelihood functions. The resulting physics-constrained, deep learning models are trained without any labeled data (e.g. employing only input data) and provide comparable predictive responses with data-driven models while obeying the constraints of the problem at hand. This work employs a convolutional encoder-decoder neural network approach as well as a conditional flow-based generative model for the solution of PDEs, surrogate model construction, and uncertainty quantification tasks. The methodology is posed as a minimization problem of the reverse Kullback-Leibler (KL) divergence between the model predictive density and the reference conditional density, where the later is defined as the Boltzmann-Gibbs distribution at a given inverse temperature with the underlying potential relating to the PDE system of interest. The generalization capability of these models to out-of-distribution input is considered. Quantification and interpretation of the predictive uncertainty is provided for a number of problems.
•Physics-constrained surrogates achieve comparable accuracy with data-driven ones without labeled data and generalize better.•Flow-based conditional generative model trained with reverse KL-divergence without labels captures the predictive uncertainty.•The developed models are used to solve PDEs, as surrogate models and for uncertainty propagation and calibration tasks.•Convolutional neural nets capture multiscale features of PDE solution fields much more effectively than fully-connected ones.
The present study deals with the successful preparation of kaolin/chitosan/titanium dioxide (KL-CTS-TiO2) ternary hybrid nanocomposite adsorbent using conventional and ultrasound assisted method ...synthesis. The particle size (observed form TEM image) of TiO2 nanoparticles dispersed in the nanocomposite was found to be around 5 nm for U-KL-CTS-TiO2 nanocomposite (ultrasonically prepared), which is lesser compared to conventionally prepared nanocomposite. The average particle size of U-KL-CTS-TiO2 nanocomposite was observed to be 293 nm which is very less compared to C-KL-CTS-TiO2 nanocomposite (prepared by conventional method) that is 439 nm. The BET surface area of U-KL-CTS-TiO2 nanocomposite was found to be 116.5 m2/g which is significantly higher than the C-KL-CTS-TiO2 nanocomposite (4.95 m2/g). In batch adsorption experiments the effect of initial dye concentration, time, temperature, and adsorbent dose was studied and equilibrium data, adsorption kinetics and adsorption isotherms parameters are reported. The adsorption equilibrium data was best fitted by Freundlich isotherm compared to the Langmuir and Temkin model for U-KL-CTS-TiO2 and C-KL-CTS-TiO2 nanocomposite. The % removal of CV dye for 2 g/L loading of U-KL-CTS-TiO2 and C-KL-CTS-TiO2 nanocomposite was found to be 93.30% and 85.49%, respectively. The higher adsorption in the case of U-KL-CTS-TiO2 nanocomposite is attributed to the physical effects of ultrasound which are responsible for the preparation of finely dispersed KL-CTS-TiO2 nanocomposite compared to conventional method. Finely dispersed KL-CTS-TiO2 nanocomposite provides more sites for the adsorption which in turn enhances the adsorption capacity of CV dye on ultrasonically prepared KL-CTS-TiO2 nanocomposite.
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•Preparation of KL-CTS-TiO2 nanocomposite using ultrasound assisted method.•KL-CTS-TiO2 nanocomposite prepared by ultrasonication is superior.•Effect of various parameters on CV dye adsorption is reported.•Pseudo second order kinetic model show better fit compared to other kinetic models.
Low-rank tensor completion plays an important role in many applications such as image processing, computer vision, and machine learning. A widely used convex relaxation of this problem is to minimize ...the nuclear norm of the square deal matrix generated by reshaping a tensor. However, this approach can be substantially suboptimal. In order to seek a highly accurate solution, in this paper, we propose to use a family of nonconvex functions onto the singular values of the square deal matrix of the tensor to approximate the rank of the tensor. A proximal linearized minimization (PLM) algorithm is proposed to solve the resulting model. Furthermore, based on the Kurdyka-Łojasiewicz property, we show that the sequence generated by the PLM algorithm globally converges to a critical point of the objective function. Extensive numerical experiments including synthetic data, video data, and the extended Yale Face Database B show the effectiveness of the proposed model compared with several existing state-of-the-art models.
In this paper, a bi-level control framework is proposed to improve the energy efficiency for a hybrid tracked vehicle. The higher-level discusses how to accurately predict power demand based on the ...Markov Chain. Specially, fuzzy encoding predictor is used for power demand prediction, and a real-time recursive algorithm is applied to fuse the future power demand information into transition probability matrix (TPM) computation. Furthermore, the Kullback-Leibler (KL) divergence rate is employed to decide the alteration of control strategy. The lower-level computes the relevant energy management strategy, based on the updated TPM and a model-free reinforcement learning (RL) technique. Simulation results illustrate that the vehicular energy efficiency in the proposed scheme exceeds the common RL control by tuning the KL divergence value. Comparative results also show that the developed control strategy outperforms the common RL one, in terms of energy efficiency and computational speed.
•A DRO model based on Kullback-Leibler (KL) divergence is constructed.•Energy sharing and profit allocation are considered in dispatching.•By introducing energy sharing, the operation cost of the ...community can be reduced.•Through profit reallocation, the enthusiasm of community cooperation can be improved.
Community energy consumption accounts for a large proportion of total social energy consumption. Therefore, community integrated energy systems have emerged to satisfy these demands. However, limited resource allocation of the integrated energy system in a single community is possible because of poor anti-interference and insufficient photovoltaic absorption. A distributionally robust optimal dispatching model was proposed for the joint operation of multiple community integrated energy systems, and the energy sharing and joint demand response among communities were considered. Kullback–Leibler (KL) divergence was used to describe the ambiguity set of photovoltaic output probability distribution. When the photovoltaic output prediction error obeyed the extreme probability distribution of ambiguity set, the minimum operation cost of multi-community was solved. Furthermore, to ensure successful alliance of multi-community cooperative alliance, a profit allocation mechanism based on the improved Shapley value was proposed to reallocate the excess return fairly. Finally, a typical multi community integrated energy system was simulated and analysed. The results revealed that energy sharing and joint demand response improve the economy of multi community operation, and the coordination of system robustness and economy of the model was realised. The proposed profit allocation mechanism can effectively ensure multi community cooperation.
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