Optimization-Based Three-Way Decisions With Interval-Valued Intuitionistic Fuzzy Information.

Due to the effectiveness and advantages of interval-valued intuitionistic fuzzy sets (IVIFSs) in evaluating uncertainty and risk, we introduce IVIFSs into loss functions of decision-theoretic rough sets (DTRSs) and propose an optimization-based approach to interval-valued intuitionistic fuzzy three-way decisions. First, based on the classical DTRSs and two previous optimization models, we construct a new concise linear programming model for simultaneously determining the threshold pair. Our model is mathematically equivalent to the DTRSs and the previous models under the Karush-Kuhn-Tucker (KKT) condition. Second, we extend the constructed model via the IVIFSs of loss functions and we discuss the relations between these loss functions based on a similarity measure function-based ranking method and a multiple score function-based ranking method for IVIFSs. Third, we develop our extended models via two ranking methods and we prove the existence and uniqueness of the optimal solution of the model. The optimization-based method, along with its algorithm for three-way decisions, is designed in an interval-valued intuitionistic fuzzy environment. Compared to the latest existing methods, our method has three advantages (see Advantages 1-3). Finally, an illustrative example is considered, and the advantages of our approach are demonstrated by this example.

Electronic, Thermal, and Thermoelectric Transport Properties of ε-Ga2O3 from First Principles.

The electronic, thermal, and thermoelectric transport properties of ε-Ga2O3 have been obtained from first-principles calculation. The band structure and electron effective mass tensor of ε-Ga2O3 were investigated by density functional theory. The Born effective charge and dielectric tensor were calculated by density perturbation functional theory. The thermal properties, including the heat capacity, thermal expansion coefficient, bulk modulus, and mode Grüneisen parameters, were obtained using the finite displacement method together with the quasi-harmonic approximation. The results for the relationship between the Seebeck coefficient and the temperature and carrier concentration of ε-Ga2O3 are presented according to the ab initio band energies and maximally localized Wannier function. When the carrier concentration of ε-Ga2O3 increases, the electrical conductivity increases but the Seebeck coefficient decreases. However, the figure of merit of thermoelectric application can still increase with the carrier concentration.

Enhanced Group Sparse Regularized Nonconvex Regression for Face Recognition.

Regression analysis based methods have shown strong robustness and achieved great success in face recognition. In these methods, convex l1-norm and nuclear norm are usually utilized to approximate the l0-norm and rank function. However, such convex relaxations may introduce a bias and lead to a suboptimal solution. In this paper, we propose a novel Enhanced Group Sparse regularized Nonconvex Regression (EGSNR) method for robust face recognition. An upper bounded nonconvex function is introduced to replace l1-norm for sparsity, which alleviates the bias problem and adverse effects caused by outliers. To capture the characteristics of complex errors, we propose a mixed model by combining γ-norm and matrix γ-norm induced from the nonconvex function. Furthermore, an l2,γ-norm based regularizer is designed to directly seek the interclass sparsity or group sparsity instead of traditional l2,1-norm. The locality of data, i.e., the distance between the query sample and multi-subspaces, is also taken into consideration. This enhanced group sparse regularizer enables EGSNR to learn more discriminative representation coefficients. Comprehensive experiments on several popular face datasets demonstrate that the proposed EGSNR outperforms the state-of-the-art regression based methods for robust face recognition.

Locality-Constrained Discriminative Matrix Regression for Robust Face Identification.

Regression-based methods have been widely applied in face identification, which attempts to approximately represent a query sample as a linear combination of all training samples. Recently, a matrix regression model based on nuclear norm has been proposed and shown strong robustness to structural noises. However, it may ignore two important issues: the label information and local relationship of data. In this article, a novel robust representation method called locality-constrained discriminative matrix regression (LDMR) is proposed, which takes label information and locality structure into account. Instead of focusing on the representation coefficients, LDMR directly imposes constraints on representation components by fully considering the label information, which has a closer connection to identification process. The locality structure characterized by subspace distances is used to learn class weights, and the correct class is forced to make more contribution to representation. Furthermore, the class weights are also incorporated into a competitive constraint on the representation components, which reduces the pairwise correlations between different classes and enhances the competitive relationships among all classes. An iterative optimization algorithm is presented to solve LDMR. Experiments on several benchmark data sets demonstrate that LDMR outperforms some state-of-the-art regression-based methods.

Fabrication of Embedded Silver Nanowires on Arbitrary Substrates with Enhanced Stability via Chemisorbed Alkanethiolate.

We propose a versatile yet practical transferring technique to fabricate a high performance and extremely stable silver nanowire (AgNW) transparent electrode on arbitrary substrates. Hydroxylated poly(ethylene glycol) terephthalate (PET) or poly(dimethylsiloxane) (PDMS) deposited with AgNWs was selectively decorated to lower its polar surface energy, so that the AgNWs were easily and efficiently transferred into an epoxy resin (EPR) as a freestanding film (AgNWs-EPR) or onto various substrates. The AgNWs-EPR capped with alkanethiolate monolayers exhibits high conductivity, low roughness, ultraflexibility, and strong corrosion resistance. Using the transferring process, AgNWs-EPR was successfully constructed on rough, adhesive, flimsy, or complex curved substrates, including PET, thin optically clear adhesive, papers, a beaker, convex spherical PDMS, and leaves. A flexible touch panel enabling multitouch and a curved transparent heater on a beaker were first fabricated by using the composite film. These demonstrations suggest that the proposed technique for AgNWs is a promising strategy toward the next generation of flexible/portable/wearable electronics.