En Wang

Modern cosmology has two competing theories of the origin of the universe: the "Singularity theory" and the "Superstring theory". Four Dilemmas of the "Super-string theory" are presented: the incompleteness of the eleven space–time dimensions,the inextricable dependence on the “Space–Time Background”, the "Zero-Brane the-ory" admitting stuff smaller than the Planck scale, and the pure mathematical theory that cannot be falsified by experiments. Although the "Singularity theory" is faced with many critiques from the "Superstring theory", from the perspective of Informa-tion Ontology, treating the "Singularity" as "Origin Information" can dissolve these troubles well. For the "Singularity theory", the new philosophical thinking frame-work effectively explains the parameter problems of the universe, and gives satisfied response to the challenges from the "Superstring theory". As a result, the "Singularity theory" has a more competitive advantage on the origin of the universe.

Smooth function is the essence of the ε-smooth support vector regression (ε-SSVR) which is an unconstrained convex quadratic programming. In this paper, Bézier function as a new smooth function is proposed to replace the ε-insensitive loss function of ε-SSVR with tolerating a smaller error in fitting given data sets linearly and nonlinearly. The ε-Bézier function smooth support vector regression (ε-BSSVR) model is presented. Theoretical analyses show that the derived smooth function has improved approximation precision compared with other smooth approximate functions. The Newton-Armijo algorithm is applied to solve the new model. Numerical results and comparisons demonstrate that the proposed method has the better regression performance and learning efficiency compared with other given models.

A new smooth method for solving the support vector machine classification (SVC) is presented. Since the objective function of the unconstrained SVC is non-smooth, we apply the smooth technique and replace the SVC function with Bézier function and get a class of Bézier smooth support vector machines (BSSVM). The fast Newton-Armijo algorithm is used to solve the BSSVM. Theoretical analysis and numerical results illustrate that this smooth SVM model improves in efficiency and accuracy compared with other smooth methods.

ε-support vector regression (ε-SVR) as a constrained minimization problem can be converted into an unconstrained convex quadratic programming. Smooth function is the essence of the ε-smooth support vector regression (ε-SSVR). In this paper, a new rotated hyperbola function is proposed to replace the ε-insensitive loss function. The ε-rotated hyperbola smooth support vector regression (ε-RHSSVR) model is presented. Theoretical analyses show that the derived smooth function has improved approximation precision compared with other smooth approximate functions. The Newton-Armijo algorithm is applied to train the new model. Numerical results and comparisons are given to demonstrate the effectiveness and speed of the method.

This article puts forward a novel smooth rotated hyperbola model for support vector machine (RHSSVM) for classification. As is well known, the Support vector machine (SVM) is based on Statistical Learning Theory and performs its high precision on data classification. However, the objective function is non-differentiable at the zero point. Therefore the fast algorithms cannot be used to train and test the SVM. To deal with it, the proposed method is based on the approximation property of the hyperbola to its asymptotic lines. Firstly, we describe the development of RHSSVM from the basic linear SVM optimization programming. Then we extend the linear model to non-linear model. We prove the solution of RHSSVM is convergent, unique, and global optimal. We show how RHSSVM can be practically implemented. At last, the theoretical analysis illustrates that compared with other three typical models, the rotated hyperbola model has the least error on approximating the plus function. Meanwhile, computer simulations show that the RHSSVM can reduce the consuming time at most 54.6% and can efficiently handle large scale and high dimensional programming.

From the origin of ancient Greek philosophy to the philosophy of medieval ages, although it appeared the discussion of "nominalism" and "realism" in medieval times, the exploration of the concept of "objective but non-real" did not get further developed. From the view of the inherent integration of the unity of general rationality on science and philosophy, professor Wu Kun revived the concept of "objective but non-reality" and creatively developed his "philosophy of information" system. Because the existence of "objective but non-reality" is inherently a kind of "crossover" field in the traditional philosophy, it certainty solve the problems of traditional philosophy from the ontology, which will lead to the breakthrough in the fundamental paradigm in philosophy, and the philosophy begin its fundamental turn.

A smooth rotated hyperbola model for support vector machine (SVM) is proposed. The method is based on the approximation property of the hyperbola to its asymptotic lines. The rotated hyperbola model has the least error on approximating the plus function when the angle between the two asymptotic lines is 135 degree. Experimental result shows that compared with other smooth methods, the rotated hyperbola function support vector machine (RHSSVM) reduces the compute time and can efficiently handle large scale and high dimensional programs.

From the origin of ancient Greek philosophy to the philosophy of medieval ages, although it appeared the discussion of "nominalism" and "realism" in medieval times, the exploration of the concept of "objective but non-real" did not get further developed. From the view of the inherent integration of the unity of general rationality on science and philosophy, professor Wu Kun revived the concept of "objective but non-reality" and creatively developed his "philosophy of information" system. Because the existence of "objective but non-reality" is inherently a kind of "crossover" field in the traditional philosophy, it certainty solve the problems of traditional philosophy from the ontology, which will lead to the breakthrough in the fundamental paradigm in philosophy, and the philosophy begin its fundamental turn.

A new three-order piecewise function was used to smoothen the model of Support Vector Machine( SVM) and a Third-order Piecewise Smooth SVM( TPSSVM) was proposed. By theory analyzing, approximation accuracy of the smooth function to the plus function is higher than that of the available. When dealing with the multi-class problem, a coding method of multi-class classification based on one-against-rest was proposed. Principal Component Analysis( PCA) was employed to extract the main features of face image set, and multi-class classification of smooth SVM was used for face recognition. The experimental results on ORL and FERET face databases show that the recognition rate of smooth SVM for multi-class classification is better than the traditional identification methods.

The spatialization mechanism of time is one of the important ways to explore the essence of time. The theory of cognitive linguistics holds that metaphor and metonymy are two ways of spatialization of time concept. However, from the perspective of Information Philosophy, the above research only stays at the level of regenerative temporal and spatial information(concept), and does not trace back to the source of objective ontology to explain the spatialization process of time. According to the ontology theory of Information Philosophy, information can be divided into three different forms and the concept is just the third form of information. Thus, we can analyze the spatialization process of time under the objective time and space, in-itself, for-self and regenerative space-time information form, revealing the inevitability spatialization of human temporal cognitive. This informational perspective shows the ontological source of the human’s perception of "past, present and future" and deepens the study of the essence of time.