In this paper, some classes of arithmetic circuits are introduced that capture the computational complexity of computing the determinant of matrices with entries either indeterminates or constants from a field. An arithmetic circuit is just like a Boolean circuit, except that all AND and OR gates (with fan-in two) are replaced by gates realizing a multiplication and an addition, respectively, of two polynomials over some indeterminates with coefficients from the field, and the circuit computes a (formal multivariate) polynomial in the obvious sense. An arithmetic circuit is said to be skew if at least one of the inputs of each multiplication gate is either an indeterminate or a constant. Then it is shown that for all square matrices M of dimension q, the determinant of M can be computed by a skew arithmetic circuit of (q20) gates, and is shown that for all skew arithmetic circuits C of size q, the polynomial computed by C can be defined as the determinant of a square matrix M of dimension (q). Thus the size of skew arithmetic circuit is polynomially related to the dimension of square matrices when it is considered to represent multivariate polynomials in both arithmetic circuits and the determinant. The results are extended to some other classes of arithmetic circuits less restricted than skew ones, and by using such an extended result, a difference and a similarity are demonstrated between polynomials represented as the determinant of matrix of relatively small dimension and those polynomials computed by arithmetic formulas and arithmetic circuits of relatively small size and degree.

This paper presents a new connected associative memory neural network. In this network, a threshold function which has two dynamical parameters is introduced. After analyzing the dynamical behaviors and giving an upper bound of the memory capacity of the conventional connected associative memory neural network, it is demonstrated that these parameters play an important role in the recalling processes of the connected neural network. An approximate method of evaluationg their optimum values is given. Further, the optimum feedback stopping time of this network is discussed. Therefore, in our network, the recalling processes are ended at the optimum feedback stopping time whether a state energy has been local minimum or not. The simulations on computer show that the dynamical behaviors of our network are greatly improved. Even though the number of learned patterns is so large as the number of neurons, the statistical properties of the dynamical behaviors of our network are that the output series of recalling processes approach to the expected patterns on their initial inputs.

This paper proposes a knowledge-based design method of a protocol of a communication network system based on the knowledge-based design methodology for computer communication systems. In the proposed method, two knowledge models, i.e., the communication network architecture model (CNAM) and the communication protocol architecture model (CPAM), are introduced and a protocol design task is modeled as a successive transformation process of these knowledge models. Giving CNAM which represents the users' requirements concerning a communication network system, the requirements specification of a protocol is derived from CNAM and represented as CPAM. Then, the detailed requirements specification of a protocol is also derived from CPAM and represented by the formal description technique (FDT-Expressions). The derivations of CPAM and FDT-Expressions are executed by the transformation rules which represent the mappings between knowledge models. Due to formally defined knowledge models and mappings, the proposed method provides a framework of a systematic support of knowledge-based protocol design. In this paper, the formal definitions of CNAM and CPAM are given, then the derivation process of FDT-Expressions of a protocol is also formalized based on these knowledge models. Furthermore, a design example is demonstrated by using LOTOS as one of the FDT-Expressions of a protocol.

This paper discusses new trends and directions in human interface (HI) technologies, and the effects of HI technologies on human life or on social activities. This paper postulates that the HI subsumes man-machine interface, human-computer interaction, human-human interaction, human-organizational interface, human-environmental interface, human-social interface, etc. A new communication model, called Human Interface Communication Model (HICOM), and a new human dialogue model, called Human Interface Dialogue model (HIDIM), are derived by reexamining trends and directions on HI technologies from the viewpoint of functional meanings of interfaces, and from the viewpoint of a socially distributed cognition mechanism.