Secret Communication Systems Using Chaotic Wave Equations with Neural Network Boundary Conditions.

In a secret communication system using chaotic synchronization, the communication information is embedded in a signal that behaves as chaos and is sent to the receiver to retrieve the information. In a previous study, a chaotic synchronous system was developed by integrating the wave equation with the van der Pol boundary condition, of which the number of the parameters are only three, which is not enough for security. In this study, we replace the nonlinear boundary condition with an artificial neural network, thereby making the transmitted information difficult to leak. The neural network is divided into two parts; the first half is used as the left boundary condition of the wave equation and the second half is used as that on the right boundary, thus replacing the original nonlinear boundary condition. We also show the results for both monochrome and color images and evaluate the security performance. In particular, it is shown that the encrypted images are almost identical regardless of the input images. The learning performance of the neural network is also investigated. The calculated Lyapunov exponent shows that the learned neural network causes some chaotic vibration effect. The information in the original image is completely invisible when viewed through the image obtained after being concealed by the proposed system. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method.

The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations.

The combination of neural networks and numerical integration can provide highly accurate models of continuous-time dynamical systems and probabilistic distributions. However, if a neural network is used [Formula: see text] times during numerical integration, the whole computation graph can be considered as a network [Formula: see text] times deeper than the original. The backpropagation algorithm consumes memory in proportion to the number of uses times of the network size, causing practical difficulties. This is true even if a checkpointing scheme divides the computation graph into subgraphs. Alternatively, the adjoint method obtains a gradient by a numerical integration backward in time; although this method consumes memory only for single-network use, the computational cost of suppressing numerical errors is high. The symplectic adjoint method proposed in this study, an adjoint method solved by a symplectic integrator, obtains the exact gradient (up to rounding error) with memory proportional to the number of uses plus the network size. The theoretical analysis shows that it consumes much less memory than the naive backpropagation algorithm and checkpointing schemes. The experiments verify the theory, and they also demonstrate that the symplectic adjoint method is faster than the adjoint method and is more robust to rounding errors.

Measurement and visualization of face-to-face interaction among community-dwelling older adults using wearable sensors.

AIM: A number of interventions have been undertaken to develop and promote social networks among community-dwelling older adults. However, it has been difficult to examine the effects of these interventions, because of problems in assessing interactions. The present study was designed to quantitatively measure and visualize face-to-face interactions among elderly participants in an exercise program. We also examined relationships among interactional variables, personality and interest in community involvement, including interactions with the local community. METHODS: Older adults living in the same community were recruited to participate in an exercise program that consisted of four sessions. We collected data on face-to-face interactions of the participants by using a wearable sensor technology device. RESULTS: Network analysis identified the communication networks of participants in the exercise program, as well as changes in these networks. Additionally, there were significant correlations between the number of people involved in face-to-face interactions and changes in both interest in community involvement and interactions with local community residents, as well as personality traits, including agreeableness. CONCLUSIONS: Social networks in the community are essential for solving problems caused by the aging society. We showed the possible applications of face-to-face interactional data for identifying core participants having many interactions, and isolated participants having only a few interactions within the community. Such data would be useful for carrying out efficient interventions for increasing participants' involvement with their community. Geriatr Gerontol Int 2017; 17: 1752-1758.