Pattern formation in Passiflora incarnata: An activator-inhibitor model

Based on a careful examination of the onset of violet colored dots along the filaments in the developing floral bud stage and the formation of alternating bands of violet and white color in the matured flowers of Passiflora incarnata (Passion flower), it is concluded that the pattern arises from a competition between the production of violet colored anthocyanin and the colorless flavonols along the filaments. The activator-inhibitor model of Gierer and Meinhardt along with the reaction diffusion theory of Turing is used to explain the formation of concentric rings in the flower

Secure and energy-efficient smart building architecture with emerging technology IoT

With the advent of the Internet-of-Things (IoT), it is considered to be one of the latest innovations that offer interesting opportunities for different vertical industries. One of the most relevant IoT technology areas is smart construction. IoT operates in several sectors on a daily basis; implementation includes smart building, smart grids, smart cities, smart houses, physical defense, e-health, asset, and transportation management, but it is not restricted to this. Support from smart IoT buildings is an IoT-level, connected, and cost-effective system. Commercial space has major requirements in terms of comfort, accessibility, security, and energy management. Such requirements can be served organically by IoT-based systems. As the supply of energy has been exhausted and energy demand has risen, there has been a growing focus on energy usage and the maintenance of buildings.With the use of evolving IoT technology, we present a secure and energy-efficient smart building architecture.Every device is known by its unique address, and one of the key web transfer protocols is the Constrained Application Protocol (CoAP). It’s an application layer protocol that doesn’t use protected channels for data transfer. Automatic key management, confidentiality, authentication, and data integrity are all features of the Datagram Transport Layer Protection (DTLS).To achieve energy efficiency, we propose a smart construction architecture that, through IoT, manages the performance of all technological systems. The results of the simulation show that the energy consumption is lowered by about 30.86% with the use of the CoAP in the smart building, which is less than the Message Queuing Telemetry Transport case (MQTT). This paper also aims to observe how to integrate the DTLS protocol with the Secure Hash Algorithm (SHA-256) using optimizations from the Certificate Authority (CA) to improve security

Smoothed Analysis of Tensor Decompositions

Low rank tensor decompositions are a powerful tool for learning generative models, and uniqueness results give them a significant advantage over matrix decomposition methods. However, tensors pose significant algorithmic challenges and tensors analogs of much of the matrix algebra toolkit are unlikely to exist because of hardness results. Efficient decomposition in the overcomplete case (where rank exceeds dimension) is particularly challenging. We introduce a smoothed analysis model for studying these questions and develop an efficient algorithm for tensor decomposition in the highly overcomplete case (rank polynomial in the dimension). In this setting, we show that our algorithm is robust to inverse polynomial error -- a crucial property for applications in learning since we are only allowed a polynomial number of samples. While algorithms are known for exact tensor decomposition in some overcomplete settings, our main contribution is in analyzing their stability in the framework of smoothed analysis. Our main technical contribution is to show that tensor products of perturbed vectors are linearly independent in a robust sense (i.e. the associated matrix has singular values that are at least an inverse polynomial). This key result paves the way for applying tensor methods to learning problems in the smoothed setting. In particular, we use it to obtain results for learning multi-view models and mixtures of axis-aligned Gaussians where there are many more "components" than dimensions. The assumption here is that the model is not adversarially chosen, formalized by a perturbation of model parameters. We believe this an appealing way to analyze realistic instances of learning problems, since this framework allows us to overcome many of the usual limitations of using tensor methods.Comment: 32 pages (including appendix