Composing Fine-Grained Parallel Algorithms for Spatial Dynamics Simulation

Artificial genetic circuits have been recently proposed to realize unprecedented biological systems which do not exist in nature by combining several genes. For example, recombinant Escherichia coli provides novel artificial functions such as blinking periodically or invading cancer cells. In protein engineering, new functional proteins have been created by site-directed mutagenesis methods. The main purpose of our research is to implement the Cellular Automata (CA) in vivo employing these methods. Computational capacity of CA is equivalent to the universal Turing Machine. CA are multi-cellular systems in which uniform cells are allocated on lattice grid. Each cell has a finite number of states. The state transition is determined by the current state and the states of neighbor cells. Three mechanisms are required to implement CA in bacteria: (1) Sending and receiving signals between cells, (2) Sustaining the state, (3) Sensing input signals and changing the state following the state transition rules. To encode signals for the mechanism (1), we use small molecules inducing transcription. For the mechanism (2), the toggle switch circuit was employed to represent a finite number of states using gene expression. If one gene does express and the other does not, the state is 1, and vice versa, 0. For the mechanism (3), the state transition functions were implemented as logic gates using transcriptional regulatory proteins which bind to specific signal molecules (chemicals). In this paper, we applied this method to in vivo implementation of one-dimensional CA and corroborated whether it behaves correctly. In principle, this method can be applied to any dimensional CA by employing more signal molecules Permission to make digital or hard copies of all or part of this work for to construct more complex logic gate. Furthermore, we succeeded to execute one step calculation of one-dimensional CA in vivo in a laboratory experiment.

It is shown how to construct a general linear hybrid cellular automaton (CA) such that it has a maximum length cycle, and how the aliasing properties of such automata compare with linear feedback shift registers (LFSRs) when used as signature analyzers. The construction is accomplished by formally demonstrating the isomorphism which binds this kind of CA to the LFSRs. Consequently, these CAs can be analyzed as linear machines. Linear algebraic techniques are then applied appropriately for the transformations, and a useful search algorithm is developed which, given an irreducible characteristic polynomial, finds a corresponding linear hybrid automaton. Such CAs are tabulated for all irreducible and primitive polynomials up to degree 16, plus a selection of others of higher degree. The behavior of a linear hybrid CA and that of its corresponding LFSR are similar-that is, they have the same cycle structure and only relabel the states. The aliasing properties, when they are used as signature analyzers, remain unchanged.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In Section 7.1, we introduce the notion of a cellular (or tessellation) automaton, and show how to embed CT-machines within the cellular array. These have the computational power of Turing machines (thus the T) as well as the ability to Construct other CT-machines (thus the C). We then prove results about universal constructors and self-reproducing machines. In Section 7.2, we further formalize the notion of cellular automata, and prove that there exist Garden-of-Eden configurations that cannot arise in the cellular array without intervention of an external agency. Finally, Section 7.3 discusses differences between the type of self-reproduction formalized here and the growth processes of embryology.

A basic property of one-dimensional surjective cellular automata (CA) is that any preimage of a spatially periodic configuration (SPC) is spatially periodic as well. This paper investigates the relationship between the periods of SPC and the periods of their preimages for various classes of CA. When the CA is only surjective and y is a SPC of least period p, the least periods of all preimages of y are multiples of p. By leveraging on the De Bruijn graph representation of CA, we devise a general algorithm to compute the least periods appearing in the preimages of a SPC, along with their corresponding multiplicities (i.e. how many preimages have a particular least period). Next, we consider the case of linear and bipermutive cellular automata (LBCA) defined over a finite field as state alphabet. In particular, we show an equivalence between preimages of LBCA and concatenated linear recurring sequences (LRS) that allows us to give a complete characterization of their periods. Finally, we generalize these results to LBCA defined over a finite ring as alphabet.

From classical infinite space–time CA to a hybrid CA model for natural sciences modeling

Webly-supervised fine-grained visual classification (FGVC) has attracted increasing attention in recent years because of the unaffordable cost of obtaining correctly-labeled large-scale fine-grained datasets. However, due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images tends to have an inferior recognition ability. In the literature, to alleviate this issue, loss correction methods try to estimate the noise transition matrix, but the inevitable false correction would cause accumulated errors. Sample selection methods identify clean (‘`easy’') samples based on the fact that small losses can alleviate the accumulated errors. However, ‘`hard’' and mislabeled examples that can both boost the robustness of FG models are also dropped. To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the network. Furthermore, in order to endow our model with the capability to capture richer and more discriminative feature representations, we propose a cross-layer attention-based feature refinement (CLAR) block. We demonstrate the superiority of the proposed approach from both theoretical and experimental perspectives. The source code, models, and data have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/CLAR-CRSSC.

Cellular Automata (CA) as bio-inspired parallel computational models of self-reproducing organisms can capture the essential features of systems where global behavior arises from the collective effect of simple components which interact locally. In this aspect, CAs have been considered as a fine candidate to model pedestrian behavior and crowd dynamics in a fine manner. In specific, for crowd modeling, the CA models show evidence of a macroscopic nature with microscopic extensions, i.e. they provide adequate details in the description of human behavior and interaction, whilst they retain the computational cost at low levels. In this paper several CA models for crowd evacuation taking into consideration different modeling principles, like potential fields techniques, obstacle avoidance, follow-the-leader principles, grouping theory, etc. will be presented in an attempt to accomplish efficient crowd evacuation simulation. Moreover, an integrated system based on CAs that operates as an anticipative crowd management tool in cases of medium density crowd evacuation for indoor and outdoor environments is also shown, and its results different real world cases and different environments prove its efficiency. Finally, robot guided evacuation with the help of CAs is also presented. Quite recently, an evacuation system was proposed, based on an accurate CA model capable of assessing the human behavior during emergency situations takes advantage of the simulation output to provide sufficient information to a mobile robotic guide, which in turn guides people towards a less congestive exit at a time.

We investigate chaoticity and complexity of a binary general network automata of finite size with external input which we call a computron. As a generalization of cellular automata, computrons can have non-uniform cell rules, non-regular cell connectivity and an external input. We show that any finite-state machine can be represented as a computron and develop two novel set-theoretic concepts: (i) diversity space as a metric space that captures similarity of configurations on a given graph and (ii) basin complexity as a measure of complexity of partitions of the diversity space. We use these concepts to quantify chaoticity of computrons’ dynamics and the complexity of their basins of attraction. The theory is then extended into probabilistic machines where we define fuzzy basin partitioning of recurrent classes and introduce the concept of ergodic decomposition. A case study on 1D cyclic computron is provided with both deterministic and probabilistic versions.

Cellular automata (CA) are fully parallel computational models and are widely applied to numerical modelling for many complex systems or nonlinear systems, such as fluid dynamics. Those systems are often governed by nonlinear partial differential equations which are hard to solve by using traditional numerical methods. In this paper, based on CA, a general model for a kind of evolutionary physics systems is proposed. As an example, a CA-like model for nonlinear parabolic equation is built by using multi-scalar analysis. The model is applied to several typical problems and satisfactory results are achieved.

Numerical simulation of the debris-flow process is commonly based on the shallow water equations. However, as a two-phase anisotropic mixture, debris flows display complex rheological behavior, making it difficult to model or to simulate these using standard approaches. In this paper, an improved cellular automaton (CA) model is developed for simulating the extent of debris-flow run-out. The CA model consists of three essential components: cellular space, lattice relation, and transition function. A two-dimensional rectangular cellular space is generated from mesh grid in the digital terrain model data, and the Moore neighborhood type is selected as the lattice relation. We also use a transition function based on a Monte Carlo iteration algorithm to automatically search the flow direction and flow routine. Specifically, this new transition function combines the topography function and persistence function (due to the flow inertia), and is advanced in its ability to avoid certain illogical lateral spreading due to abrupt changes in topography. In addition, in contrast to previous studies, in the present work, we regressed the persistence function from a well-documented flume experiment, rather than using a manipulated constant value as described in earlier empirical studies. Our results show that the debris-flow persistence function is closely related to the channel slope. It approximates the law of cosines at a steep slope and Gamma law at a gentle slope. To illustrate the performance of the improved CA model, we selected the 2010 Yohutagawa debris-flow event in Japan as a case study. Our results show that the simulated deposition perimeter pattern and run-out distance are in high accordance with the data from in situ investigation.

We present a new approach to execution of global image processing operations on massively parallel cellular processor arrays. Combining conventional synchronous processing with simple asynchronous propagations we achieve performance increase on global operations without additional hardware cost. By the example of watershed transformation we demonstrate the benefits of mixed synchronous/ asynchronous approach. In addition, we investigate asynchronous grey-scale data processing and its applications in image processing.

Emergence of self-replicating structures in a cellular automata space

Enhancing the Accuracy of Forecasting Impact of Accidents in Chemical Process Industry by the Application of Cellular Automata Technique

This paper mainly focuses on protecting healthcare data by Cellular Automata (CA) based encryption techniques. Cellular Automata is called a finite state machine where each cell has a transition function. Here the operations are done over 8 cell Programmable Cellular Automata (PCA), where elementary CA rules are passed as the key for strengthening the cryptosystem. The focus of the cryptosystem is to restrict access to the malicious, unauthorized users.

Evolution-in-Materio (EIM) is a method of using artificial evolution to exploit physical properties of materials for computation. It has previously been successfully used to evolve a multitude of different computational devices implemented in physical materials. One of the main problems has been the stability of solutions due to physical changes in the substrate materials, e.g. topological or electrical, leading to devices sensitive to the evolutionary history, i.e. instability. This paper presents stable linearly and non-linearly separable logic gates, and elementary cellular automata (CA) transition functions, successfully evolved in single-walled carbon nanotube (SWCNT) and polymer composite materials. The results herein show the feasibility of building stand-alone in-materio devices and outline a novel computational abstraction for EIM based on CA. This work is done within the European Project NASCENCE.