A Survey on Cellular Automata and Its Applications
09 Dec 2011-pp 753-762
TL;DR: The literature survey introduces the different type of cellular automata being used for modeling and also tries to present the different fields in which CA have been applied.
Abstract: A Cellular Automata (CA) is a computing model of complex system using simple rule. CA divides the problem space into number of cell and each cell can be one or several final state. Cells are affected by neighbors to the simple rule. This paper provides a survey of available literature of some methodologies employed by researchers to utilize the cellular automata for modeling purpose. The literature survey introduces the different type of cellular automata being used for modeling and also tries to present the different fields in which CA have been applied.
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01 Oct 2021
TL;DR: This work integrates whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model and demonstrates feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.
Abstract: Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.
19 citations
Posted Content•
TL;DR: A survey of available literatures of some methodologies employed by different researchers to utilize the cellular automata for solving some important problems of image processing is presented in this paper, which includes some important image processing tasks such as rotation, zooming, translation, segmentation, edge detection, compression and noise reduction.
Abstract: Parallel algorithms for solving any image processing task is a highly demanded approach in the modern world. Cellular Automata (CA) are the most common and simple models of parallel computation. So, CA has been successfully used in the domain of image processing for the last couple of years. This paper provides a survey of available literatures of some methodologies employed by different researchers to utilize the cellular automata for solving some important problems of image processing. The survey includes some important image processing tasks such as rotation, zooming, translation, segmentation, edge detection, compression and noise reduction of images. Finally, the experimental results of some methodologies are presented.
17 citations
25 Sep 2014
TL;DR: A survey of available literatures of some methodologies employed by different researchers to utilize the cellular automata for solving some important problems of image processing.
Abstract: algorithms for solving any image processing task is a highly demanded approach in the modern world. Cellular Automata (CA) are the most common and simple models of parallel computation. So, CA has been successfully used in the domain of image processing for the last couple of years. This paper provides a survey of available literatures of some methodologies employed by different researchers to utilize the cellular automata for solving some important problems of image processing. The survey includes some important image processing tasks such as rotation, zooming, translation, segmentation, edge detection, compression and noise reduction of images. Finally, the experimental results of some methodologies are presented.
16 citations
Cites background from "A Survey on Cellular Automata and I..."
...Fig 1: State transition depend on neighborhood state [8]...
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...79 The state of each cell is updated simultaneously at discrete time steps based on the states in its neighborhood at the preceding time step [8]....
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Dissertation•
29 Aug 2018
TL;DR: Simulation techniques based on geography analysis are described to compute long-range radio coverages and radio characteristics in these situations and it is shown how a unified approach also allows to characterize the behavior of potential physical risks.
Abstract: Long-range radio transmissions open new sensor application fields, in particular for environment monitoring. For example, the LoRa radio protocol enables to connect remote sensors at distance as long as ten kilometers in a line-of-sight. However, the large area covered also brings several difficulties, such as the placement of sensing devices in regard to topology in geography, or the variability of communication latency. Sensing the environment also carries constraints related to the inlerest of sensing points in relation with a physical phenomenon. Thus criteria for designs are evolving a lot from the existing methods, especially in complex terrains. This thesis describes simulation techniques based on geography analysis to compute long-range radio coverages and radio characteristics in these situations. As radio propagation is just a particular case of physical phenomena, it is shown how a unified approach also allows to characterize the behavior of potential physical risks. The case of heavy rainfall and flooding is investigated. Geography analysis is achieved using segmentation tools to produce cellular systems which are in turn translated into code for high-þerformance computations. The thesis provides results from practical complex terrain experiments using LoRa which confirm the accuracy of the simulation, and scheduling characteristics for sample networks. Performance tables are produced for these simulations on current Graphics Processing Units (GPUs).
7 citations
Cites background from "A Survey on Cellular Automata and I..."
...5 – The broad application fields of cellular automata [40, 41]....
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TL;DR: In this paper, the authors present two interesting properties of stochastic cellular automata that can be helpful in analyzing the dynamical behavior of such automata, i.e. images that show the average state of each cell during the evolution of the automata.
7 citations
References
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TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Abstract: Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
16,652 citations
Book•
01 Jan 1982
TL;DR: The winning ways for mathematical games as mentioned in this paper have become the definitive work on the subject of mathematical games, and the Second Edition retains the original's wealth of wit and wisdom, blended with their witty and irreverent style, make reading a profitable pleasure.
Abstract: In the quarter of a century since three mathematicians and game theorists collaborated to create Winning Ways for Your Mathematical Plays, the book has become the definitive work on the subject of mathematical games. Now carefully revised and broken down into four volumes to accommodate new developments, the Second Edition retains the original's wealth of wit and wisdom. The authors' insightful strategies, blended with their witty and irreverent style, make reading a profitable pleasure. In Volume 3, the authors examine Games played in Clubs, giving case studies for coin and paper-and-pencil games, such as Dots-and-Boxes and Nimstring. From the Table of Contents: - Turn and Turn About - Chips and Strips - Dots-and-Boxes - Spots and Sprouts - The Emperor and His Money - The King and the Consumer - Fox and Geese; Hare and Hounds - Lines and Squares
1,238 citations
04 Dec 2011
TL;DR: The notion of Built-in Tamper Resilience (BiTR) was introduced in this paper for cryptographic protocols, capturing the idea that the protocol that is encapsulated in a hardware token is designed in such a way so that tampering gives no advantage to an adversary.
Abstract: The assumption of the availability of tamper-proof hardware tokens has been used extensively in the design of cryptographic primitives. For example, Katz (Eurocrypt 2007) suggests them as an alternative to other setup assumptions, towards achieving general UC-secure multi-party computation. On the other hand, a lot of recent research has focused on protecting security of various cryptographic primitives against physical attacks such as leakage and tampering.
In this paper we put forward the notion of Built-in Tamper Resilience (BiTR) for cryptographic protocols, capturing the idea that the protocol that is encapsulated in a hardware token is designed in such a way so that tampering gives no advantage to an adversary. Our definition is within the UC model, and can be viewed as unifying and extending several prior related works. We provide a composition theorem for BiTR security of protocols, impossibility results, as well as several BiTR constructions for specific cryptographic protocols or tampering function classes. In particular, we achieve general UC-secure computation based on a hardware token that may be susceptible to affine tampering attacks. We also prove that two existing identification and signature schemes (by Schnorr and Okamoto, respecitively) are already BiTR against affine attacks (without requiring any modification or endcoding). We next observe that non-malleable codes can be used as state encodings to achieve the BiTR property, and show new positive results for deterministic non-malleable encodings for various classes of tampering functions.
664 citations