New Model Of Computation Research Articles

A Structure Independent Molecular Fragment Interfuse Model for Mesoscale Dissipative Particle Dynamics Simulation of Peptides.

There is a need to develop robust computational models for mesoscale simulation of the structure of peptides over large length scales toward the discovery of novel peptides for medical applications to address the issues of peptide aggregation, enzymatic degradation, and short half-life. The primary objective was to predict the structure and conformation of peptides whose native structures are not known. This work presents a new model for computation of interaction parameters between the beads in coarse-grained dissipative particle dynamics (DPD) simulation that is properly calibrated for amino acids, supports compressibility requirement of water molecules, and accounts for subtle differences in the structure of amino acids and the charge in the side chain of charged amino acids. This new model is referred to as Structure Independent Molecular Fragment Interfuse Model, abbreviated as SIMFIM, because it accounts for specific interactions between different beads, which represent molecular fragments of the amino acids, in calculating nonbonded interaction parameters in the absence of knowing the actual peptide structure. The electrostatic interactions are incorporated in this model by using a normal distribution of charges around the center of the beads to prevent the collapse of oppositely charged soft beads. The uniquely parameterized DPD force field in the SIMFIM model is optimized for a given peptide with respect to the degree of coarse-grained graining for simulating the peptide over long times and length scales. The SIMFIM model was tested in this work using four peptides, namely, TrpZip2, Rubrivinodin, Lihuanodin, and IC3-CB1/Gai peptides, whose structures were sourced from the Protein Data Bank. The SIMFIM model predicted radius of gyration (Rg) values for the peptides closer to the actual structures as compared to the conventional model, and there was less deviation between the predicted and actual structures of the peptides.

Watson–Crick Jumping Finite Automata: Combination, Comparison and Closure

Abstract A new model of computation called Watson–Crick jumping finite automata was introduced by Mahalingam et al., and the authors study the computing power and closure properties of the variants of the model. There are four variants of the model: no state, 1-limited, all-final and simple Watson–Crick jumping finite automata. In this paper, we introduce a restricted version that is a combination of variants of the existing model. It becomes essential to explore the computing power and closure properties of these combinations. The combination variants are extensively compared with Chomsky hierarchy, general jumping finite automata family and among themselves. We also explore the closure properties of such restricted automata.

Algorithms and Data Structures for New Models of Computation

In the early days of computer science, the community settled on a simple standard model of computing and a basic canon of general purpose algorithms and data structures suited to that model. With isochronous computing, heterogeneous multiprocessors, flash memory, energy-aware computing, cache and other anisotropic memory, distributed computing, streaming environments, functional languages, graphics coprocessors, etc., the basic canon of algorithms and data structures is not enough. Software developers know of real-world constraints and new models of computation and use them to design effective algorithms and data structures. These constraints motivate the development of elegant algorithms with broad utility. As examples, we present four algorithms that were motivated by specific hardware nuances, but are generally useful: reservoir sampling, majority of a stream, B-heap, and compacting an array in $\Theta (\log n)$Θ(logn) time.

Syntax and Semantics for Cinnamon Programming

Programming (CNP). CNP has established itself as a programming approach combining declarative and imperative features. It supports powerful tools for control of the computation process; in particular, these tools allow easy, intuitive, visual development of heuristic, nondeterministic, or randomized solutions. The paper providesrigorous definitions of the syntax and semantics of the new model of computation, at the same time trying to keep the intuition behind clear. The purposely simplified theoretical model is then compared to both WHILE-programs (thus demonstrating its Turing completeness), and the “real” CNP. Finally, future research possibilities are mentioned that would eventually extend the cinnamon programming and its theoretical foundation into the directions of nondeterminism, randomness and fuzziness.

The New Matrix Model of Computation Based Purely on Quite a New Concept of the Matrix Computations for Extremely Quick Web Pages Loading

We all dream of quick loading. Quicker than it is now. So that it loads immediately. What is needed for this? There are a lot of things to do. The most important things in it are computations. To speed up loading, we need to speed up computations. If we will find the way to multiply large numbers quicker than we have, the loading will be much quicker. How to do that? We need to multiply large numbers in time O(log n). How is that possible? A new model of computation may solve this problem. These are the algorithms that require considerably less amount of resources to perform them. The time complexity of the algorithm is the main (key) resource that we need to reduce to get the desired complexity. It seems incredible, but it is possible. We will get this through the sorting array. Best, worst, and average cases of a given algorithm could be considered for each particular input instance of the problem when analyzing algorithms. The worst-case complexity is the most used in algorithm analysis, it gives an upper bound on the resources required by the algorithm. Thus, the discovery of better algorithms brings the upper bound on the worst-case running time down. This paper presents the new matrix model of computation, which is based on the concept of the new matrix computations for advanced computing. The paper intends to prove the existence of better algorithms for any given input instance of the worst-case time complexity M(n) = O(n2) that take O(log n) and provide extremely quick web pages loading and create a new topic in complexity.

MODELS OF COMPUTATION FOR DIGITAL TWINS DATA PROCESSING

Background. The digital twin is a virtual model of a physical object or process (physical twin) which fully reflects its characteristics in dynamics over a period of time. The concept of digital twin involves the representation, processing, manipulation of multimodal data which characterizes the physical twin. The digital twin data processing requires new models of computation in order to simplify synchronization and aggregation of multimodal data for both behaviour and appearance models of the digital twin.Objective. The purpose of the research presented in this paper is to develop models of computation for digital twins data processing as well as to fulfil practical implementation of multimodal data synchronization and aggregation for e-health applications.Methods. The basic computation model is based on the concept of a multi-image which is considered as a data mathematical model. The notion of a multi-image is defined in the algebraic system of aggregates. In the basic computation model the multi-image is a key abstraction, which is defined at two levels: at the level of input data formation and at the level of formation of multimodal data structure to be processed. The advanced computation model for digital twins includes three stages of synchronization and aggregation, a stage of data processing of both the behavioural model and the appearance model according to the specific task of studying the real-world object (physical twin), and a stage of reproducing the digital twin.Results. The proposed computation schemes make it easy to synchronize and aggregate heterogeneous data entering into a computer system from multiple sources. The results obtained in the experimental part on this research allow concluding that the computational complexity of multimodal data processing is reduced in comparison with the traditional approach.Conclusions. Two computation models are proposed for digital twin data processing: a basic computation model and a computation advanced model for digital twins. The key component of these models is multimodal data synchronization and aggregation procedure. A mathematical apparatus of the algebraic system of aggregates is used in the presented research to simplify synchronization and aggregation of multimodal data defined in time.

Processing Information in the Clouds

Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.

Аналіз алгоритмів планування ресурсами в розподіленому обчислювальному середовищі

The article analyzes resource scheduling algorithms in a distributed computing environment. The main task that distributed computing technologies solve is providing access to globally distributed resources using special tools. The complexity of managing global resources is due to the fact that access to the necessary data can occur on different computers. In addition, global distributed computing networks formed from autonomous resources can change their configuration dynamically. Resource management in heterogeneous distributed computing systems requires the search for new models of computation and resource management. Analysis of recent research and publications has shown that currently the implementation of resource planning in a distributed computing environment requires the search for new approaches and new algorithms. Most of the work on resource planning methods in a distributed computing environment is used to solve specific tasks related to specific applications and therefore cannot be universal. The aim of the work is to analyze resource planning algorithms in a distributed computing environment in order to find methods and algorithms for resource management in a problem-oriented distributed environment, taking into account the specifics of individual tasks and use the possibility of parallel execution of different tasks. The analysis of resource planning algorithms in a distributed computing environment shows that to date, a large number of planning algorithms focused on use in a distributed computing environment. But often such algorithms do not take into account the problem-oriented specifics of the environment, and this affects the efficiency of planning. In this regard, a promising area is related to the development of resource planning algorithms in a distributed computing environment, which would create an efficient and effective resource planning system.

(Invited) Ferroelectric Devices for Alternative, Non-Von Neumann Computing

In the post-Moore era of electronics, significant interests are developing around new models of computation that overcome the bottlenecks of traditional von Neumann architectures, and draw features from the neuro-scientific principles of biological brains. In physically implementing those alternative kernels, emerging nanoelectronic materials and devices show great promise in terms of power efficiency and compactness due to their interesting features (such as hysteresis, non-volatility, plasticity, stochasticity, polymorphism)--features that are not readily available in CMOS platforms. The ferroelectric field-effect transistor (FeFET) is such an emerging device where a ferroelectric oxide is introduced into the gate oxide stack of a field-effect transistor. In this talk, we will present how the FeFET technology could be utilized for implementing non-von Neumann computing systems such as coupled neuro-oscillatory networks and spiking neural networks. The talks will include recent experimental results in tandem with theoretical calculations and simulations.

Oblivious Network RAM and Leveraging Parallelism to Achieve Obliviousness

Oblivious RAM (ORAM) is a cryptographic primitive that allows a trusted CPU to securely access untrusted memory, such that the access patterns reveal nothing about sensitive data. ORAM is known to have broad applications in secure processor design and secure multiparty computation for big data. Unfortunately, due to a logarithmic lower bound by Goldreich and Ostrovsky (J ACM 43(3):431–473, 1996), ORAM is bound to incur a moderate cost in practice. In particular, with the latest developments in ORAM constructions, we are quickly approaching this limit, and the room for performance improvement is small. In this paper, we consider new models of computation in which the cost of obliviousness can be fundamentally reduced in comparison with the standard ORAM model. We propose the oblivious network RAM model of computation, where a CPU communicates with multiple memory banks, such that the adversary observes only which bank the CPU is communicating with, but not the address offset within each memory bank. In other words, obliviousness within each bank comes for free—either because the architecture prevents a malicious party from observing the address accessed within a bank, or because another solution is used to obfuscate memory accesses within each bank—and hence we only need to obfuscate communication patterns between the CPU and the memory banks. We present new constructions for obliviously simulating general or parallel programs in the network RAM model. We describe applications of our new model in distributed storage applications with a network adversary.

Developing Tools for Networks of Processors

A great deal of research eort is currently being made in the realm of so called natural computing. Natural computing mainly focuses on the denition, formal description, analysis, simulation and programming of new models of computation (usually with the same expressive power as Turing Machines) inspired by Nature, which makes them particularly suitable for the simulation of complex systems.Some of the best known natural computers are Lindenmayer systems (Lsystems, a kind of grammar with parallel derivation), cellular automata, DNA computing, genetic and evolutionary algorithms, multi agent systems, arti- cial neural networks, P-systems (computation inspired by membranes) and NEPs (or networks of evolutionary processors). This chapter is devoted to this last model.

Kickstarting Reconnection: An Approach to Legal Problems Arising from Emerging Technologies

A new model, or ‘third wave’, of computing is emerging, based on the widespread use of processors with data handling and communications capabilities embedded in a variety of objects and environments that were not previously computerised. Various terms have been used to describe this third wave, including ‘ubiquitous’ and ‘pervasive’ computing, ‘ambient intelligence’, the ‘Internet of Things’ and ‘eObjects’. With the socio-technical change brought about by this third wave comes the possibility of a disconnection between the law and the new things, activities, and relationships enabled by this new model of computing. This disconnection may lead to legal problems of uncertainty, under- or over-inclusiveness of conduct in existing law, obsolescence, or the complete absence of laws regulating new behaviour. Early and rigorous identification and categorisation of legal problems is crucial for emerging technologies, to assist in avoiding two problems: the first being the stifling of beneficial innovation by over-regulation, the second the cementing of socially undesirable outcomes when vested interests are left too long unchecked. Although the technologies in the third wave are diverse, common attributes can be identified, and from examination of these attributes significant innovations are revealed. This paper examines these innovations to assist in identifying legal problems arising from the third wave.

Syntax and Semantics for Cinnamon Programming

Programming (CNP). CNP has established itself as a programming approach combining declarative and imperative features. It supports powerful tools for control of the computation process; in particular, these tools allow easy, intuitive, visual development of heuristic, nondeterministic, or randomized solutions. The paper providesrigorous definitions of the syntax and semantics of the new model of computation, at the same time trying to keep the intuition behind clear. The purposely simplified theoretical model is then compared to both WHILE-programs (thus demonstrating its Turing completeness), and the “real” CNP. Finally, future research possibilities are mentioned that would eventually extend the cinnamon programming and its theoretical foundation into the directions of nondeterminism, randomness and fuzziness.

Fault and Error Tolerance in Neural Networks: A Review

Beyond energy, the growing number of defects in physical substrates is becoming another major constraint that affects the design of computing devices and systems. As the underlying semiconductor technologies are getting less and less reliable, the probability that some components of computing devices fail also increases, preventing designers from realizing the full potential benefits of on-chip exascale integration derived from near atomic scale feature dimensions. As the quest for performance confronts permanent and transient faults, device variation, and thermal issues, major breakthroughs in computing efficiency are expected to benefit from unconventional and new models of computation, such as brain-inspired computing. The challenge is then to find not only high-performance and energy-efficient, but also fault-tolerant computing solutions. Neural computing principles remain elusive, yet as source of a promising fault-tolerant computing paradigm. In the quest to fault tolerance can be translated into scalable and reliable computing systems, hardware design itself and/or to use circuits even with faults has further motivated research on neural networks, which are potentially capable of absorbing some degrees of vulnerability based on their natural properties. This paper presents a survey on fault tolerance in neural networks manly focusing on well-established passive techniques to exploit and improve, by design, such potential but limited intrinsic property in neural models, particularly for feedforward neural networks. First, fundamental concepts and background on fault tolerance are introduced. Then, we review fault types, models, and measures used to evaluate performance and provide a taxonomy of the main techniques to enhance the intrinsic properties of some neural models, based on the principles and mechanisms that they exploit to achieve fault tolerance passively. For completeness, we briefly review some representative works on active fault tolerance in neural networks. We present some key challenges that remain to be overcome and conclude with an outlook for this field.

Symmetry structure in discrete models of biochemical systems: natural subsystems and the weak control hierarchy in a new model of computation driven by interactions.

Interaction computing is inspired by the observation that cell metabolic/regulatory systems construct order dynamically, through constrained interactions between their components and based on a wide range of possible inputs and environmental conditions. The goals of this work are to (i) identify and understand mathematically the natural subsystems and hierarchical relations in natural systems enabling this and (ii) use the resulting insights to define a new model of computation based on interactions that is useful for both biology and computation. The dynamical characteristics of the cellular pathways studied in systems biology relate, mathematically, to the computational characteristics of automata derived from them, and their internal symmetry structures to computational power. Finite discrete automata models of biological systems such as the lac operon, the Krebs cycle and p53-mdm2 genetic regulation constructed from systems biology models have canonically associated algebraic structures (their transformation semigroups). These contain permutation groups (local substructures exhibiting symmetry) that correspond to 'pools of reversibility'. These natural subsystems are related to one another in a hierarchical manner by the notion of 'weak control'. We present natural subsystems arising from several biological examples and their weak control hierarchies in detail. Finite simple non-Abelian groups are found in biological examples and can be harnessed to realize finitary universal computation. This allows ensembles of cells to achieve any desired finitary computational transformation, depending on external inputs, via suitably constrained interactions. Based on this, interaction machines that grow and change their structure recursively are introduced and applied, providing a natural model of computation driven by interactions.

Dynamic Random-Access Stored-Program Machine for Runtime Code Modification

This paper is concerned with the study of possibility of performing changes to existing running programs with the use of the RAM and RASP models of computation. A new model of computation is defined with the capability of performing runtime changes. Theoretical properties, including time and space complexities, of the defined models are presented and proven. A number of simple empirical tests are conducted in order to prove the ability to perform runtime changes as well as support obtained theoretical results. The paper concludes that the defined model has virtually no affect on performance when there are no changes and the performance with changes is easily manageable. Moreover, the results can be used to develop runtime change capabilities for a wide range of programming languages and paradigms.

HINDERING DATA THEFT ATTACK THROUGH FOG COMPUTING

Fog computing is a paradigm that extends cloud computing which has become a reality that paved the way for new model of computing. Also fog provides application services to end terminal in the age of network. The inner data stealing attacks in which a user of a system illegitimately poses as the identity of another legitimate user which is an arising new challenge to the service provider where cloud service provider may not be able to protect the data. So to secure the real user’s sensitive information form the attacker in the cloud. We are proposing a completely distinct approach with the help of offensive decoy information technology, which is used for validating whether the data access is authorized where abnormal information is detected and thereby confusing the attacker with the bogus information.

Algorithm and proof as Ω-invariance and transfer: A new model of computation in nonstandard analysis

We propose a new model of computation based on nonstandard analysis. Intuitively, the role of "algorithm" is played by a new notion of finite procedure, called Omega-invariance and inspired by physics, from nonstandard analysis. Moreover, the role of 'proof' is taken up by the Transfer Principle from nonstandard analysis. We obtain a number of results in Constructive Reverse Mathematics to illustrate the tight correspondence to Errett Bishop's Constructive Analysis and the associated Constructive Reverse Mathematics.

English

Cloud computing is a phenomenon which bestows new model of computing. It has various service modes. The service model which provides on-demand access to computing resources is known as infrastructure-as- a-service (IaaS). The IaaS allows cloud users to access resources in pay per use fashion. The problem with this service model in the existing solutions is that the cloud provider may either overprovision the resources or not in a position to serve large number of requests. At any given time, all users might not need on-demand access. Keeping this in mind a strategy is required in order to optimize resource allocation. Many applications exists that take care of high-throughput computing (HTC) where more resources are expected to be allocated. For instance Condor is such application that assigns available resource to jobs and then the resources are released once job is completed. Recently Marshall et al. proposed cloud infrastructure that has provision for on-demand allocation of resources using backfill virtual machines. For this purpose Nimbus is used. In this paper we have implemented the concept of optimal resource allocation using the notion of HTC users and on-demand users. We built a prototype application that demonstrates the proof of concept. The empirical results are encouraging.

Monoidal computer I: Basic computability by string diagrams

We present a new model of computation, described in terms of monoidal categories. It conforms to the Church–Turing Thesis, and captures the same computable functions as the standard models. It provides a succinct categorical interface to most of them, free of their diverse implementation details, using the ideas and structures that in the meantime emerged from research in semantics of computation and programming. The salient feature of the language of monoidal categories is that it is supported by a sound and complete graphical formalism, string diagrams, which provide a concrete and intuitive interface for abstract reasoning about computation. The original motivation and the ultimate goal of this effort is to provide a convenient high level programming language for a theory of computational resources, such as one-way functions, and trapdoor functions, by adopting the methods for hiding the low level implementation details that emerged from practice.