As good as it gets: A scaling comparison of DNA computing, network biocomputing, and electronic computing approaches to an NP-complete problem

Abstract All known algorithms to solve Nondeterministic Polynomial (NP) Complete problems, relevant to many real-life applications, require the exploration of a space of potential solutions, which grows exponentially with the size of the problem. Since electronic computers can implement only limited parallelism, their use for solving NP-complete problems is impractical for very large instances, and consequently alternative massively parallel computing approaches were proposed to address this challenge. We present a scaling analysis of two such alternative computing approaches, DNA Computing (DNA-C) and Network Biocomputing with Agents (NB-C), compared with Electronic Computing (E-C). The Subset Sum Problem (SSP), a known NP-complete problem, was used as a computational benchmark, to compare the volume, the computing time, and the energy required for each type of computation, relative to the input size. Our analysis shows that the sequentiality of E-C translates in a very small volume compared to that required by DNA-C and NB-C, at the cost of the E-C computing time being outperformed first by DNA-C (linear run time), followed by NB-C. Finally, NB-C appears to be more energy-efficient than DNA-C for some types of input sets, while being less energy-efficient for others, with E-C being always an order of magnitude less energy efficient than DNA-C. This scaling study suggest that presently none of these computing approaches win, even theoretically, for all three key performance criteria, and that all require breakthroughs to overcome their limitations, with potential solutions including hybrid computing approaches.

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An Efficient Technique to Ensure the Logical Consistency of Interacting Knowledge Bases

International Journal of Cooperative Information Systems ◽

10.1142/s0218843097000045 ◽

1997 ◽

Vol 06 (01) ◽

pp. 27-36 ◽

Cited By ~ 7

Author(s):

Bertrand Mazure ◽

Lakhdar Saïs ◽

Éric Grégoire

Keyword(s):

Local Search ◽

Fundamental Problem ◽

Real Life ◽

Knowledge Bases ◽

The Other ◽

Logical Consistency ◽

Complete Problem ◽

Np Complete ◽

The One ◽

Cooperative Knowledge

In this paper, we address a fundamental problem in the formalization and implementation of cooperative knowledge bases: the difficulty of preserving consistency while interacting or combining them. Indeed, knowledge bases that are individually consistent can exhibit global inconsistency. This stumbling-block problem is an even more serious drawback when knowledge and reasoning are expressed using logical terms. Indeed, on the one hand, two contradictory pieces of information lead to global inconsistency under complete standard rules of deduction: every assertion and its contrary can be deduced. On the other hand, checking the logical consistency of a propositional knowledge base is an NP-complete problem and is often out of reach for large real-life applications. In this paper, a new practical technique to locate inconsistent interacting pieces of information is presented in the context of cooperative logical knowledge bases. Based on a recently discovered heuristic about the work performed by local search techniques, it can be applied in the context of large interacting knowledge bases.

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A New Algorithm to Solve the Maximal Connected Subgraph Problem Based on Parallel Molecular Computing

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.4276 ◽

2016 ◽

Vol 13 (10) ◽

pp. 7692-7695

Author(s):

Nan Guo ◽

Jun Pu ◽

Zhaocai Wang ◽

Dongmei Huang ◽

Lei Li ◽

...

Keyword(s):

Dna Computing ◽

Combinatorial Problems ◽

Search Problem ◽

Connected Subgraph ◽

Complete Problems ◽

Portfolio Problem ◽

Path Search ◽

Np Complete ◽

Connected Subgraphs ◽

Connected Vertex

DNA computing is widely used in complex NP-complete problems, such as the optimal portfolio problem, the optimum path search problem. DNA computing, having the characteristics of high parallelism, huge storage capacity and low energy loss, is very suitable for solving complex combinatorial problems. The maximal connected subgraph problem aims to find a connected vertex subset with maximal number of vertices in a simple undirected graph. Using biological computing technology, we give a new DNA algorithm to solve the maximal connected subgraphs problem with O(n) time complexity, which can significantly reduce the complexity of computing compared with the previous algorithms.

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Something has to give: scaling combinatorial computing by biological agents exploring physical networks encoding NP-complete problems

Interface Focus ◽

10.1098/rsfs.2018.0034 ◽

2018 ◽

Vol 8 (6) ◽

pp. 20180034 ◽

Cited By ~ 4

Author(s):

Falco C. M. J. M. van Delft ◽

Giulia Ipolitti ◽

Dan V. Nicolau ◽

Ayyappasamy Sudalaiyadum Perumal ◽

Ondřej Kašpar ◽

...

Keyword(s):

Computing Time ◽

Biological Agents ◽

Combinatorial Problems ◽

Complete Problems ◽

Input Size ◽

Depth Analysis ◽

Analytical Review ◽

Electronic Computers ◽

Np Complete ◽

On Chip

On-chip network-based computation, using biological agents, is a new hardware-embedded approach which attempts to find solutions to combinatorial problems, in principle, in a shorter time than the fast, but sequential electronic computers. This analytical review starts by describing the underlying mathematical principles, presents several types of combinatorial (including NP-complete) problems and shows current implementations of proof of principle developments. Taking the subset sum problem as example for in-depth analysis, the review presents various options of computing agents, and compares several possible operation ‘run modes’ of network-based computer systems. Given the brute force approach of network-based systems for solving a problem of input size C, 2 C solutions must be visited. As this exponentially increasing workload needs to be distributed in space, time, and per computing agent, this review identifies the scaling-related key technological challenges in terms of chip fabrication, readout reliability and energy efficiency. The estimated computing time of massively parallel or combinatorially operating biological agents is then compared to that of electronic computers. Among future developments which could considerably improve network-based computing, labelling agents ‘on the fly’ and the readout of their travel history at network exits could offer promising avenues for finding hardware-embedded solutions to combinatorial problems.

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Solving the 0/1 Knapsack Problem by a Biomolecular DNA Computer

Advances in Bioinformatics ◽

10.1155/2013/341419 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Hassan Taghipour ◽

Mahdi Rezaei ◽

Heydar Ali Esmaili

Keyword(s):

Parallel Processing ◽

Knapsack Problem ◽

Dna Computing ◽

Solution Space ◽

Combinatorial Problems ◽

Complete Problems ◽

Short Period ◽

Np Complete ◽

Silicon Based ◽

Dna Computers

Solving some mathematical problems such as NP-complete problems by conventional silicon-based computers is problematic and takes so long time. DNA computing is an alternative method of computing which uses DNA molecules for computing purposes. DNA computers have massive degrees of parallel processing capability. The massive parallel processing characteristic of DNA computers is of particular interest in solving NP-complete and hard combinatorial problems. NP-complete problems such as knapsack problem and other hard combinatorial problems can be easily solved by DNA computers in a very short period of time comparing to conventional silicon-based computers. Sticker-based DNA computing is one of the methods of DNA computing. In this paper, the sticker based DNA computing was used for solving the 0/1 knapsack problem. At first, a biomolecular solution space was constructed by using appropriate DNA memory complexes. Then, by the application of a sticker-based parallel algorithm using biological operations, knapsack problem was resolved in polynomial time.

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Experimental demonstration of quantum advantage for NP verification

10.21203/rs.3.rs-64904/v1 ◽

2020 ◽

Author(s):

Federico Centrone ◽

Niraj Kumar ◽

Eleni Diamanti ◽

Iordanis Kerenidis

Keyword(s):

Limited Information ◽

Experimental Demonstration ◽

Linear Optics ◽

Exponential Time ◽

Complete Problem ◽

Complete Problems ◽

Np Complete ◽

Age Of The Universe ◽

Classical Computer ◽

The Universe

Abstract We show the first experimental demonstration of a computational quantum advantage (also referred to as quantum supremacy) with linear optics, by studying the computational task of the verification of an NP-complete problem by a verifier who only gets limited information about the proof. We provide a simple linear optical implementation that can perform this task efficiently (within a few seconds), while we also provide strong evidence that a classical computer would take time greater than the age of the universe (assuming only that classically it takes exponential time to solve an NP-complete problem). The verification of NP-complete problems with limited information brings us a step closer to real-world useful applications, such as server-client quantum computing.

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Application of DNA Nanoparticle Conjugation on the Hamiltonian Path Problem

Journal of Nanoelectronics and Optoelectronics ◽

10.1166/jno.2021.2930 ◽

2021 ◽

Vol 16 (3) ◽

pp. 501-505

Author(s):

Jingjing Ma

Keyword(s):

Graph Theory ◽

Self Assembly ◽

Dna Computing ◽

Hamiltonian Path ◽

Au Nanoparticle ◽

Assembly Model ◽

Hamiltonian Path Problem ◽

Complete Problem ◽

Np Complete ◽

Nanoparticle Conjugation

A DNA computing algorithm is proposed in this paper. The algorithm uses the assembly of DNA/Au nanoparticle conjugation to solve an NP-complete problem in the Graph theory, the Hamiltonian Path problem. According to the algorithm, I designed the special DNA/Au nanoparticle conjugations which assembled based on a specific graph, then, a series of experimental techniques are utilized to get the final result. This biochemical algorithm can reduce the complexity of the Hamiltonian Path problem greatly, which will provide a practical way to the best use of DNA self-assembly model.

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On the Classification of NP Complete Problems and Their Duality Feature

International Journal of Computer Science and Information Technology ◽

10.5121/ijcsit.2018.10106 ◽

2018 ◽

Vol 10 (1) ◽

pp. 67-78 ◽

Cited By ~ 1

Author(s):

Wenhong Tian ◽

Wenxia Guo ◽

Majun He

Keyword(s):

Complete Problems ◽

Np Complete

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Energy-Efficient Packet Forwarding Scheme Based on Fuzzy Decision-Making in Underwater Sensor Networks

Sensors ◽

10.3390/s21134368 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4368

Author(s):

Jitander Kumar Pabani ◽

Miguel-Ángel Luque-Nieto ◽

Waheeduddin Hyder ◽

Pablo Otero

Keyword(s):

Energy Consumption ◽

Sensor Networks ◽

Energy Efficient ◽

Real Life ◽

Base Station ◽

Fuzzy Decision Making ◽

Underwater Sensor Networks ◽

Packet Forwarding ◽

Node Number ◽

The Impact

Underwater Wireless Sensor Networks (UWSNs) are subjected to a multitude of real-life challenges. Maintaining adequate power consumption is one of the critical ones, for obvious reasons. This includes proper energy consumption due to nodes close to and far from the sink node (gateway), which affect the overall energy efficiency of the system. These wireless sensors gather and route the data to the onshore base station through the gateway at the sea surface. However, finding an optimum and efficient path from the source node to the gateway is a challenging task. The common reasons for the loss of energy in existing routing protocols for underwater are (1) a node shut down due to battery drainage, (2) packet loss or packet collision which causes re-transmission and hence affects the performance of the system, and (3) inappropriate selection of sensor node for forwarding data. To address these issues, an energy efficient packet forwarding scheme using fuzzy logic is proposed in this work. The proposed protocol uses three metrics: number of hops to reach the gateway node, number of neighbors (in the transmission range of a node) and the distance (or its equivalent received signal strength indicator, RSSI) in a 3D UWSN architecture. In addition, the performance of the system is also tested with adaptive and non-adaptive transmission ranges and scalable number of nodes to see the impact on energy consumption and number of hops. Simulation results show that the proposed protocol performs better than other existing techniques or in terms of parameters used in this scheme.

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Characterization of Airborne Bacteria at an Underground Subway Station

Applied and Environmental Microbiology ◽

10.1128/aem.07212-11 ◽

2012 ◽

Vol 78 (6) ◽

pp. 1917-1929 ◽

Cited By ~ 48

Author(s):

Marius Dybwad ◽

Per Einar Granum ◽

Per Bruheim ◽

Janet Martha Blatny

Keyword(s):

Real Life ◽

Performance Criteria ◽

Anthropogenic Sources ◽

Subway Station ◽

Airborne Bacteria ◽

Surveillance Systems ◽

Operational Environment ◽

Biological Detection ◽

Content Type ◽

Subway Stations

ABSTRACTThe reliable detection of airborne biological threat agents depends on several factors, including the performance criteria of the detector and its operational environment. One step in improving the detector's performance is to increase our knowledge of the biological aerosol background in potential operational environments. Subway stations are enclosed public environments, which may be regarded as potential targets for incidents involving biological threat agents. In this study, the airborne bacterial community at a subway station in Norway was characterized (concentration level, diversity, and virulence- and survival-associated properties). In addition, a SASS 3100 high-volume air sampler and a matrix-assisted laser desorption ionization–time of flight mass spectrometry-based isolate screening procedure was used for these studies. The daytime level of airborne bacteria at the station was higher than the nighttime and outdoor levels, and the relative bacterial spore number was higher in outdoor air than at the station. The bacterial content, particle concentration, and size distribution were stable within each environment throughout the study (May to September 2010). The majority of the airborne bacteria belonged to the generaBacillus,Micrococcus, andStaphylococcus, but a total of 37 different genera were identified in the air. These results suggest that anthropogenic sources are major contributors to airborne bacteria at subway stations and that such airborne communities could harbor virulence- and survival-associated properties of potential relevance for biological detection and surveillance, as well as for public health. Our findings also contribute to the development of realistic testing and evaluation schemes for biological detection/surveillance systems by providing information that can be used to mimic real-life operational airborne environments in controlled aerosol test chambers.

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