25,883 research outputs found
A Framework for Complexity Classes in Membrane Computing
The purpose of the present work is to give a general idea about the existing results and open problems
concerning the study of complexity classes within the membrane computing framework. To this aim,
membrane systems (seen as computing devices) are briefly introduced, providing the basic definition and
summarizing the key ideas, trying to cover the various approaches that are under investigation in this area
– of course, special attention is paid to the study of complexity classes. The paper concludes with some
final remarks that hint the reasons why this field (as well as other unconventional models of computation)
is attracting the attention of a growing community.Ministerio de Educación y Ciencia TIN2005-09345-C04-01Junta de Andalucía TIC-58
On the efficiency of cell-like and tissue-like recognizing membrane systems
Cell-like recognizing membrane systems are computational devices in the framework of membrane
computing inspired from the structure of living cells, where biological membranes are arranged
hierarchically. In this paper tissue-like recognizing membrane systems are presented. The idea is to
consider that membranes are placed in the nodes of a graph, mimicking the cell intercommunication in
tissues.
In this context, polynomial complexity classes associated with recognizing membrane systems can be
defined. We recall the definition for cell-like systems, and we introduce the corresponding complexity
classes for the tissue-like case. Moreover, in this paper two efficient solutions to the satisfiability
problem are analyzed and compared from a complexity point of view.Ministerio de Educación y Ciencia TIN2005-09345-C04-01Junta de Andalucía TIC-58
An apparently innocent problem in Membrane Computing
The search for effcient solutions of computationally hard problems by means
of families of membrane systems has lead to a wide and prosperous eld of research. The
study of computational complexity theory in Membrane Computing is mainly based on
the look for frontiers of effciency between different classes of membrane systems. Every
frontier provides a powerful tool for tackling the P versus NP problem in the following
way. Given two classes of recognizer membrane systems R1 and R2, being systems from
R1 non-effcient (that is, capable of solving only problems from the class P) and systems
from R2 presumably e cient (that is, capable of solving NP-complete problems), and
R2 the same class that R1 with some ingredients added, passing from R1 to R2 is
comparable to passing from the non effciency to the presumed effciency. In order to
prove that P = NP, it would be enough to, given a solution of an NP-complete problem
by means of a family of recognizer membrane systems from R2, try to remove the added
ingredients to R2 from R1. In this paper, we study if it is possible to solve SAT by
means of a family of recognizer P systems from AM0(�����d;+n), whose non-effciency was
demonstrated already
MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME
Computational experiments using spatial stochastic simulations have led to
important new biological insights, but they require specialized tools, a
complex software stack, as well as large and scalable compute and data analysis
resources due to the large computational cost associated with Monte Carlo
computational workflows. The complexity of setting up and managing a
large-scale distributed computation environment to support productive and
reproducible modeling can be prohibitive for practitioners in systems biology.
This results in a barrier to the adoption of spatial stochastic simulation
tools, effectively limiting the type of biological questions addressed by
quantitative modeling. In this paper, we present PyURDME, a new, user-friendly
spatial modeling and simulation package, and MOLNs, a cloud computing appliance
for distributed simulation of stochastic reaction-diffusion models. MOLNs is
based on IPython and provides an interactive programming platform for
development of sharable and reproducible distributed parallel computational
experiments
Computing with cells: membrane systems - some complexity issues.
Membrane computing is a branch of natural computing which abstracts computing models from the structure and the functioning of the living cell. The main ingredients of membrane systems, called P systems, are (i) the membrane structure, which consists of a hierarchical arrangements of membranes which delimit compartments where (ii) multisets of symbols, called objects, evolve according to (iii) sets of rules which are localised and associated with compartments. By using the rules in a nondeterministic/deterministic maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one membrane to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. Membrane systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future biotechnology gives way to a practical bio-realization. In this paper we survey some interesting and fundamental complexity issues such as universality vs. nonuniversality, determinism vs. nondeterminism, membrane and alphabet size hierarchies, characterizations of context-sensitive languages and other language classes and various notions of parallelism
A new perspective on computational complexity theory in Membrane Computing
A single Turing machine can solve decision problems with an in nite number
of instances. On the other hand, in the framework of membrane computing, a \solution"
to an abstract decision problem consists of a family of membrane systems (where each
system of the family is associated with a nite set of instances of the problem to be
solved). An interesting question is to analyze the possibility to nd a single membrane
system able to deal with the in nitely many instances of a decision problem.
In this context, it is fundamental to de ne precisely how the instances of the problem
are introduced into the system. In this paper, two different methods are considered:
pre-computed (in polynomial time) resources and non-treated resources.
An extended version of this work will be presented in the 20th International Conference
on Membrane Computing.Ministerio de Economía, Industria y Competitividad TIN2017-89842-
Computational convergence of the path integral for real dendritic morphologies
Neurons are characterised by a morphological structure unique amongst biological cells, the core of which is the dendritic tree. The vast number of dendritic geometries, combined with heterogeneous properties of the cell membrane, continue to challenge scientists in predicting neuronal input-output relationships, even in the case of sub-threshold dendritic currents. The Green’s function obtained for a given dendritic geometry provides this functional relationship for passive or quasi-active dendrites and can be constructed by a sum-over-trips approach based on a path integral formalism. In this paper, we introduce a number of efficient algorithms for realisation of the sum-over-trips framework and investigate the convergence of these algorithms on different dendritic geometries. We demonstrate that the convergence of the trip sampling methods strongly depends on dendritic morphology as well as the biophysical properties of the cell membrane. For real morphologies, the number of trips to guarantee a small convergence error might become very large and strongly affect computational efficiency. As an alternative, we introduce a highly-efficient matrix method which can be applied to arbitrary branching structures
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
Computer vision is experiencing an AI renaissance, in which machine learning
models are expediting important breakthroughs in academic research and
commercial applications. Effectively training these models, however, is not
trivial due in part to hyperparameters: user-configured values that control a
model's ability to learn from data. Existing hyperparameter optimization
methods are highly parallel but make no effort to balance the search across
heterogeneous hardware or to prioritize searching high-impact spaces. In this
paper, we introduce a framework for massively Scalable Hardware-Aware
Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the
relative complexity of each search space and monitors performance on the
learning task over all trials. These metrics are then used as heuristics to
assign hyperparameters to distributed workers based on their hardware. We first
demonstrate that our framework achieves double the throughput of a standard
distributed hyperparameter optimization framework by optimizing SVM for MNIST
using 150 distributed workers. We then conduct model search with SHADHO over
the course of one week using 74 GPUs across two compute clusters to optimize
U-Net for a cell segmentation task, discovering 515 models that achieve a lower
validation loss than standard U-Net.Comment: 10 pages, 6 figure
Limits on P Systems with Proteins and Without Division
In the field of Membrane Computing, computational complexity theory has
been widely studied trying to nd frontiers of efficiency by means of syntactic or semantical ingredients. The objective of this is to nd two kinds of systems, one non-efficient
and another one, at least, presumably efficient, that is, that can solve NP-complete prob-
lems in polynomial time, and adapt a solution of such a problem in the former. If it is
possible, then P = NP. Several borderlines have been defi ned, and new characterizations
of different types of membrane systems have been published.
In this work, a certain type of P system, where proteins act as a supporting element
for a rule to be red, is studied. In particular, while division rules, the abstraction of
cellular mitosis is forbidden, only problems from class P can be solved, in contrast to the
result obtained allowing them.Ministerio de Economía y Competitividad TIN2017-89842-PNational Natural Science Foundation of China No 6132010600
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