Miguel A. Gutiérrez-Naranjo

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• PhD
• Professor at University of Seville

Generalized Nash Equilibrium is an extended version of the standard Nash Equilibrium with important implications in real-life problems such as economics, wireless communication, the electricity market, or engineering among other areas. In this paper, we propose a first approach to computing Generalized Nash Equilibria using Membrane Computing techn...

Natural and political disasters, including earthquakes, hurricanes, and tsunamis, but also migration and refugees crisis, need quick and coordinated responses in order to support vulnerable populations. In such disasters, nongovernmental organizations compete with each other for financial donations, while people who need assistance suffer a lack of...

Natural and political disasters, including earthquakes, hurricanes, and tsunamis, but also migration and refugees crisis, need quick and coordinated responses in order to support vulnerable populations. In such disasters, nongovernmental organizations compete with each other for financial donations, while people who need assistance suffer a lack of...

Intent detection is a text classification task whose aim is to recognize and label the semantics behind a users’ query. It plays a critical role in various business applications. The output of the intent detection module strongly conditions the behavior of the whole system. This sequence analysis task is mainly tackled using deep learning technique...

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance al...

Intent detection is a text classification task whose aim is to recognize and label the semantics behind a users query. It plays a critical role in various business applications. The output of the intent detection module strongly conditions the behavior of the whole system. This sequence analysis task is mainly tackled using deep learning techniques...

In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance als...

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed we...

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation capability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high dimensions. First, no SMNN training process has been defi...

Intracranial hemorrhage is a serious medical problem that requires rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in the treatment of the patient. Detection of, and diagnosis of, a hemorrhage that requires an urgent procedure is a difficult and time-consuming process for human...

Intracranial hemorrhage is a serious medical problem that requires rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in the treatment of the patient. Diagnosis requires an urgent procedure, and the detection of hemorrhage is a difficult and time-consuming process for human experts...

Allergic diseases are increasing around the world with unprecedented complexity and severity. One of the reasons is that genetically modified crops produce new potentially allergenic proteins. From this starting point, many researchers have paid attention to the development of tools to predict the allergenicity of new proteins. In this study, a nov...

One of the main drawbacks of the practical use of neural networks is the long time required in the training process. Such a training process consists of an iterative change of parameters trying to minimize a loss function. These changes are driven by a dataset, which can be seen as a set of labeled points in an n-dimensional space. In this paper, w...

Inception module is one of the most used variants in convolutional neural networks. It has a large portfolio of success cases in computer vision. In the past years, diverse inception flavours, differing in the number of branches, the size and the number of the kernels, have appeared in the scientific literature. They are proposed based on the exper...

Intracranial hemorrhage is a serious health problem requiring rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. Diagnosis requires an urgent procedure and the detection of the hemorrhage is a hard and time-consuming process for human experts. In this paper...

The improvement of air-quality in urban areas is one of the main concerns of public government bodies. This concern emerges from the evidence between the air quality and the public health. Major efforts from government bodies in this area include monitoring and forecasting systems, banning more pollutant motor vehicles, and traffic limitations duri...

Evolutionary Game Theory studies the spreading of strategies in populations. An important question of the area concerns the possibility that certain population structures can facilitate the spreading of more cooperative behaviours associated to the sustainability and resilience of many different systems ranging from ecological to socio-economic sys...

Intracranial hemorrhage is a serious health problem requiring rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. Diagnosis requires an urgent procedure and the detection of the hemorrhage is a hard and time-consuming process for human experts. In this paper...

Simplicial-map neural networks are a recent neural network architecture induced by simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural networks are universal approximators and that they can be refined to be robust to adversarial attacks. In this paper, the refinement toward robustness is optimized by...

Broadly speaking, an adversarial example against a classification model occurs when a small perturbation on an input data point produces a change on the output label assigned by the model. Such adversarial examples represent a weakness for the safety of neural network applications, and many different solutions have been proposed for minimizing thei...

The optimization of hyperparameters in Deep Neural Networks is a critical task for the final performance, but it involves a high amount of subjective decisions based on previous researchers’ expertise. This paper presents the implementation of Population-based Incremental Learning for the automatic optimization of hyperparameters in Deep Learning a...

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show tha...

It is well-known that artificial neural networks are universal approximators. The classical existence result proves that, given a continuous function on a compact set embedded in an -dimensional space, there exists a one-hidden-layer feed-forward network that approximates the function. In this paper, a constructive approach to this problem is given...

The canon of the baroque Spanish literature has been thoroughly studied with philological techniques. The major representatives of the poetry of this epoch are Francisco de Quevedo and Luis de Góngora y Argote. They are commonly classified by the literary experts in two different streams: Quevedo belongs to the Conceptismo and Góngora to the Culter...

Self-reconfigurable robots are built by modules which can move in relationship to each other, which allows the robot to change its physical form. Finding a sequence of module moves that reconfigures the robot from the initial configuration to the goal configuration is a hard task and many control algorithms have been proposed. In this paper, we pre...

It is well known that Artificial Neural Networks are universal approximators. The classical result proves that, given a continuous function on a compact set on an n-dimensional space, then there exists a one-hidden-layer feedforward network which approximates the function. Such result proves the existence, but it does not provide a method for findi...

One of the main drawbacks of the practical use of neural networks is the long time needed in the training process. Such training process consists in an iterative change of parameters trying to minimize a loss function. These changes are driven by a dataset, which can be seen as a set of labeled points in an n-dimensional space. In this paper, we ex...

Membrane computing is a well-known research area in computer science inspired by the organization and behavior of live cells and tissues. Their computational devices, called P systems, work in parallel and distributed mode and the information is encoded by multisets in a localized manner. All these features make P systems appropriate for dealing wi...

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show tha...

Neural networks present big popularity and success in many fields. The large training time process problem is a very important task nowadays. In this paper, a new approach to get over this issue based on reducing dataset size is proposed. Two algorithms covering two different shape notions are shown and experimental results are given.

Self-reconfigurable robots are robots built by modules which can move in relationship to each other. This ability of changing its physical form provides the robots a high level of adaptability and robustness. Given an initial configuration and a goal configuration of the robot, the problem of self-regulation consists on finding a sequence of module...

The 3-COL problem consists on deciding if the regions of a map can be coloured with only three colors bearing in mind that two adjacent regions must be coloured with different colors. It is a NP problem and it has been previously used in complexity studies in membrane computing to check the ability of a model for solving problems of such complexity...

The integration of symbolic reasoning systems based on logic and connectionist systems based on the functioning of living neurons is a vivid research area in computer science. In the literature, one can find many efforts where different reasoning systems based on different logics are linked to classic artificial neural networks. In this paper, we s...

The ability of tissue P systems with 2-division for solving NP problems in polynomial time is well-known and many solutions can be found in the literature to several of such problems. Nonetheless, there are very few papers devoted to the Bin-packing problem. The reason may be the difficulties for dealing with different number of bins, capacity and...

In this paper, we present a bio-inspired parallel implementation of a solution of the problem of looking for the representative geometrical objects of the homology groups in a binary 2D image (extended-HGB2I problem), which is an extended version of a well-known problem in homology theory. In particular, given a binary 2D image, all black connected...

In this paper, we present a bio-inspired parallel implementation of a solution of the problem of looking for the representative geometrical objects of the homology groups in a binary 2D image (extended-HGB2I problem), which is an extended version of a well-known problem in homology theory. In particular, given a binary 2D image, all black connected...

This paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics Processing Units (GPU) technology. Image edges are extracted using a new operator, called AGP-color segmentator. The resulting image is binarized with Hamadani’s tech...

In this paper, we consider recognizer P systems with antimatter and the influence of the matter/antimatter annihilation rules having weak priority over all the other rules or not. We first provide a uniform family of P systems with active membranes which solves the strongly NP-complete problem SAT, the Satisfiability Problem, without polarizations...

We prove that every single-tape deterministic Turing machine working in \(t(n)\) time, for some function \(t:\mathbb {N}\rightarrow \mathbb {N}\), can be simulated by a uniform family of polarizationless P systems with active membranes. Moreover, this is done without significant slowdown in the working time. Furthermore, if \(\log t(n)\) is space c...

In Membrane Computing, the solution of a decision problem \(X\) belonging to the complexity class P via a polynomially uniform family of recognizer P systems is trivial, since the polynomial encoding of the input can involve the solution of the problem. The design of such solution has one membrane, two objects, two rules and one computation step. S...

The problem of automatically marking the interior and exterior regions of a simple curve in a digital image becomes a hard task due to the noise and the intrinsic difficulties of the media where the image is taken. In this paper, we propose a definition of the interior of a partially bounded region and present a bio-inspired algorithm for finding i...

It is well known that the polynomial complexity class of recognizer P systems with active membranes without polarizations, without dissolution and with division for elementary and non-elementary membranes is exactly the complexity class P (see [9], Theorem 2). In this paper, we prove that if such a P systems model is endowed with antimatter and ann...

The skeletonization of an image consists of converting the initial image into a more compact representation. In general, the skeleton preserves the basic structure and, in some sense, keeps the meaning. The most important features concerning a shape are its topology (represented by connected components, holes, etc.) and its geometry (elongated part...

Smoothing is often used in Digital Imagery for improving the quality of an image by reducing its level of noise. This paper presents a parallel implementation of an algorithm for smoothing 2D images in the framework of Membrane Computing. The chosen formal framework has been tissue-like P systems. The algorithm has been implemented by using a novel...

One of the concepts that lie at the basis of membrane computing is the multiset rewriting rule. On the other hand, the paradigm of rules is profusely used in computer science for representing and dealing with knowledge. Therefore, establishing a “bridge” between these domains is important, for instance, by designing P systems reproducing the modus...

Skeletonization is a common type of transformation within image analysis. In general, the image B is a skeleton of the black and white image A, if the image B is made of fewer black pixels than the image A, it does preserve its topological properties and, in some sense, keeps its meaning. In this paper, we aim to use spiking neural P systems (a com...

Local search is currently one of the most used methods for finding solutions in real-life problems. It is usually considered when the research is interested in the final solution of the problem instead of the how the solution is reached. In this paper, the authors present an implementation of local search with Membrane Computing techniques applied...

In this paper, we present a parallel implementation of a new algorithm for segmenting images with gradient-based edge detection by using techniques from Natural Computing. This bio-inspired parallel algorithm has been implemented in a novel device architecture called CUDA™(Compute Unified Device Architecture). The implementation has been designed v...

In this paper, we report a pioneer study of the decrease in chlorophyll fluo-rescence produced by the reduction of MTT (a dimethyl thiazolyl diphenyl tetrazolium salt) monitored using an epifluorescence microscope coupled to automate image analysis in the framework of P systems. Such analysis has been performed by a family of tissue P systems worki...

The Rete algorithm is a well-known pattern matching algorithm conceived to make rule-based production system implementations more efficient. It builds a directed acyclic graph, representing higher-level rule sets, that allows the implementation to avoid checking each step the applicability of all the rules. Instead, only those affected by a change...

Effective Homology is an algebraic-topological method based on the computational concept of chain homotopy equivalence on a cell complex. Using this algebraic data structure, Effective Homology gives answers to some important computability problems in Algebraic Topology. In a discrete context, Effective Homology can be seen as a combinatorial layer...

In this paper we present a new software tool for dealing with the problem of segmentation in Digital Imagery. The implementation is inspired in the design of a tissue-like P system which solves the problem in constant time due the intrinsic parallelism of Membrane Computing devices. KeywordsMembrane computing–Digital Imagery–Segmentation

Recent developments of computer architectures together with alternative formal descriptions provide new challenges in the study of digital Images. In this paper we present a new implementation of the Guo & Hall algorithm [8] for skeletonizing images based on Cellular Automata. The implementation is performed in a real-time parallel way by using the...

Counting the number of cells obtained in an experiment is crucial in many areas in Biology. Nonetheless, this is usually performed by hand by the researcher due the intrinsic difficulty of the task. In this paper, we present a set of techniques for counting cells inspired in the treatment of Digital Images via tissue-like P systems with promoters.

One of the concepts that lie at the basis of membrane computing is the multiset rewriting rule. On the other hand, the paradigm of rules is profusely used in computer science for representing and dealing with knowledge. Therefore, establishing a “bridge” between these domains is important, for instance, by designing P systems reproducing the modus...

In this paper we present a parallel algorithm to solve the thresholding problem by using Membrane Computing techniques. This bio-inspired algorithm has been implemented in a novel device architecture called CUDATM, (Compute Unified Device Architecture). We present some examples, compare the obtained time and present some research lines for the futu...

Smoothing is often used in Digital Imagery for improving the quality of an image by reducing its level of noise. This paper presents a parallel implementation of an algorithm for smoothing 2D images in the framework of Membrane Computing. The chosen formal framework has been tissue-like P systems. The algorithm has been implemented by using a novel...

The hand-made graphical representation of the configuration of a P system becomes a hard task when the number of membranes and objects increases. In this paper we present a new software tool, called JPLANT, for computing and representing the evolution of a P system model with membrane creation. We also present some experiments performed with JPLANT...

On the one hand, one of the concepts which lies at the basis of membrane computing is the multiset rewriting rule. On the other hand, the paradigm of rules is profusely used in computer science for representing and dealing with knowledge. Therefore, it makes much scene to establish a ”bridge ” between these domains, for instance, by designing P sys...

Problems associated with the treatment of digital images have several in-teresting features from a bio-inspired point of view. One of them is that they can be suitable for parallel processing, since the same sequential algorithm is usually applied in different regions of the image. In this paper we report a work-in-progress of a hardware implementa...

At the beginning of 2005, Gheorghe Păun formulated a conjecture stating that in the framework of recognizer P systems with active membranes (evolution rules, communication rules, dissolution rules and division rules for elementary membranes), polarizations cannot be avoided in order to solve computationally hard problems efficiently (assuming that...

Local search is currently one of the most used methods for finding solutions in real-life problems. It is usually considered when the research is interested in the final solution of the problem instead of the how the solution is reached. In this paper, the authors present an implementation of local search with Membrane Computing techniques applied...

One of the concepts that lie at the basis of membrane computing is the multiset rewriting rule. On the other hand, the paradigm of rules is profusely used in computer science for representing and dealing with knowledge. Therefore, establishing a “bridge” between these domains is important, for instance, by designing P systems reproducing the modus...

Segmentation in computer vision refers to the process of partitioning a digital image into multiple segments (sets of pixels). It has several features which make it suitable for techniques inspired by nature. It can be parallelized, locally solved and the input data can be easily encoded by bio-inspired representations. In this paper, we present a...

Sudoku is a very popular puzzle which consists on placing several numbers in a squared grid according to some simple rules. In this paper we present an efficient family of P systems which solve sudokus of any order verifying a specific property. The solution is searched by using a simple human-style method. If the sudoku cannot be solved by using t...

The usual way to find a solution for an NP complete problem in Membrane Computing is by brute force algorithms. These solutions work from a theoretical point of view but they are implementable only for small instances of the problem. In this paper we provide a family of P systems which brings techniques from Artificial Intelligence into Membrane Co...

Tissue-like P systems with cell division is a computing model in the framework of membrane computing that is based on the intercellular communication and cooperation between neurons. In such a model, the structure of the devices is a network of elementary cells. Tissue-like P systems with cell division have the ability of increasing the number of c...

Array grammars have been studied in the framework of Membrane Comput-ing by using rewriting rules from transition P systems. In this paper we present a new approach to dealing with array grammars by using tissue-like P systems and present an application to the segmentation of images in two dimensional computer graphics.

Searching all the configurations C ′ such that produce a given configuration C, or, in other words, computing backwards in Membrane Computing is an extremely hard task. The current approximations are based in heavy handmade calculus by considering the specific features of the given configuration. In this paper we present a general method for charac...

In this paper we present a tissue-like P systems model with cell division the environment has been replaced by an extra cell. In such model, we present a uniform family of recognizer P systems which solves the Subset Sum problem. This solution establishes a new frontier for the tractability of computationally hard problems in Membrane Computing, si...

In this paper, we describe a new representation for deterministic rational-valued P systems that allows us to form a bridge between membrane computing and linear algebra. On the one hand, we prove that an efficient computation for these P systems can be described using linear algebra techniques. In particular, we show that the computation for getti...

In this paper we address the problem of describing the complexity of the evolution of a tissue-like P system with cell division. In the computations of such systems the number of (parallel) steps is not sufficient to evaluate the complexity. Following this consideration, Sevilla Carpets were introduced as a tool to describe the space-time complexit...

We consider spiking neural P systems as devices which can be used to perform some basic arithmetic operations, namely addition, subtraction, comparison and multiplica-tion by a fixed factor. The input to these systems are natural numbers expressed in binary form, encoded as appropriate sequences of spikes. A single system accepts as inputs num-bers...

Searching all the configurations C′ which produce a given configuration C is an extremely hard task. The current approximations are based on heavy hand-made calculus by considering the specific features of the given configuration. In this paper we present a general method for characterizing all the configurations C′ which produce a given configurat...

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,...

Tissue-like P systems with cell division is a computing model in the framework of Membrane Computing inspired by the intercellular communication and neuronal synaptics. It considers the cells as unit processors and the computation is performed by the parallel application of given rules. Division rules allow an increase of the number of cells during...

Ballistic Deposition was proposed by Vold [10] and Sutherland [9] as a model for colloidal aggregation. These early works were later extended to simulate the process of vapour deposition. In general, Ballistic Deposition models involve (d+1)-dimensional particles which rain down sequentially at random onto a d-dimensional substrate; when a particle...

The formal verification of a Spiking Neural P System (SN P Systems, for short) designed for solving a given problem is usually a hard task. Basically, the verification process consists of the search of invariant formulae such that, once proved their validity, show the right answer to the problem. Even though there does not exist a general methodolo...

Tissue-like P systems with cell division is a computing model in the framework of membrane computing based on the intercellular communication and cooperation between neurons. In such a model, the structure of the devices is a network of elementary cells. Tissue-like P systems with cell division have the ability of increasing the number of cells dur...

Several examples of the efficiency of cell-like P systems regarding the solution of NP-complete problems in polynomial time can be found in the literature(obviously, trading space for time). Recently, different new models of tissue-like P systems have received much attention from the scientific community. In this paper we present a linear-time solu...

Spiking neural P systems and artificial neural networks are computational devices which share a biological inspiration based on the flow of information among neurons. In this paper we present a first model for Hebbian learning in the framework of spiking neural P systems by using concepts borrowed from neuroscience and artificial neural network the...

Recently the possibility of using spiking neural P systems for solving computationally hard problems has been considered. Such solutions assume that some (possibly exponentially large) pre-computed resources are given in advance, provided that their structure is “regular” and they do not contain neither “hidden information” that simplify the soluti...

Recently we have considered the possibility of using spiking neural P systems for solving computationally hard problems, under the assumption that some (possibly ex-ponentially large) pre-computed resources are given in advance. In this paper we continue this research line, and we investigate the possibility of solving numerical NP-complete problem...

In the literature, several examples of the efficiency of cell-like P systems regarding the solution of NP-complete problems in polynomial time can be found (obviously, trading space for time). Recently, different new models of tissue-like P systems have received important attention from the scientific community. In this paper we present a linear-ti...

The aim of our paper is twofold. On one hand we prove the ability of polarizationless P systems with dissolution and with division rules for non-elementary membranes to solve NP-complete problems in a polynomial number of steps, and we do this by presenting a solution to the Subset Sum problem. On the other hand, we improve some similar results obt...

Tissue P systems with cell division is a computing model in the framework of Membrane Computing based on intercellular communication and cooperation between neurons. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to NP-complete problems in polynomial time. In this pap...

In this paper we discuss the potential usefulness of membrane systems as tools for modelling tumours. The approach is followed both from a macroscopic and a microscopic point of view. In the first case, one considers the tumour as a growing mass of cells, focusing on its external shape. In the second case, one descends to the microscopic level, stu...

In the literature, several designs of P systems might be found for performing the same task. The use of different techniques or even different P system models makes it very difficult to compare these designs. In this paper, we introduce a new criterion for such a comparison: the degree of parallelism of a P system. With this aim, we define the labe...

In living cells, new membranes are produced basically through two processes: mitosis and autopoiesis. These two processes have inspired two variants of cell-like membrane systems, namely P systems with active membranes and P systems with membrane creation. In this paper, we provide the first uniform, efficient solution to the SAT problem in the fra...

Membrane Computing is a branch of Natural Computing which starts from the assumption that the processes taking place in the compartmental structure of a living cell can be interpreted as computations. The description of the complexity of the computations of the membrane devices (P systems) is a hard task which goes beyond the usual parameters of ti...

The aim of this paper is to start an investigation and a comparison of the expressiveness of the two most relevant formalisms inspired by membranes interactions, namely, P systems and Brane Calculi. We compare the two formalisms with respect to their ability to act as generator devices. In particular, we show different ways of generating the set L=...

L systems have been widely used to model and graphically represent the growth of higher plants [20]. In this paper we continue developing the framework introduced in [21], which make use of the topology of membrane structures to model the morphology of branching structures.

Trading (in polynomial time) space for time in the framework of membrane systems is not sufficient to efficiently solve computationally hard problems. On the one hand, an exponential number of objects generated in polynomial time is not sufficient to solve NP-complete problems in polynomial time. On the other hand, when an exponential number of mem...

Current P systems which solve NP–complete numerical problems represent the instances of the problems in unary notation. However, in classical complexity theory, based upon Turing machines, switching from binary to unary encoded instances generally corresponds to simplify the problem. In this paper we show that, when working with P systems, we can a...

Membrane Computing and Brane Calculi are two recent computational paradigms in the framework of Natural Computing. They are based on the study of the structure and functioning of living cells as living organisms able to process and generate information. In this paper we give a short introduction to both areas and point out some open research lines.

The simulation of a P system with current computers is a quite complex task. P systems are intrinsically nondeterministic computational devices and therefore their computation trees are difficult to store and handle with computers with one processor (or a bounded number of processors). Nevertheless, there exists a first generation of simulators whi...

The aim of this paper is to start an investigation and a com- parison of the expressiveness of the two most relevant formalisms in- spired by membranes interactions, namely, P systems and Brane Calculi. We compare the two formalisms w.r.t. their ability to act as language generators. In particular, we show dierent ways of generating the set L = {n2...