Probing the structure–function relationship with neural networks constructed by solving a system of linear equations

Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting. Here we propose to invert the fitting process by proceeding from the network dynamics towards network parameters. Firing state transitions are chosen according to the transition graph associated with the solution of a task. Then, a system of linear equations is constructed from the network firing states and membrane potentials, in a way that guarantees the consistency of the system. This allows us to uncouple the dynamical features of the model, like its neurons firing rate and correlation, from the structural features, and the task-solving algorithm implemented by the network. We employed our method to probe the structure–function relationship in a sequence memory task. The networks obtained showed connectivity and firing statistics that recapitulated experimental observations. We argue that the proposed method is a complementary and needed alternative to the way neural networks are constructed to model brain function.Fil: Mininni, Camilo Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica.; ArgentinaFil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica.; Argentin

Teaching Mathematics to Computer Scientists: Reflections and a Case Study

Mathematics, despite being the foundation of computer science, is nowadays often considered a totally separate subject. The fact that many jobs in computer science do not explicitly require any specific mathematical knowledge posed questions about the importance of mathematics within computer science undergraduate curricula. In many educational systems a prior high school knowledge of mathematics is often not a mandatory requirement to be enrolled into a degree of computer science. On the other hand, several studies report that mathematics is important to computer scientists since it provides essential analytical and critical skills and since many professional and research tasks in computer science require an in-depth understanding of mathematical concepts. From this assumption, this article proposes an analysis of the cohort of computer science' students, with a specific reference to British Universities, and identifies some challenges that lecturers of mathematical subjects normally face. On the basis of this analysis this article proposes two teaching techniques to promote effective learning. The proposed techniques aim at addressing the diversity of cohorts in terms of mathematical background and skepticism from part of the cohort of students to consider mathematics as an essential element of their education. Numerical results indicate the validity and effectiveness of the proposed teaching techniques

Cloud-Assisted Secure eHealth Systems for Tamper-Proofing EHR via Blockchain

The wide deployment of cloud-assisted electronic health (eHealth) systems has already shown great benefits in managing electronic health records (EHRs) for both medical institutions and patients. However, it also causes critical security concerns. Since once a medical institution generates and outsources the patients' EHRs to cloud servers, patients would not physically own their EHRs but the medical institution can access the EHRs as needed for diagnosing, it makes the EHRs integrity protection a formidable task, especially in the case that a medical malpractice occurs, where the medical institution may collude with the cloud server to tamper with the outsourced EHRs to hide the medical malpractice. Traditional cryptographic primitives for the purpose of data integrity protection cannot be directly adopted because they cannot ensure the security in the case of collusion between the cloud server and medical institution. In this paper, a secure cloud-assisted eHealth system is proposed to protect outsourced EHRs from illegal modification by using the blockchain technology (blockchain-based currencies, e.g., Ethereum). The key idea is that the EHRs only can be outsourced by authenticated participants and each operation on outsourcing EHRs is integrated into the public blockchain as a transaction. Since the blockchain-based currencies provide a tamper-proofing way to conduct transactions without a central authority, the EHRs cannot be modified after the corresponding transaction is recorded into the blockchain. Therefore, given outsourced EHRs, any participant can check their integrity by checking the corresponding transaction. Security analysis and performance evaluation demonstrate that the proposed system can provide a strong security guarantee with a high efficiency

Generalised Pattern Search Based on Covariance Matrix Diagonalisation

Pattern Search is a family of gradient-free direct search methods for numerical optimisation problems. The characterising feature of pattern search methods is the use of multiple directions spanning the problem domain to sample new candidate solutions. These directions compose a matrix of potential search moves, that is the pattern. Although some fundamental studies theoretically indicate that various directions can be used, the selection of the search directions remains an unaddressed problem. The present article proposes a procedure for selecting the directions that guarantee high convergence/high performance of pattern search. The proposed procedure consists of a fitness landscape analysis to characterise the geometry of the problem by sampling points and selecting those that whose objective function values are below a threshold. The eigenvectors of the covariance matrix of this distribution are then used as search directions for the pattern search. Numerical results show that the proposed method systematically out-performs its standard counterpart and is competitive with modern complex direct search and metaheuristic methods

A Study on Rotation Invariance in Differential Evolution

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Epistasis is the correlation between the variables of a function and is a challenge often posed by real-world optimisation problems. Synthetic benchmark problems simulate a highly epistatic problem by performing a so-called problem's rotation. Mutation in Differential Evolution (DE) is inherently rotational invariant since it simultaneously perturbs all the variables. On the other hand, crossover, albeit fundamental for achieving a good performance, retains some of the variables, thus being inadequate to tackle highly epistatic problems. This article proposes an extensive study on rotational invariant crossovers in DE. We propose an analysis of the literature, a taxonomy of the proposed method and an experimental setup where each problem is addressed in both its non-rotated and rotated version. Our experimental study includes $280$ problems over five different levels of dimensionality and nine algorithms. Numerical results show that 1) for a fixed quota of transferred design variables, the exponential crossover displays a better performance, on both rotated and non-rotated problems, in high dimensions while the binomial crossover seems to be preferable in low dimensions; 2) the rotational invariant mutation DE/current-to-rand is not competitive with standard DE implementations throughout the entire set of experiments we have presented; 3) DE crossovers that perform a change of coordinates to distribute the moves over the components of the offspring offer high-performance results on some problems. However, on average the standard DE/rand/1/exp appears to achieve the best performance on both rotated and non-rotated testbeds

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Novel Strategies to Accelerate Search Algorithms in Data Reduction

In our current hyper-connected digital world where data is growing enormously, instance reduction is an essential pre-processing phase to obtain cleaner and smaller datasets that are free from noise, redundant or irrelevant samples (the so-called, Smart Data). The data after pre-processing may become more reliable, accurate and useful for subsequent data mining tasks. Instance reduction consists of two types: instance selection and instance generation; each can be formulated as a combinatorial/continuous optimisation problem depending on whether its decision variable is discrete or continuous, respectively. It is an emerging challenge characterised by multimodality and a large number of decision variables. Given such difficulties, derivative-free methods are likely promising approaches to address the problem. They are powerful search algorithms that seek the nearest local optimum and do not necessarily take into account the gradient computation of the objective function like derivative methods. Solutions for instance reduction fall into the intersection of machine learning, data mining and optimisation at which the process of a domain can take part in the execution of another. Thus, the synergy between domains is important to solve the problem more effectively, and this has attracted a significant interest from researchers. Among many different derivative-free search approaches, the family of direct search methods has introduced various strategies to tackle numerous modern numerical optimisation problems, where population-based meta-heuristics and pattern search can be considered two of the most prevalent in the literature. Population-based meta-heuristics are an iterative search framework composing several subordinate low-level heuristics to control exploration and exploitation for a pool of solution candidates. This set of methods searches for high-quality solutions from multi-points, and thus is usually associated with high computational expense. Pattern search methods seek an improved solution from candidates that are generated from different directions. They examine trial solutions sequentially by comparing each trial solution with the `best' solution found up to the present time. In this dissertation, we will investigate these derivative-free search strategies to address instance reduction, a critical optimisation problem in the field of data science. Although many derivative-free methods have been proved effective in addressing instance reduction, they are usually time-consuming, especially when handling relatively large datasets. This impediment limits their practicality in many data mining systems and thus necessitates a solution to accelerate the search process. The need for a fast and effective search framework for instance reduction has motivated us to develop novel search strategies in the family of direct search approaches, aiming to still obtain high quality solutions achieved by state-of-the-art techniques in the domain, but significantly reduce the runtime of the search process. Three major work packages presented in this thesis will cover two direct search approaches for two types of instance reduction, arranged in a progressive order at which findings at an earlier stage will contribute to the understanding of the later outcomes. Firstly, a novel evolutionary search framework for instance selection is proposed to balance the number of samples between classes to address a case study of imbalanced classification. Secondly, we develop another search framework for instance generation based on single-point search and memetic computing, namely Single-Point Memetic Structure. An accelerated mechanism for computing the objective function is embedded into the proposed search design, thus reducing significantly the runtime. Finally, a novel search framework for simultaneous instance selection and generation is designed to handle the instance reduction problem in both combinatorial and continuous search spaces. In summary, the research conducted here introduces a set of novel search strategies towards derivative-free methods to tackle instance reduction problems. They are different search frameworks which aim to produce a high quality reduced set from a relatively large original source within a reasonable amount of time. This is accomplished by either taking advantage of machine learning integration or the Single-Point Memetic Structure with an accelerated mechanism. The use of machine learning in a meta-heuristic search framework greatly speeds up the computation of the objective function while the Single-Point Memetic Search allows us to reuse virtually all prior calculations for computing the fitness value of newly evolved individuals. Hence, these novel search strategies can save vast computational cost. Finally, we leverage the insights previously found to propose another novel search framework that handles both instance selection and instance generation simultaneously, and operates in both combinatorial and continuous search spaces. These novel search strategies are examined with a large number of datasets in different hyper-parameter settings. The obtained numerical results are comprehensively analysed and verified by different statistical tests to prove the robustness of the proposed search strategies with respect to other state-of-the-art techniques in the domain

Novel Strategies to Accelerate Search Algorithms in Data Reduction

In our current hyper-connected digital world where data is growing enormously, instance reduction is an essential pre-processing phase to obtain cleaner and smaller datasets that are free from noise, redundant or irrelevant samples (the so-called, Smart Data). The data after pre-processing may become more reliable, accurate and useful for subsequent data mining tasks. Instance reduction consists of two types: instance selection and instance generation; each can be formulated as a combinatorial/continuous optimisation problem depending on whether its decision variable is discrete or continuous, respectively. It is an emerging challenge characterised by multimodality and a large number of decision variables. Given such difficulties, derivative-free methods are likely promising approaches to address the problem. They are powerful search algorithms that seek the nearest local optimum and do not necessarily take into account the gradient computation of the objective function like derivative methods. Solutions for instance reduction fall into the intersection of machine learning, data mining and optimisation at which the process of a domain can take part in the execution of another. Thus, the synergy between domains is important to solve the problem more effectively, and this has attracted a significant interest from researchers. Among many different derivative-free search approaches, the family of direct search methods has introduced various strategies to tackle numerous modern numerical optimisation problems, where population-based meta-heuristics and pattern search can be considered two of the most prevalent in the literature. Population-based meta-heuristics are an iterative search framework composing several subordinate low-level heuristics to control exploration and exploitation for a pool of solution candidates. This set of methods searches for high-quality solutions from multi-points, and thus is usually associated with high computational expense. Pattern search methods seek an improved solution from candidates that are generated from different directions. They examine trial solutions sequentially by comparing each trial solution with the `best' solution found up to the present time. In this dissertation, we will investigate these derivative-free search strategies to address instance reduction, a critical optimisation problem in the field of data science. Although many derivative-free methods have been proved effective in addressing instance reduction, they are usually time-consuming, especially when handling relatively large datasets. This impediment limits their practicality in many data mining systems and thus necessitates a solution to accelerate the search process. The need for a fast and effective search framework for instance reduction has motivated us to develop novel search strategies in the family of direct search approaches, aiming to still obtain high quality solutions achieved by state-of-the-art techniques in the domain, but significantly reduce the runtime of the search process. Three major work packages presented in this thesis will cover two direct search approaches for two types of instance reduction, arranged in a progressive order at which findings at an earlier stage will contribute to the understanding of the later outcomes. Firstly, a novel evolutionary search framework for instance selection is proposed to balance the number of samples between classes to address a case study of imbalanced classification. Secondly, we develop another search framework for instance generation based on single-point search and memetic computing, namely Single-Point Memetic Structure. An accelerated mechanism for computing the objective function is embedded into the proposed search design, thus reducing significantly the runtime. Finally, a novel search framework for simultaneous instance selection and generation is designed to handle the instance reduction problem in both combinatorial and continuous search spaces. In summary, the research conducted here introduces a set of novel search strategies towards derivative-free methods to tackle instance reduction problems. They are different search frameworks which aim to produce a high quality reduced set from a relatively large original source within a reasonable amount of time. This is accomplished by either taking advantage of machine learning integration or the Single-Point Memetic Structure with an accelerated mechanism. The use of machine learning in a meta-heuristic search framework greatly speeds up the computation of the objective function while the Single-Point Memetic Search allows us to reuse virtually all prior calculations for computing the fitness value of newly evolved individuals. Hence, these novel search strategies can save vast computational cost. Finally, we leverage the insights previously found to propose another novel search framework that handles both instance selection and instance generation simultaneously, and operates in both combinatorial and continuous search spaces. These novel search strategies are examined with a large number of datasets in different hyper-parameter settings. The obtained numerical results are comprehensively analysed and verified by different statistical tests to prove the robustness of the proposed search strategies with respect to other state-of-the-art techniques in the domain

Linear Algebra for Computational Sciences and Engineering

This book presents the main concepts of linear algebra from the viewpoint of applied scientists such as computer scientists and engineers, without compromising on mathematical rigor. Based on the idea that computational scientists and engineers need, in both research and professional life, an understanding of theoretical concepts of mathematics in order to be able to propose research advances and innovative solutions, every concept is thoroughly introduced and is accompanied by its informal interpretation. Furthermore, most of the theorems included are first rigorously proved and then shown in practice by a numerical example. When appropriate, topics are presented also by means of pseudocodes, thus highlighting the computer implementation of algebraic theory.It is structured to be accessible to everybody, from students of pure mathematics who are approaching algebra for the first time to researchers and graduate students in applied sciences who need a theoretical manual of algebra to successfully perform their research. Most importantly, this book is designed to be ideal for both theoretical and practical minds and to offer to both alternative and complementary perspectives to study and understand linear algebra