Gardner optimal capacity of the diluted Blume-Emery-Griffiths neural network

The optimal capacity of a diluted Blume-Emery-Griffiths neural network is studied as a function of the pattern activity and the embedding stability using the Gardner entropy approach. Annealed dilution is considered, cutting some of the couplings referring to the ternary patterns themselves and some of the couplings related to the active patterns, both simultaneously (synchronous dilution) or independently (asynchronous dilution). Through the de Almeida-Thouless criterion it is found that the replica-symmetric solution is locally unstable as soon as there is dilution. The distribution of the couplings shows the typical gap with a width depending on the amount of dilution, but this gap persists even in cases where a particular type of coupling plays no role in the learning process.Comment: 9 pages Latex, 2 eps figure

Acute stress response for self-optimizing mechatronic systems

Self-optimizing mechatronic systems react autonomously and flexibly to changing conditions. They are capable of learning and optimize their behavior throughout their life cycle. The paradigm of self-optimization is originally inspired by the behavior of biological systems. The key to the successful development of self-optimizing systems is a conceptual design process that precisely describes the desired system behavior. In the area of mechanical engineering, active principles based on physical effects such as friction or lever are widely used to concretize the construction structure and the behavior. The same approach can be found in the domain of software-engineering with software patterns such as the broker-pattern or the strategy pattern. However there is no appropriate design schema for the development of intelligent mechatronic systems covering the needs to fulfill the paradigm of self-optimization. This article proposes such a schema called Active Patterns for Self-Optimization. It is shown how a catalogue of active patterns can be derived from a set of four basic active patterns. This design approach is validated for a networked mechatronic system in a multiagent setting where the behavior is implemented according to a biologically inspired technique – the neuro-fuzzy learning method.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Mechatronics and Computer ClustersRed de Universidades con Carreras en Informática (RedUNCI

Active patterns for self-optimization : Schemes for the design of intelligent mechatronic systems

Self-optimizing mechatronic systems react autonomously and flexibly to changing conditions. They are capable of learning and optimize their behavior throughout their life cycle. The paradigm of self-optimization is originally inspired by the behavior of biological systems. The key to the successful development of self-optimizing systems is a conceptual design process that precisely describes the desired system behavior. In the area of mechanical engineering, active principles based on physical effects such as friction or lever are widely used to concretize the construction structure and the behavior. The same approach can be found in the domain of software-engineering with software patterns such as the broker-pattern or the strategy pattern. However there is no appropriate design schema for the development of intelligent mechatronic systems covering the needs to fulfill the paradigm of self-optimization. This article proposes such a schema called Active Patterns for Self-Optimization. It is shown how a catalogue of active patterns can be derived from a set of four basic active patterns. This design approach is validated for a networked mechatronic system in a multiagent setting where the behavior is implemented according to a biologically inspired technique – the neuro-fuzzy learning method.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Mechatronics and Computer ClustersRed de Universidades con Carreras en Informática (RedUNCI

Using alternatives to the car and risk of all-cause, cardiovascular and cancer mortality

Abstract: Objective: To investigate the associations between using alternatives to the car which are more active for commuting and non-commuting purposes and morbidity and mortality Methods: We conducted a prospective study using 358799 participants aged 37-73 from UK Biobank. Commute and non-commute travel were assessed at baseline in 2006-2010. We classified participants according to whether they relied exclusively on the car, or used alternative modes of transport that were more active at least some of the time. Main outcome measures were incident CVD and cancer, and CVD, cancer and all-cause mortality. We excluded events in the first two years and conducted analyses separately for those who regularly commuted and those who did not. Results: In maximally-adjusted models, regular commuters with more active patterns of travel on the commute had a lower risk of incident (HR 0.89, 95% CI 0.79 to 1.00) and fatal CVD (HR 0.70, 95% CI 0.51 to 0.95). Those regular commuters who also had more active patterns of non-commute travel had an even lower risk of fatal CVD (HR 0.57, 95% CI 0.39 to 0.85). Among those who were not regular commuters, more active patterns of travel were associated with a lower risk of all-cause mortality (HR 0.92, 95% CI 0.86 to 0.99). Conclusions: More active patterns of travel are associated with a reduced risk of incident and fatal CVD and all-cause mortality in adults. This is an important message for clinicians advising people about how to be physically active and reduce their risk of disease.JP, DO, SB and SS are supported by the Medical Research Council (Unit Programme Nos MC_UU_12015/1, MC_UU_12015/3 and MC_UU_12015/6) and KW is also supported by the British Heart Foundation (Intermediate Basic Science Research Fellowship grant No FS/12/58/29709). AAL is funded by the NIHR (RP 014-04-032), and the Public Health Policy Evaluation Unit are grateful for the support of the NIHR School of Public Health Research. This research was conducted using the UK Biobank resource (application No 20684). The work was also supported under the auspices of the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence at the University of Cambridge, for which funding from the British Heart Foundation, Economic and Social Research Council, Medical Research Council, National Institute for Health Research and the Wellcome Trust, under the auspices of the United Kingdom Clinical Research Collaboration, is gratefully acknowledged

A layered neural network with three-state neurons optimizing the mutual information

The time evolution of an exactly solvable layered feedforward neural network with three-state neurons and optimizing the mutual information is studied for arbitrary synaptic noise (temperature). Detailed stationary temperature-capacity and capacity-activity phase diagrams are obtained. The model exhibits pattern retrieval, pattern-fluctuation retrieval and spin-glass phases. It is found that there is an improved performance in the form of both a larger critical capacity and information content compared with three-state Ising-type layered network models. Flow diagrams reveal that saddle-point solutions associated with fluctuation overlaps slow down considerably the flow of the network states towards the stable fixed-points.Comment: 17 pages Latex including 6 eps-figure

Non-linear Pattern Matching with Backtracking for Non-free Data Types

Non-free data types are data types whose data have no canonical forms. For example, multisets are non-free data types because the multiset $\{a,b,b\}$ has two other equivalent but literally different forms $\{b,a,b\}$ and $\{b,b,a\}$. Pattern matching is known to provide a handy tool set to treat such data types. Although many studies on pattern matching and implementations for practical programming languages have been proposed so far, we observe that none of these studies satisfy all the criteria of practical pattern matching, which are as follows: i) efficiency of the backtracking algorithm for non-linear patterns, ii) extensibility of matching process, and iii) polymorphism in patterns. This paper aims to design a new pattern-matching-oriented programming language that satisfies all the above three criteria. The proposed language features clean Scheme-like syntax and efficient and extensible pattern matching semantics. This programming language is especially useful for the processing of complex non-free data types that not only include multisets and sets but also graphs and symbolic mathematical expressions. We discuss the importance of our criteria of practical pattern matching and how our language design naturally arises from the criteria. The proposed language has been already implemented and open-sourced as the Egison programming language

Scaling behavior of online human activity

The rapid development of Internet technology enables human explore the web and record the traces of online activities. From the analysis of these large-scale data sets (i.e. traces), we can get insights about dynamic behavior of human activity. In this letter, the scaling behavior and complexity of human activity in the e-commerce, such as music, book, and movie rating, are comprehensively investigated by using detrended fluctuation analysis technique and multiscale entropy method. Firstly, the interevent time series of rating behaviors of these three type medias show the similar scaling property with exponents ranging from 0.53 to 0.58, which implies that the collective behaviors of rating media follow a process embodying self-similarity and long-range correlation. Meanwhile, by dividing the users into three groups based their activities (i.e., rating per unit time), we find that the scaling exponents of interevent time series in three groups are different. Hence, these results suggest the stronger long-range correlations exist in these collective behaviors. Furthermore, their information complexities vary from three groups. To explain the differences of the collective behaviors restricted to three groups, we study the dynamic behavior of human activity at individual level, and find that the dynamic behaviors of a few users have extremely small scaling exponents associating with long-range anticorrelations. By comparing with the interevent time distributions of four representative users, we can find that the bimodal distributions may bring the extraordinary scaling behaviors. These results of analyzing the online human activity in the e-commerce may not only provide insights to understand its dynamic behaviors but also be applied to acquire the potential economic interest