LUNAR: Cellular automata for drifting data streams

With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods have to detect changes in the data distribution and adapt to these evolving conditions. Several emerging paradigms such as the so-called Smart Dust, Utility Fog, or Swarm Robotics are in need for efficient and scalable solutions in real-time scenarios, and where usually computing resources are constrained. Cellular automata, as low-bias and robust-to-noise pattern recognition methods with competitive classification performance, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature. In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. LUNAR is able to act as a real incremental learner while adapting to drifting conditions. Furthermore, LUNAR is highly interpretable, as its cellular structure represents directly the mapping between the feature space and the labels to be predicted. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods

Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning

Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learn- ing is the Spiking Neural Network, and some of them use an interesting population encod- ing scheme to transform the incoming stimuli into spikes. This study sheds lights on the key issue of this encoding scheme, the Gaussian receptive fields, and focuses on applying them as a pre-processing technique to any dataset in order to gain representativeness, and to boost the predictive performance of the stream learning methods. Experiments with synthetic and real data sets are presented, and lead to confirm that our approach can be applied successfully as a general pre-processing technique in many real cases

Rank aggregation for non-stationary data streams

The problem of learning over non-stationary ranking streams arises naturally, particularly in recommender systems. The rankings represent the preferences of a population, and the non-stationarity means that the distribution of preferences changes over time. We propose an algorithm that learns the current distribution of ranking in an online manner. The bottleneck of this process is a rank aggregation problem. We propose a generalization of the Borda algorithm for non-stationary ranking streams. As a main result, we bound the minimum number of samples required to output the ground truth with high probability. Besides, we show how the optimal parameters are set. Then, we generalize the whole family of weighted voting rules (the family to which Borda belongs) to situations in which some rankings are more reliable than others. We show that, under mild assumptions, this generalization can solve the problem of rank aggregation over non-stationary data streams

AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process

Multi-objective heuristics applied to robot task planning for inspection plants

Robotics are generally subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this regard the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by presenting experimental results obtained over realistic scenarios of two heuristic solvers (MOHS and NSGA-II) aimed at efficiently scheduling tasks in robotic swarms that collaborate together to accomplish a mission. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks whereas the relative execution order of such tasks within the schedule of certain robots is computed based on the Traveling Salesman Problem (TSP). Experimental results in three different deployment scenarios reveal the goodness of the proposed technique based on the Multi-objective Harmony Search algorithm (MOHS) in terms of Hypervolume (HV) and Coverage Rate (CR) performance indicators

On the Creation of Diverse Ensembles for Nonstationary Environments using Bio-inspired Heuristics

Recently the relevance of adaptive models for dynamic data environments has turned into a hot topic due to the vast number of sce- narios generating nonstationary data streams. When a change (concept drift) in data distribution occurs, the ensembles of models trained over these data sources are obsolete and do not adapt suitably to the new distribution of the data. Although most of the research on the field is focused on the detection of this drift to re-train the ensemble, it is widely known the importance of the diversity in the ensemble shortly after the drift in order to reduce the initial drop in accuracy. In a Big Data sce- nario in which data can be huge (and also the number of past models), achieving the most diverse ensemble implies the calculus of all possible combinations of models, which is not an easy task to carry out quickly in the long term. This challenge can be formulated as an optimization prob- lem, for which bio-inspired algorithms can play one of the key roles in these adaptive algorithms. Precisely this is the goal of this manuscript: to validate the relevance of the diversity right after drifts, and to un- veil how to achieve a highly diverse ensemble by using a self-learning optimization technique

Pathological gambling subtypes: A comparison of treatment-seeking and non-treatment-seeking samples from Brazil and Canada

Background: Pathological gambling (PG) is a heterogeneous disorder. The identification and characterization of PG subtypes could lead to tailored treatment approaches, which may, in turn, improve treatment outcomes. Objective: To investigate PG subtypes based on personality traits across two different cultural and clinical settings. Consistent with the Pathways Model, we hypothesized the presence of three subtypes (behaviorally conditioned — BC, emotionally vulnerable — EV, and antisocial impulsivist — AI). Methods: 140 PG adults from São Paulo, Brazil (SP sample) and 352 adults with PG (n = 214) or sub-clinical PG (n = 138) from Toronto, Canada (TO sample) completed the Temperament and Character Inventory (TCI). Latent-class analysis was used to investigate subtypes. Results: A 2-class solution was the best model for the pooled SP and TO samples. Class 1 presented a normative personality profile and was composed exclusively of participants from Toronto (BC subtype). Class 2 was characterized by high novelty seeking, high harm avoidance, and low self-directedness, and included participants from both SP and TO (EV subtype). When sub-clinical PGs were excluded from the analysis, a single-class solution better characterized the SP and TO samples. Conclusions: Our results suggest that PG severity, rather than community or clinical settings, may have an effect on PG subtypes. The generalizability of the results is limited by the demographic and clinical features of the selected samples. Future neurobiological studies may contribute to the categorization of subjects into PG subtypes based on different underlying biological pathways

Gender differences in the incidence of and risk factors for hip fracture: A 16-year longitudinal study in a southern European population

Objectives To analyze independently in men and women the incidence rate of and risk factors for hip fracture in a southern European population. Illiteracy, dementia, clinically significant depression and disability were factors to receive special emphasis. Study design A community sample of 4803 individuals aged over 55 years was assessed in a two-phase case-finding study in Zaragoza, Spain, and was followed up for 16 years. Medical history and psychiatric history were collected with standardized instruments, including the History and Aetiology Schedule, the Geriatric Mental State (GMS) scale, and a Risk Factors Questionnaire. Operational criteria were used to define covariates, including diagnostic criteria for both dementia and depression. The statistical analysis included calculations of incidence rate, IR; women/men incidence rate ratio (IRR); and Hazard Ratios (HR) in multivariate Cox proportional hazards regression models. Main outcome measures Cases of hip fracture (International Classification of Diseases, WHO) identified in the treating hospitals, validated by blinded researchers. Results Hip fractures were more frequent among women than men (IRR = 3.1). Illiteracy (HR = 1.55) and depression (HR = 1.44) increased the risk in women, and smoking (HR = 2.13) and disability in basic activities of daily living (HR = 3.14) increased the risk in men. Dementia was associated with an increased risk in an univariate analysis, but the association disappeared (power = 85% in men, 95% in women) when disability was included in the multivariate models. Conclusions The IR of hip fractures was three times higher among women. Illiteracy and clinically significant depression among women and active smoking and disability (HR = 3.14) among men independently increased the risk, but dementia did not

Simple multiplexing scheme for fibre optic grating sensor network

A new approach for the interrogation of a large number of fiber-optic grating sensors is proposed and demonstrated for a small number of sensors in which signal recovery is achieved by matching a receiving grating to its corresponding sensor. This technique is demonstrated for both quasi-static and periodic measurands, and the resolution achieved for a single sensor-receiving grating pair for quasi-static strain of 4.12µε

The Dynamical Behaviour of Test Particles in a Quasi-Spherical Spacetime and the Physical Meaning of Superenergy

We calculate the instantaneous proper radial acceleration of test particles (as measured by a locally defined Lorentzian observer) in a Weyl spacetime, close to the horizon. As expected from the Israel theorem, there appear some bifurcations with respect to the spherically symmetric case (Schwarzschild), which are explained in terms of the behaviour of the superenergy, bringing out the physical relevance of this quantity in the study of general relativistic systems.Comment: 14 pages, Latex. 4 figures. New references added. Typos corrected. To appear in Int. J. Theor. Phy