Multi-label Classification via Adaptive Resonance Theory-based Clustering

This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning

An Explanation of Episodic Tremor and Slow Slip Constrained by Crack-Seal Veins and Viscous Shear in Subduction Mélange

Episodic tremor and slow slip (ETS) occurs in the transition zone between the locked seismogenic zone and the deeper, stably sliding zone. Actual mechanisms of ETS are enigmatic, caused by lack of geological observations and limited spatial resolution of geophysical information from the ETS source. We report that quartz‐filled, crack‐seal shear and extension veins in subduction mélange record repeated low‐angle thrust‐sense frictional sliding and tensile fracturing at near‐lithostatic fluid pressures. Crack‐seal veins were coeval with viscous shear zones that accommodated deformation by pressure solution creep. The minimum time interval between thrusting events, determined from a kinetic model of quartz precipitation in shear veins, was less than a few years. This short recurrence time of low‐angle brittle thrusting at near‐lithostatic fluid overpressures within viscous shear zones may be explained by frequent release of accumulated strain by ETS

An explanation of episodic tremor and slow slip constrained by crack-seal veins and viscous shear in subduction mélange

Episodic tremor and slow slip (ETS) occurs in the transition zone between the locked seismogenic zone and the deeper, stably sliding zone. Actual mechanisms of ETS are enigmatic, caused by lack of geological observations and limited spatial resolution of geophysical information from the ETS source. We report that quartz‐filled, crack‐seal shear and extension veins in subduction mélange record repeated low‐angle thrust‐sense frictional sliding and tensile fracturing at near‐lithostatic fluid pressures. Crack‐seal veins were coeval with viscous shear zones that accommodated deformation by pressure solution creep. The minimum time interval between thrusting events, determined from a kinetic model of quartz precipitation in shear veins, was less than a few years. This short recurrence time of low‐angle brittle thrusting at near‐lithostatic fluid overpressures within viscous shear zones may be explained by frequent release of accumulated strain by ETS

Megathrust shear modulated by Albite Metasomatism in subduction mélanges

Aseismic megathrust slip downdip of the seismogenic zone is accommodated by either steady creep or episodic slow slip events (SSEs). However, the geological conditions defining the rheology of megathrust slip remain elusive. We examined exhumed subduction mélanges on Kyushu, Japan, which deformed at ∼370–500°C under greenschist to epidote‐amphibolite facies conditions, comparable to warm‐slab environments. The mélanges recorded fluid release and viscous shear localization associated with metasomatic reactions between juxtaposed metapelitic and metabasaltic rocks. Metasomatic reactions caused albitization of metapelite, resulting in depth‐dependent changes to megathrust rheology. In a mélange deformed at ∼370°C, very fine grained reaction products (metasomatic albite) facilitated grain boundary diffusion creep at stresses of ∼45 MPa, less than those in the surrounding metabasalt. Mineralogical and chemical changes during metasomatic reactions, and their field content, imply an onset of albite metasomatism at ∼350°C. Albite metasomatism therefore potentially contributed to decreased megathrust strength around the inferred thermally controlled base of the seismogenic zone. In a mélange deformed near the mantle wedge corner at ∼500°C, metasomatic reactions promoted local quartz vein formation and localized viscous shear at slow slip strain rates, during which the coarse‐grained metasomatic albite behaved as relatively rigid blocks in a viscous matrix. We suggest that albite metasomatism can facilitate changes in a megathrust slip mode with depth and may explain why slip mode changes from creep to SSEs with tremor with increasing depth

Quantum-inspired associative memories for incorporating emotion in a humanoid / Naoki Masuyama

Associative memory is essential to human activity. In the past decades, several artificial neural associativememorymodels have been developed and were expected to provide a new perspective for the modeling of the human brain, and these models were expected to form the basis for a robot to exhibit human-like behavior. However, the conventional models suffered from limited abilities. In 2006, Rigatos and Tzafestas applied the concept of Quantum Mechanics to associative memory, and introduced Quantum-Inspired Hopfield AssociativeMemory (QHAM). Although QHAM showed outstanding potential and superiority, it is limited as an auto-association model with binary state information. With regards to events in the real world, information representation by a binary or bipolar state is insufficient. Therefore, the ability and functional improvements of associative memory based on quantum-inspired model are defined as the main objectives in this thesis. In regards to the ability improvements in terms of memory capacity and noise tolerance, the quantum-inspired hetero-association models with batch/incremental learning algorithm are developed based on QHAM. Furthermore, the quantum-inspired complexvalued hetero-association models are considered to accommodate the high dimensional problems. Based on the results of experiments, it is shown that the quantum-inspired hetero-association models have outstanding abilities. In regards to the functional improvements, the emotion affected association model is developed in an interactive robot system based on the relationship between memory and emotion from the viewpoint of psychology and neuroscience, which is called the mood-congruency effect. The experimental results show that the emotion affected association model is able to associate the emotion dependent information to the robot similar with the mood-congruency effect

Personality affected robotic emotional model with associative memory for human-robot interaction

The decision making process in communication is affected by internal and external factors from dynamic environments. Humans can perform a variety of behaviors in a similar situation, unlike robots. This paper discusses human psychological phenomena during communication from the point of view of internal and external factors, such as perception, memory, and emotional information. Based on these, we introduce the personality affected robotic emotional model and the emotion affected associative memory model for the robot. We organize an interactive robot system to provide suitable decisions for the robot. Results from interactive communication experiments indicate that the robot is able to perform different actions based on internal and external factors

Kernel Bayesian ART and ARTMAP

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving “the plasticity–stability dilemma” in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes’ Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively

A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments. © 2019 World Scientific Publishing Company

Parameters Estimation in Topological Kernel Bayesian ART using Multi-objective Particle Swarm Optimization

Potentials in Topological Kernel Bayesian Adaptive Resonance Theory (TKBA) are advocated by the data specific parameters: kernel bandwidth σcim in correntropy induced metric (CIM) and kernel bandwidth σkbr in kernel Bayes' rule (KBR). This paper proposes a new calibration mechanism to implement Multi-objective Particle Swarm optimization (MOPSO) for parameters estimation in TKBA with the intention of searching for optimal values of parameters σcim and σkbr. Calibration mechanism is designed based on the measure of robustness, adaptability and the quality of the learned topological network. Two case studies has been empirically carried out using UCI real world dataset. Experiment results in case study I provide proof-of-concept of the proposed calibration mechanism. Case study II compares MOPSO calibrated TKBA with manual calibrated TKBA in terms of classification performance. Experiment results shows that MOPSO calibrated TKBA is able to enhance the capabilities of TKBA