This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture. This optimized mapping extends the use of the NeuCube, which was initially designed for spatiotemporal brain data, to work on arbitrary stream data and to achieve a better accuracy of temporal pattern recognition, a better and earlier event prediction and a better understanding of complex temporal stream data through visualization of the NeuCube connectivity. The effect of the new mapping is demonstrated on three bench mark problems. The first one is early prediction of patient sleep stage event from temporal physiological data. The second one is pattern recognition of dynamic temporal patterns of traffic in the Bay Area of California and the last one is the Challenge 2012 contest data set. In all cases the use of the proposed mapping leads to an improved accuracy of pattern recognition and event prediction and a better understanding of the data when compared to traditional machine learning techniques or spiking neural network reservoirs with arbitrary mapping of the variables.

A Gaussian restricted Boltzmann machine (GRBM) is a Boltzmann machine defined on a bipartite graph and is an extension of usual restricted Boltzmann machines. A GRBM consists of two different layers: a visible layer composed of continuous visible variables and a hidden layer composed of discrete hidden variables. In this paper, we derive two different inference algorithms for GRBMs based on the naive mean-field approximation (NMFA). One is an inference algorithm for whole variables in a GRBM, and the other is an inference algorithm for partial variables in a GBRBM. We compare the two methods analytically and numerically and show that the latter method is better.

We study Nystr\"om type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nystr\"om Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.

Two big recent revolutions: machine learning technologies; such as “deep learning” in Artificial Intelligence (AI), and personal genome informatics in biomedical science, provide us with new opportunities for understanding human happiness. Our ongoing important challenges are to discover our own truly meaningful personal happiness with the aid of AI and personal genome technologies. We have been developing a personal genome information agent entitled MyFinder, which supports searching for our inherited talents and maximizes our potential for a meaningful life. In the MyFinder project, we have provided a crowd-sourced DIY (Do it yourself) genomics research platform and conducted various “citizen science” projects in health and wellness. In this paper, we discuss how machine learning technologies and personal genome informat-ics might contribute to happiness sciences. We introduce the “Social Intelligence Genomics and Empathy-Building Study” and report the preliminary results of applying deep learning and six other machine learning algorithms for predicting social intelligence levels from nine SNPs genetic profiles. We dis-cuss the possibilities and limitations of applying machine learning technologies for personal happiness trait prediction. We also discuss future AI challenges in the context of wellbeing computing.

We study the complexity of a combinatorial variant of the Shift Bribery problem in elections. In the standard Shift Bribery problem, we are given an election where each voter has a preference order over the set of candidates and where an outside agent, the briber, can pay each voter to rank the briber's favorite candidate a given number of positions higher. The goal is to ensure the victory of the briber's preferred candidate. The combinatorial variant of the problem, introduced in this paper, models settings where it is possible to affect the position of the preferred candidate in multiple votes, either positively or negatively, with a single bribery action. This variant of the problem is particularly interesting in the context of large-scale campaign management problems (which, from the technical side, are modeled as bribery problems). We show that, in general, the combinatorial variant of the problem is highly intractable; specifically, NP-hard, hard in the parameterized sense, and hard to approximate. Nevertheless, we provide parameterized algorithms and approximation algorithms for natural restricted cases.

Applications of robotic and wearable sensors based systems for human assistance or health monitoring have been gaining popularity in recent years. Among its diverse applications, therapeutic robotic systems have been utilized in the muscular physiotherapies for movement training, wrist and arm treatment for injuries and overexertions, and other therapies. Applications of wearable sensors for human assistance or health monitoring have been also gaining popularity in recent years. Wireless wearable sensor systems enable proactive personal health management and the ubiquitous monitoring of vital signs to keep an active watch on immediate health conditions. In this paper, we develop a system that consists of multiple wearable sensors, software agents and robots, where a robot has the intelligence to process its own observed data, the collected wearable sensor data, and to aggregate the information into a single compiled report. Our system is also able to detect severe abnormalities with the well-being of the monitored individual as detected by the sensors and to create immediate alerts. Our preliminary experimental results show that our system is accurate in detecting and monitoring basic human conditions. We posit that the approach of non-invasive monitoring, when combined with an alert system, will make this a desirable personalized well-being monitoring system in future health care.

Human conscious flows can alter brain states. Such brain activities modulate energy consumptions, which can be manifest in the BOLD effect in fMRI experiment. The goal of this study is to identify whether there is difference in such BOLD effects between experienced Tai Chi master in meditation state and normal control subjects. In this experiment, both the meditator and the controls using their conscious to lead a flow periodically circling in their brain in axial, sagittal, and coronal orientations inside a MRI scanner. The experimental results showed significant differences between the meditator and the controls. The most important one is that the meditator activates frontal medial cortex and precuneous regions without any visual excitation, while the controls only utilize visual cortex and precuneous regions without any frontal medial excitation. These seems suggest that for performing the same tasks, the meditator is in cognitive control state, while the controls are in spatial imagination state.

This paper describes one of the future images of the sleep monitoring system. The new technology should satisfy the following requirements: (1) noninvasive, (2) low cost and (3) long-term monitoring. What we propose here is the sleep monitoring system based on the human body vibrations sensed by the mattress type pressure sensors that gradually improves its estimation performance to the particular user by learning collected data and reconstructing its classifier.%In order to learn the data, however, the system needs the vibration data mapped to the appropriate sleep stages. As the solution to the problem, we use the existing approximate sleep stage estimation method. The experimental results reveal that (1)there is only a slightly difference between the accuracies of the two classifiers; the one trained the original dataset plus PSG based sleep stage labeled data; the other one trained the original dataset plus approximate sleep stage labeled data; (2 )Adding a particular user's several days data to the training data improves the accuracy of the original classifiers. The REM estimation accuracy is 87% in maximum. From those results, the contribution of this research is suggesting the way to personalize sleep estimation, and proving the effectiveness.

Two big recent revolutions: machine learning technologies; such as “deep learning” in Artificial Intelligence (AI), and personal genome informatics in biomedical science, provide us with new opportunities for understanding human happiness. Our ongoing important challenges are to discover our own truly meaningful personal happiness with the aid of AI and personal genome technologies. We have been developing a personal genome information agent entitled MyFinder, which supports searching for our inherited talents and maximizes our potential for a meaningful life. In the MyFinder project, we have provided a crowd-sourced DIY (Do it yourself) genomics research platform and conducted various “citizen science” projects in health and wellness. In this paper, we discuss how machine learning technologies and personal genome informat-ics might contribute to happiness sciences. We introduce the “Social Intelligence Genomics and Empathy-Building Study” and report the preliminary results of applying deep learning and six other machine learning algorithms for predicting social intelligence levels from nine SNPs genetic profiles. We dis-cuss the possibilities and limitations of applying machine learning technologies for personal happiness trait prediction. We also discuss future AI challenges in the context of wellbeing computing.

Well-being and mental health are directly associated with relationship status particularly in the context of relatedness and support. A key factor in relationship functioning is emotional arousal. We examine the interplay between emotional arousal manifested through acoustic and physiological cues and its association to relationship satisfaction. We propose a dynamical systems model to infer the within- and across-modality as well as the between-partner relations. Our results suggest that increased emotional regulation is negatively associated with relationship satisfaction and indicate that the proposed system consists a viable framework for analyzing such multimodal interrelations within romantic partners.