Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain

In this paper, we show that we can apply probabilistic spatiotemporal macroblock filtering (PSMF) and partial decoding processes to effectively detect and track multiple objects in real time in H.264|AVC bitstreams with stationary background. Our contribution is that our method cannot only show fast processing time but also handle multiple moving objects that are articulated, changing in size or internally have monotonous color, even though they contain a chaotic set of non-homogeneous motion vectors inside. In addition, our partial decoding process for H.264|AVC bitstreams enables to improve the accuracy of object trajectories and overcome long occlusion by using extracted color information.Comment: SPIE Real-Time Image and Video Processing Conference 200

Fractal-driven distortion of resting state functional networks in fMRI: a simulation study

Fractals are self-similar and scale-invariant patterns found ubiquitously in nature. A lot of evidences implying fractal properties such as 1/f power spectrums have been also observed in resting state fMRI time series. To explain the fractal behavior in rs-fMRI, we have proposed the fractal-based model of resting state hemodynamic response function (rs-HRF) whose properties can be summarized by a fractal exponent. Here we show, through a simulation studies, that the fractal behavior of cerebral hemodynamics may cause significant distortion of network properties between neuronal activities and BOLD signals. We simulated neuronal population activities based on the stochastic neural field model from the Macaque brain network, and then obtained their corresponding BOLD signals by convolving them with the rs-HRF filter. The precision of centrality estimated in each node was deteriorated overall in three networks based on transfer entropy, mutual information, and Pearson correlation; particularly the distortion of transfer entropy was more sensitive to the standard deviation of fractal exponents. A node with high centrality was resilient to desynchronized fractal dynamics over all frequencies while a node with small centrality exhibited huge distortion of both wavelet correlation and centrality over low frequencies. This theoretical expectation indicates that the difference of fractal exponents between brain regions leads to discrepancy of statistical network properties, especially at nodes with small centrality, between neuronal activities and BOLD signals, and that the traditional definitions of resting state functional connectivity may not effectively reflect the dynamics of spontaneous neuronal activities.Comment: The 3rd Biennial Conference on Resting State Brain Connectivit

Fractal analysis of resting state functional connectivity of the brain

A variety of resting state neuroimaging data tend to exhibit fractal behavior where its power spectrum follows power-law scaling. Resting state functional connectivity is significantly influenced by fractal behavior which may not directly originate from neuronal population activities of the brain. To describe the fractal behavior, we adopted the fractionally integrated process (FIP) model instead of the fractional Gaussian noise (FGN) since the FIP model covers more general aspects of fractality than the FGN model. We also introduce a novel concept called the nonfractal connectivity which is defined as the correlation of short memory independent of fractal behavior, and compared it with the fractal connectivity which is an asymptotic wavelet correlation. We propose several wavelet-based estimators of fractal connectivity and nonfractal connectivity for a multivariate fractionally integrated noise (mFIN). The performance of these estimators was evaluated through simulation studies and the analyses of resting state functional MRI data of the rat brain.Comment: The 2012 International Joint Conference on Neural Network

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.Comment: 14 pages, 6 figures, 6 tables, 2 algorithm

Capacitive Heart-Rate Sensing on Touch Screen Panel with Laterally Interspaced Electrodes

It is demonstrated that the heart-rate can be sensed capacitively on a touch screen panel (TSP) together with touch signals. The existing heart-rate sensing systems measure blood pulses by tracing the amount of light reflected from blood vessels during a number of cardiac cycles. This type of sensing system requires a considerable amount of power and space to be implemented in multi-functional mobile devices such as smart phones. It is found that the variation of the effective dielectric constant of finger stemming from the difference of systolic and diastolic blood flows can be measured with laterally interspaced top electrodes of TSP. The spacing between a pair of non-adjacent top electrodes turns out to be wide enough to distinguish heart-rate signals from noises. With the aid of fast Fourier transform, the heart-rate can be extracted reliably, which matches with the one obtained by actually counting heart beats on the wrist

Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently

Long memory model of resting state functional MRI

<h3>In the latest years momentous advance has been made in understanding the endogenous brain dynamics from resting state functional MRI (rs-fMRI) signals. An rs-fMRI signal tends to have long memory in time as well as the $1/f$ power spectrum at low frequencies. A few statistical models of rs-fMRI time series, such as fractional Gaussian noise (FGN), had been proposed to describe such properties called the fractal behavior. Nonetheless, the long memory properties have not been elucidated by the underlying physical mechanism. In addition, how such properties have an impact on large-scale functional networks of the brain has been unclear. This thesis develops not only a parsimonious model of long memory in rs-fMRI, which provides us hypothetical ideas on these unresolved issues, but also advanced techniques for estimating intrinsic functional connectivity among brain regions hidden beyond the long memory phenomenon of rs-fMRI signals. </h3><div><div>The long memory model of rs-fMRI was constructed by extending the present models of cerebral hemodynamics which describe the association between synaptic activities and fMRI signals. This model empowers us to deduce a rigorous hemodynamic condition that brings about long memory in rs-fMRI time series, and has essential implication on resting state brain dynamics. First, the impulse hemodynamic response to resting state brain activity may have considerably different shape from the typical hemodynamic response function corresponding to evoked state. The variability of hemodynamic responses directs us to hypothesize the history dependent excitability of hemodynamic response such that the hemodynamic state is subordinate to the history of brain activities. Second, the nonlinearity of hemodynamics has little influence on long memory properties in rs-fMRI data. Third, a fractionally integrated (FI) process can be taken into account as a novel statistical model of rs-fMRI time series since it is suitable for the long memory model of hemodynamic response. Lastly, the heterogeneity of fractal behavior among brain regions incurs significant divergence in both functional connectivity and information flow between rs-fMRI signals and the corresponding spontaneous neuronal activities. </div><div><br></div><div>To cope with the fractal-driven connectivity distortion in rs-fMRI, nonfractal connectivity was proposed as a novel concept of resting state functional connectivity. It is defined as the correlation of nonfractal constituents of two rs-fMRI time series that are independent of fractal behavior, and is comparable to the fractal connectivity defined as the convergence of wavelet correlation. Although the nonfractal connectivity is not akin to correlation of neuronal population activities, it is capable of efficaciously mitigating the inaccuracy of functional connectivity estimation attributed to fractal behavior. A diversity of wavelet-based estimators for both nonfractal connectivity and fractal connectivity were developed and verified through simulation studies. Moreover, a multivariate method was suggested as a robust estimator of memory parameter which is resilient to severe signal contamination. This fractal-based approach to resting state functional connectivity has been effectively exploited for the analyses of both human and animal brain. These applications demonstrate that the fractal-based analysis is instrumental in revealing the between-group difference in functional connectivity.</div><div><br></div><div>In consequence, all these results may give valuable insights on the scientific implication of fractal behavior on functional connectivity, and lead to further exploration of endogenous brain dynamics beyond fractal behavior of rs-fMRI.</div></div