Flexible resonant acoustic sensors have attracted substantial attention as an essential component for intuitive human-machine interaction (HMI) in the future voice user interface (VUI). Several researches have been reported by mimicking the basilar membrane but still have dimensional drawback due to limitation of controlling a multifrequency band and broadening resonant spectrum for full-cover phonetic frequencies. Here, highly sensitive piezoelectric mobile acoustic sensor (PMAS) is demonstrated by exploiting an ultrathin membrane for biomimetic frequency band control. Simulation results prove that resonant bandwidth of a piezoelectric film can be broadened by adopting a lead-zirconate-titanate (PZT) membrane on the ultrathin polymer to cover the entire voice spectrum. Machine learning–based biometric authentication is demonstrated by the integrated acoustic sensor module with an algorithm processor and customized Android app. Last, exceptional error rate reduction in speaker identification is achieved by a PMAS module with a small amount of training data, compared to a conventional microelectromechanical system microphone.

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11Face-selective neurons are observed in the primate visual pathway and are considered the basis of facial 12 recognition in the brain. However, it is debated whether this neuronal selectivity can arise spontaneously, 13 or requires training from visual experience. Here, we show that face-selective neurons arise 14 spontaneously in random feedforward networks in the absence of learning. Using biologically inspired 15 deep neural networks, we found that face-selective neurons arise under three different network conditions: 16 one trained using non-face natural images, one randomized after being trained, and one never trained. 17 We confirmed that spontaneously emerged face-selective neurons show the biological view-point-18 invariant characteristics observed in monkeys. Such neurons suddenly vanished when feedforward 19 weight variation declined to a certain level. Our results suggest that innate face-selectivity originates from 20 statistical variation of the feedforward projections in hierarchical neural networks. 22The ability to identify and recognize faces is a crucial function in visual-priority social animals such as 23 humans and other primates, and is thought to originate from neuronal tuning at a single or multi-neuronal 24 level. Neurons that selectively respond to faces (face-selective neurons) are observed to occur in the 25 inferior temporal cortex (IT) 1-6 , superior temporal sulcus (STS) 7-10 , and fusiform face area (FFA) [11][12][13][14][15] in 26 the primate brain ( Fig. 1A). Several contradictory observations on the origin of face-selective neurons in 27 infant animals have been reported, raising two different scenarios for the development of this intriguing 28 functional tuning. 29The first scenario is that visual experience develops face-selective neurons. A study using 30 functional magnetic resonance imaging (fMRI) to examine FFA in monkeys reported that the category of 31 selective neuronal activity observed, depends greatly on what a subject had experienced in its lifetime 16 . 32Another fMRI study of IT in monkeys reported that robust tuning of face-selective neurons is not observed 33 until one year after birth 5 and that face-selectivity relies on experience during the early infant years. 34Furthermore, it was reported that monkeys raised without face exposure did not develop normal face-35 selective domains 17 . These results suggest that face-selective neurons are developed from training with 36 visual experience.37However, the other view suggests that face-selectivity can innately arise without visual 38 experience 18-21 . It was observed that human infants behaviorally prefer to look face-like objects rather 39 than non-face ones [22][23][24] , implying that face-encoding units may already exist in infants. It was also 40 reported that adult humans with no visual experience have category-selective domains including face, in 41 the ventral visual cortex 19 . In addition, a recent fMRI study of infant animals reported that face-selective 42 neurons are observed with movie st...

Inferences about unobserved random variables, such as future observations, random effects and latent variables, are of interest. In this paper, to make probability statements about unobserved random variables without assuming priors on fixed parameters, we propose the use of the confidence distribution for fixed parameters. We focus on their interval estimators and related probability statements. In random-effect models, intervals can be formed either for future (yet-tobe-realised) random effects or for realised values of random effects. The consistency of intervals for these two cases requires different regularity conditions. Via numerical studies, their finite sampling properties are investigated.

We propose a Bayesian approach for estimating the hazard functions under the constraint of a monotone hazard ratio. We construct a model for the monotone hazard ratio utilizing the Cox's proportional hazards model with a monotone time-dependent coefficient. To reduce computational complexity, we use a signed gamma process prior for the time-dependent coefficient and the Bayesian bootstrap prior for the baseline hazard function. We develop an efficient MCMC algorithm and illustrate the proposed method on simulated and real data sets.

Highlights d Orthogonal organization of visual tuning maps is observed in both V1 and the retina d Cortical tuning maps are correlated with the profile of ON-OFF feedforward projections d The profile of ON-OFF receptive fields varies periodically across the V1 and retina d Regularly structured retinal mosaics initiate the orthogonal tiling of the

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In this article, 1 we change the affiliation of the first author as the next line, and add an additional acknowledgement.