A hot-wire surface gage for skin friction and separation detection measurements

A heated-element, skin-friction gage employing a very low thermal conductivity support is described. It is shown that the effective dimension of the gage in the stream direction in only 0.06 mm, including the effects of heat conduction in the supporting material. Because of its small size, the calibration of the gage is independent of the kind of boundary-layer flow (whether laminar or turbulent) and is insensitive to pressure gradients. Construction tolerances can be maintained so that a single universal calibration can be applied. Multiple gages, sufficiently closely spaced so as to interfere with each other, are shown to provide accurate determinations of the locations of the points of boundary-layer separation and reattachment

Synaptic tagging and capture : differential role of distinct calcium/calmodulin kinases in protein synthesis-dependent long-term potentiation

Weakly tetanized synapses in area CA1 of the hippocampus that ordinarily display long-term potentiation lasting ~3 h (called early-LTP) will maintain a longer-lasting change in efficacy (late-LTP) if the weak tetanization occurs shortly before or after strong tetanization of an independent, but convergent, set of synapses in CA1. The synaptic tagging and capture hypothesis explains this heterosynaptic influence on persistence in terms of a distinction between local mechanisms of synaptic tagging and cell-wide mechanisms responsible for the synthesis, distribution, and capture of plasticity-related proteins (PRPs). We now present evidence that distinct CaM kinase (CaMK) pathways serve a dissociable role in these mechanisms. Using a hippocampal brain-slice preparation that permits stable long-term recordings in vitro for >10 h and using hippocampal cultures to validate the differential drug effects on distinct CaMK pathways, we show that tag setting is blocked by the CaMK inhibitor KN-93 (2-[N-(2-hydroxyethyl)]-N-(4-methoxybenzenesulfonyl)amino-N-(4-chlorocinnamyl)-N-methylbenzylamine) that, at low concentration, is more selective for CaMKII. In contrast, the CaMK kinase inhibitor STO-609 [7H-benzimidazo(2,1-a)benz(de)isoquinoline-7-one-3-carboxylic acid] specifically limits the synthesis and/or availability of PRPs. Analytically powerful three-pathway protocols using sequential strong and weak tetanization in varying orders and test stimulation over long periods of time after LTP induction enable a pharmacological dissociation of these distinct roles of the CaMK pathways in late-LTP and so provide a novel framework for the molecular mechanisms by which synaptic potentiation, and possibly memories, become stabilized

A Modeling of Singing Voice Robust to Accompaniment Sounds and Its Application to Singer Identification and Vocal-Timbre-Similarity-Based Music Information Retrieval

This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection . The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity

Nonparametric Bayesian Dereverberation of Power Spectrograms Based on Infinite-Order Autoregressive Processes

This paper describes a monaural audio dereverberation method that operates in the power spectrogram domain. The method is robust to different kinds of source signals such as speech or music. Moreover, it requires little manual intervention, including the complexity of room acoustics. The method is based on a non-conjugate Bayesian model of the power spectrogram. It extends the idea of multi-channel linear prediction to the power spectrogram domain, and formulates a model of reverberation as a non-negative, infinite-order autoregressive process. To this end, the power spectrogram is interpreted as a histogram count data, which allows a nonparametric Bayesian model to be used as the prior for the autoregressive process, allowing the effective number of active components to grow, without bound, with the complexity of data. In order to determine the marginal posterior distribution, a convergent algorithm, inspired by the variational Bayes method, is formulated. It employs the minorization-maximization technique to arrive at an iterative, convergent algorithm that approximates the marginal posterior distribution. Both objective and subjective evaluations show advantage over other methods based on the power spectrum. We also apply the method to a music information retrieval task and demonstrate its effectiveness

Classification of Known and Unknown Environmental Sounds Based on Self-Organized Space Using a Recurrent Neural Network

Our goal is to develop a system to learn and classify environmental sounds for robots working in the real world. In the real world, two main restrictions pertain in learning. (i) Robots have to learn using only a small amount of data in a limited time because of hardware restrictions. (ii) The system has to adapt to unknown data since it is virtually impossible to collect samples of all environmental sounds. We used a neuro-dynamical model to build a prediction and classification system. This neuro-dynamical model can self-organize sound classes into parameters by learning samples. The sound classification space, constructed by these parameters, is structured for the sound generation dynamics and obtains clusters not only for known classes, but also unknown classes. The proposed system searches on the basis of the sound classification space for classifying. In the experiment, we evaluated the accuracy of classification for both known and unknown sound classes

Origin of four-fold anisotropy in square lattices of circular ferromagnetic dots

We discuss the four-fold anisotropy of in-plane ferromagnetic resonance (FMR) field $H_r$, found in a square lattice of circular Permalloy dots when the interdot distance $a$ gets comparable to the dot diameter $d$. The minimum $H_r$, along the lattice $$axes, and the maximum, along the$$ axes, differ by $\sim$ 50 Oe at $a/d$ = 1.1. This anisotropy, not expected in uniformly magnetized dots, is explained by a non-uniform magnetization \bm(\br) in a dot in response to dipolar forces in the patterned magnetic structure. It is well described by an iterative solution of a continuous variational procedure.Comment: 4 pages, 3 figures, revtex, details of analytic calculation and new references are adde

Higher arc nucleus-to-cytoplasm ratio during sleep in the superficial layers of the mouse cortex

The activity-regulated cytoskeleton associated protein Arc is strongly and quickly upregulated by neuronal activity, synaptic potentiation and learning. Arc entry in the synapse is followed by the endocytosis of glutamatergic AMPA receptors (AMPARs), and its nuclear accumulation has been shown in vitro to result in a small decline in the transcription of the GluA1 subunit of AMPARs. Since these effects result in a decline in synaptic strength, we asked whether a change in Arc dynamics may temporally correlate with sleep-dependent GluA1 down-regulation. We measured the ratio of nuclear to cytoplasmic Arc expression (Arc Nuc/Cyto) in the cerebral cortex of EGFP-Arc transgenic mice that were awake most of the night and then perfused immediately before lights on (W mice), or were awake most of the night and then allowed to sleep (S mice) or sleep deprived (SD mice) for the first 2 h of the light phase. In primary motor cortex (M1), neurons with high levels of nuclear Arc (High Arc cells) were present in all mice, but in these cells Arc Nuc/Cyto was higher in S mice than in W mice and, importantly, ~15% higher in S mice than in SD mice collected at the same time of day, ruling out circadian effects. Greater Arc Nuc/Cyto with sleep was observed in the superficial layers of M1, but not in the deep layers. In High Arc cells, Arc Nuc/Cyto was also ~15%–30% higher in S mice than in W and SD mice in the superficial layers of primary somatosensory cortex (S1) and cingulate cortex area 1 (Cg1). In High Arc Cells of Cg1, Arc Nuc/Cyto and cytoplasmic levels of GluA1 immunoreactivities in the soma were also negatively correlated, independent of behavioral state. Thus, Arc moves to the nucleus during both sleep and wake, but its nuclear to cytoplasmic ratio increases with sleep in the superficial layers of several cortical areas. It remains to be determined whether the relative increase in nuclear Arc contributes significantly to the overall decline in the strength of excitatory synapses that occurs during sleep. Similarly, it remains to be determined whether the entry of Arc into specific synapses is gated by sleep