81 research outputs found

    Key signal contributions in photothermal deflection spectroscopy

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    We report on key signal contributions in photothermal deflection spectroscopy (PDS) of semiconductors at photon energies below the bandgap energy and show how to extract the actual absorption properties from the measurement data. To this end, we establish a rigorous computation scheme for the deflection signal including semi-analytic raytracing to analyze the underlying physical effects. The computation takes into account linear and nonlinear absorption processes affecting the refractive index and thus leading to a deflection of the probe beam. We find that beside the linear mirage effect, nonlinear absorption mechanisms make a substantial contribution to the signal for strongly focussed pump beams and sample materials with high two-photon absorption coefficients. For example, the measured quadratic absorption contribution exceeds 5% at a pump beam intensity of about 1.3×105  W/cm2{1.3}\times{10^{5}}\;{W}/{cm^{2}} in Si and at 5×104  W/cm2{5}\times{10^{4}}\;{W}/{cm^{2}} in GaAs. In addition, our method also includes thermal expansion effects as well as spatial gradients of the attenuation properties. We demonstrate that these effects result in an additional deflection contribution which substantially depends on the distance of the photodetector from the readout point. This distance dependent contribution enhances the surface related PDS signal up to two orders of magnitude and may be misinterpreted as surface absorption if not corrected in the analysis of the measurement data. We verify these findings by PDS measurements on crystalline silicon at a wavelength of 1550 nm and provide guidelines how to extract the actual attenuation coefficient from the PDS signal.Comment: 10 pages, 16 figures, submitted to Journal of Applied Physiv

    New concept single screw compressors and their manufacture technology

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    Single screw compressors were generally acknowledged as one of the nearly perfect machines by compressor researchers and manufacturers. However the rapid wear of the star- wheel in a single screw compressor during operation is a key reason why it hasn’t previously joined the main current compressors’ market. After more than ten years of effective work, the authors of this paper have proposed a new concept single screw compressor whose mesh-couple profile is enveloped with multi-column. Also a new design method and manufacture equipment for this kind of compressor have been developed and are described in this paper. A lot of prototype tests and a long period of industrial operations under full loading conditions have shown that the mesh-couple profiles of the new concept single compressors have excellent anti- wearness

    Two-Photon Rabi Splitting in a Coupled System of a Nanocavity and Exciton Complexes

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    Two-photon Rabi splitting in a cavity-dot system provides a basis for multi-qubit coherent control in quantum photonic network. Here we report on two-photon Rabi splitting in a strongly coupled cavity-dot system. The quantum dot was grown intentionally large in size for large oscillation strength and small biexciton binding energy. Both exciton and biexciton transitions couple to a high quality factor photonic crystal cavity with large coupling strengths over 130 ÎĽ\mueV. Furthermore, the small binding energy enables the cavity to simultaneously couple with two exciton states. Thereby two-photon Rabi splitting between biexciton and cavity is achieved, which can be well reproduced by theoretical calculations with quantum master equations.Comment: 12 pages, 4 figure

    Enhanced strong interaction between nanocavities and p-shell excitons beyond the dipole approximation

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    Large coupling strengths in exciton-photon interactions are important for the quantum photonic network, while strong cavity–quantum dot interactions have been focused on s-shell excitons with small coupling strengths. Here we demonstrate strong interactions between cavities and p-shell excitons with a great enhancement by the in situ wave-function control. The p-shell excitons are demonstrated with much larger wave-function extents and nonlocal interactions beyond the dipole approximation. Then the interaction is tuned from the nonlocal to the local regime by the wave function shrinking, during which the enhancement is obtained. A large coupling strength of 210     μ eV has been achieved, indicating the great potential of p-shell excitons for coherent information exchange. Furthermore, we propose a distributed delay model to quantitatively explain the coupling strength variation, revealing the intertwining of excitons and photons beyond the dipole approximation

    The gray matter volume of the amygdala is correlated with the perception of melodic intervals: a voxel-based morphometry study

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    Music is not simply a series of organized pitches, rhythms, and timbres, it is capable of evoking emotions. In the present study, voxel-based morphometry (VBM) was employed to explore the neural basis that may link music to emotion. To do this, we identified the neuroanatomical correlates of the ability to extract pitch interval size in a music segment (i.e., interval perception) in a large population of healthy young adults (N = 264). Behaviorally, we found that interval perception was correlated with daily emotional experiences, indicating the intrinsic link between music and emotion. Neurally, and as expected, we found that interval perception was positively correlated with the gray matter volume (GMV) of the bilateral temporal cortex. More important, a larger GMV of the bilateral amygdala was associated with better interval perception, suggesting that the amygdala, which is the neural substrate of emotional processing, is also involved in music processing. In sum, our study provides one of first neuroanatomical evidence on the association between the amygdala and music, which contributes to our understanding of exactly how music evokes emotional responses

    Cross-movie prediction of individualized functional topography

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    Participant-specific, functionally defined brain areas are usually mapped with functional localizers and estimated by making contrasts between responses to single categories of input. Naturalistic stimuli engage multiple brain systems in parallel, provide more ecologically plausible estimates of real-world statistics, and are friendly to special populations. The current study shows that cortical functional topographies in individual participants can be estimated with high fidelity from naturalistic stimuli. Importantly, we demonstrate that robust, individualized estimates can be obtained even when participants watched different movies, were scanned with different parameters/ scanners, and were sampled from different institutes across the world. Our results create a foundation for future studies that allow researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate high-level cognitive functions across datasets from laboratories worldwide

    Modeling naturalistic face processing in humans with deep convolutional neural networks

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    Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features

    A computational model of shared fine-scale structure in the human connectome

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    <div><p>Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.</p></div
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