Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition

Most of the research on data-driven speech representation learning has focused on raw audios in an end-to-end manner, paying little attention to their internal phonological or gestural structure. This work, investigating the speech representations derived from articulatory kinematics signals, uses a neural implementation of convolutive sparse matrix factorization to decompose the articulatory data into interpretable gestures and gestural scores. By applying sparse constraints, the gestural scores leverage the discrete combinatorial properties of phonological gestures. Phoneme recognition experiments were additionally performed to show that gestural scores indeed code phonological information successfully. The proposed work thus makes a bridge between articulatory phonology and deep neural networks to leverage informative, intelligible, interpretable,and efficient speech representations.Comment: Submitted to 2022 Interspeec

Validation of Quasi-Invariant Ice Cloud Radiative Quantities with MODIS Satellite-Based Cloud Property Retrievals

Similarity relations applied to ice cloud radiance calculations are theoretically analyzed and numerically validated. If t(1v) and t(1vg) are conserved where t is optical thickness, v the single-scattering albedo, and g the asymmetry factor, it is possible that substantially different phase functions may give rise to similar radiances in both conservative and non-conservative scattering cases, particularly in the case of large optical thicknesses. In addition to theoretical analysis, this study uses operational ice cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Collection5 (C5) and Collection 6 (C6) cloud property products to verify radiative similarity relations. It is found that, if the MODIS C5 and C6 ice cloud optical thickness values are multiplied by their respective (1wg)factors, the resultant products referred to as the effective optical thicknesses become similar with their ratio values around unity. Furthermore, the ratios of the C5 and C6 ice cloud effective optical thicknesses display an angular variation pattern similar to that of the corresponding ice cloud phase function ratios. The MODIS C5 and C6 values of ice cloud similarity parameter, defined as [(1w)(1(exp. 1/2)wg)]12, also tend to be similar

Spectral and Timing Analysis of NuSTAR and Swift/XRT Observations of the X-Ray Transient MAXI J0637-430

We present results for the first observed outburst from the transient X-ray binary source MAXI J0637-430. This study is based on eight observations from the Nuclear Spectroscopic Telescope Array (NuSTAR) and six observations from the Neil Gehrels Swift Observatory X-Ray Telescope (Swift/XRT) collected from 2019 November 19 to 2020 April 26 as the 3-79 keV source flux declined from 8.2 × 10-10 to 1.4 × 10-12 erg cm-2 s-1. We see the source transition from a soft state with a strong disk-blackbody component to a hard state dominated by a power-law or thermal Comptonization component. NuSTAR provides the first reported coverage of MAXI J0637-430 above 10 keV, and these broadband spectra show that a two-component model does not provide an adequate description of the soft-state spectrum. As such, we test whether blackbody emission from the plunging region could explain the excess emission. As an alternative, we test a reflection model that includes a physical Comptonization continuum. Finally, we also test a spectral component based on reflection of a blackbody illumination spectrum, which can be interpreted as a simple approximation to the reflection produced by returning disk radiation due to the bending of light by the strong gravity of the black hole. We discuss the physical implications of each scenario and demonstrate the value of constraining the source distance

Quantitative phase imaging through an ultra-thin lensless fiber endoscope

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications