1,341 research outputs found
Coupled thermo-mechanics of single-wall carbon nanotubes
The temperature-dependent transverse mechanical properties of single-walled
nanotubes are studied using a molecular mechanics approach. The stretching and
bond angle force constants describing the mechanical behaviour of the sp^{2}
bonds are resolved in the temperature range between 0 K and 1600 K, allowing to
identify a temperature dependence of the nanotubes wall thickness. We observe a
decrease of the stiffness properties (axial and shear Young's modulus) with
increasing temperatures, and an augmentation of the transverse Poisson's ratio,
with magnitudes depending on the chirality of the nanotube. Our closed-form
predictions compare well with existing Molecular Dynamics simulations.Comment: 15 pages, 4 figures. Accepted for Applied Physics Letter
Gamma rays and neutrinos from dark matter annihilation in galaxy clusters
The -ray and neutrino emissions from dark matter (DM) annihilation in
galaxy clusters are studied. After about one year operation of Fermi-LAT,
several nearby clusters are reported with stringent upper limits of GeV
-ray emission. We use the Fermi-LAT upper limits of these clusters to
constrain the DM model parameters. We find that the DM model distributed with
substructures predicted in cold DM (CDM) scenario is strongly constrained by
Fermi-LAT -ray data. Especially for the leptonic annihilation scenario
which may account for the excesses discovered by
PAMELA/Fermi-LAT/HESS, the constraint on the minimum mass of substructures is
of the level M, which is much larger than that expected
in CDM picture, but is consistent with a warm DM scenario. We further
investigate the sensitivity of neutrino detections of the clusters by IceCube.
It is found that neutrino detection is much more difficult than -rays.
Only for very heavy DM ( TeV) together with a considerable branching
ratio to line neutrinos the neutrino sensitivity is comparable with that of
-rays.Comment: 21 pages, 8 figures and 1 table; extended discussion about the
uncertainties of concentration and subhalo models, figures replotted for
better read; references updated; accepted for publication by Phys. Rev.
APNet2: High-quality and High-efficiency Neural Vocoder with Direct Prediction of Amplitude and Phase Spectra
In our previous work, we proposed a neural vocoder called APNet, which
directly predicts speech amplitude and phase spectra with a 5 ms frame shift in
parallel from the input acoustic features, and then reconstructs the 16 kHz
speech waveform using inverse short-time Fourier transform (ISTFT). APNet
demonstrates the capability to generate synthesized speech of comparable
quality to the HiFi-GAN vocoder but with a considerably improved inference
speed. However, the performance of the APNet vocoder is constrained by the
waveform sampling rate and spectral frame shift, limiting its practicality for
high-quality speech synthesis. Therefore, this paper proposes an improved
iteration of APNet, named APNet2. The proposed APNet2 vocoder adopts ConvNeXt
v2 as the backbone network for amplitude and phase predictions, expecting to
enhance the modeling capability. Additionally, we introduce a multi-resolution
discriminator (MRD) into the GAN-based losses and optimize the form of certain
losses. At a common configuration with a waveform sampling rate of 22.05 kHz
and spectral frame shift of 256 points (i.e., approximately 11.6ms), our
proposed APNet2 vocoder outperformed the original APNet and Vocos vocoders in
terms of synthesized speech quality. The synthesized speech quality of APNet2
is also comparable to that of HiFi-GAN and iSTFTNet, while offering a
significantly faster inference speed
Towards High-Quality and Efficient Speech Bandwidth Extension with Parallel Amplitude and Phase Prediction
Speech bandwidth extension (BWE) refers to widening the frequency bandwidth
range of speech signals, enhancing the speech quality towards brighter and
fuller. This paper proposes a generative adversarial network (GAN) based BWE
model with parallel prediction of Amplitude and Phase spectra, named AP-BWE,
which achieves both high-quality and efficient wideband speech waveform
generation. The proposed AP-BWE generator is entirely based on convolutional
neural networks (CNNs). It features a dual-stream architecture with mutual
interaction, where the amplitude stream and the phase stream communicate with
each other and respectively extend the high-frequency components from the input
narrowband amplitude and phase spectra. To improve the naturalness of the
extended speech signals, we employ a multi-period discriminator at the waveform
level and design a pair of multi-resolution amplitude and phase discriminators
at the spectral level, respectively. Experimental results demonstrate that our
proposed AP-BWE achieves state-of-the-art performance in terms of speech
quality for BWE tasks targeting sampling rates of both 16 kHz and 48 kHz. In
terms of generation efficiency, due to the all-convolutional architecture and
all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform
samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1
times faster than real-time on a single CPU. Notably, to our knowledge, AP-BWE
is the first to achieve the direct extension of the high-frequency phase
spectrum, which is beneficial for improving the effectiveness of existing BWE
methods.Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language
Processin
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