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 $\gamma$-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 $\gamma$-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 $\gamma$-ray data. Especially for the leptonic annihilation scenario which may account for the $e^{\pm}$ excesses discovered by PAMELA/Fermi-LAT/HESS, the constraint on the minimum mass of substructures is of the level $10^2-10^3$ M$_{\odot}$, 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 $\gamma$-rays. Only for very heavy DM ($\sim 10$ TeV) together with a considerable branching ratio to line neutrinos the neutrino sensitivity is comparable with that of $\gamma$-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