Improved Noisy Student Training for Automatic Speech Recognition

Recently, a semi-supervised learning method known as "noisy student training" has been shown to improve image classification performance of deep networks significantly. Noisy student training is an iterative self-training method that leverages augmentation to improve network performance. In this work, we adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method. We find effective methods to filter, balance and augment the data generated in between self-training iterations. By doing so, we are able to obtain word error rates (WERs) 4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h subset of LibriSpeech as the supervised set and the rest (860h) as the unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h (4.74%/12.20%) and LibriSpeech (1.9%/4.1%).Comment: 5 pages, 5 figures, 4 tables; v2: minor revisions, reference adde

Multilabel Classification Based on Graph Neural Networks

Typical Laplacian embedding focuses on building Laplacian matrices prior to minimizing weights of connected graph components. However, for multilabel problems, it is difficult to determine such Laplacian graphs owing to multiple relations between vertices. Unlike typical approaches that require precomputed Laplacian matrices, this chapter presents a new method for automatically constructing Laplacian graphs during Laplacian embedding. By using trace minimization techniques, the topology of the Laplacian graph can be learned from input data, subsequently creating robust Laplacian embedding and influencing graph convolutional networks. Experiments on different open datasets with clean data and Gaussian noise were carried out. The noise level ranged from 6% to 12% of the maximum value of each dataset. Eleven different multilabel classification algorithms were used as the baselines for comparison. To verify the performance, three evaluation metrics specific to multilabel learning are proposed because multilabel learning is much more complicated than traditional single-label settings; each sample can be associated with multiple labels. The experimental results show that the proposed method performed better than the baselines, even when the data were contaminated by noise. The findings indicate that the proposed method is reliably robust against noise

Coupling and stacking order of ReS2 atomic layers revealed by ultralow-frequency Raman spectroscopy

We investigate the ultralow-frequency Raman response of atomically thin ReS2, a special type of two-dimensional (2D) semiconductors with unique distorted 1T structure. Bilayer and few-layer ReS2 exhibit rich Raman spectra at frequencies below 50 cm-1, where a panoply of interlayer shear and breathing modes are observed. The emergence of these interlayer phonon modes indicate that the ReS2 layers are coupled and stacked orderly, in contrast to the general belief that the ReS2 layers are decoupled from one another. While the interlayer breathing modes can be described by a linear chain model as in other 2D layered crystals, the shear modes exhibit distinctive behavior due to the in-plane lattice distortion. In particular, the two shear modes in bilayer ReS2 are non-degenerate and well separated in the Raman spectrum, in contrast to the doubly degenerate shear modes in other 2D materials. By carrying out comprehensive first-principles calculations, we can account for the frequency and Raman intensity of the interlayer modes, and determine the stacking order in bilayer ReS2

Parathyroid adenoma with rare severe pathological osteolytic lesion: a case report and literature review

Parathyroid adenomas are benign proliferative disorders of parathyroid glands. Patients typically exhibit hyperparathyroidism and elevated serum calcium levels due to elevated levels of parathyroid hormone (PTH). We report a newly diagnosed case of a rare pathological osteolytic lesion. Radiological evaluation revealed multiple bony lesions in multiple parts of the pelvis, vertebral body, and spinous process, suggesting hematological neoplasms or bone marrow metastatic carcinoma. The morphology revealed many abnormal cells in the bone marrow smear. Furthermore, serum calcium and PTH levels were significantly increased compared to normal levels. Doppler color ultrasound showed a thyroid mass (left), suspected parathyroid adenoma, thyroid, and isthmus nodular goiter (right). The patient underwent bilateral neck exploration with parathyroidectomy, and serum calcium and PTH levels significantly decreased on the second day after surgery and had a surgical cure

Methyl 5-hy­droxy-3-phenyl-1,2-oxazolidine-5-carboxyl­ate

In the title compound, C11H13NO4, the isoxazolidine ring has an envelope conformation with the O atom as the flap. In the crystal, mol­ecules are liked via N—H⋯O and bifurcated O—H⋯(O,N) hydrogen bonds forming chains propagating along [010]. There are also C—H⋯O inter­actions present