The Effects of Ti addition and High Cu/Sn ratio on tube type (Nb,Ta)3Sn strands and a new type of Strand designed to reduced unreacted Nb Ratio

In this work we report the properties of two tube type Ta doped Nb3Sn strands: one strand was additionally Ti doped by way of a Sn-Ti alloy core, and the other had high Cu/Sn ratio within the filaments. Higher irreversibility field (Birr) was obtained on the quaternary strand with respect to the (Nb-7.5wt.%Ta)3Sn strand. High Cu/Sn ratio decreased the amount of coarse grain (CG) formation, but also degraded the layer Jc of the tube type strand by depressing the Sn content in the fine grain (FG) layer. A new type of strand, the subelement of which is composed of 7 bare Cu-Sn cored Nb tube filaments, was designed with the aim to reduce the unreacted Nb area fractions. The test results of the first experimental strand are reported. The unreacted Nb ratio is reduced relative to normal tube type strands and the FG area fraction is improved. The unique structure of this strand makes it also possible to improve the stoichiometry of FG and reduce the effective diameter (deff).This work was supported in part by the U.S. Dept. of Energy, Office of High Energy Physics, under Grants No. DE-FG02-95ER40900 (OSU) and a DOE SBIR.Ti addition via Sn-Ti alloy improves the Birr of (Nb,Ta)3Sn tube type strands and thus benefits high field Jc. Enhancing Cu/Sn ratio within the filaments reduces the formation of CG, but also decreases the Sn content in the FG layer and thus is not beneficial for the layer Jc. A new type of strand was designed. This type of strand has smaller unreacted Nb ratio with respect to the normal tube type strands, and also achieves better stoichiometry and Birr than normal Nb3Sn strands

LightGrad: Lightweight Diffusion Probabilistic Model for Text-to-Speech

Recent advances in neural text-to-speech (TTS) models bring thousands of TTS applications into daily life, where models are deployed in cloud to provide services for customs. Among these models are diffusion probabilistic models (DPMs), which can be stably trained and are more parameter-efficient compared with other generative models. As transmitting data between customs and the cloud introduces high latency and the risk of exposing private data, deploying TTS models on edge devices is preferred. When implementing DPMs onto edge devices, there are two practical problems. First, current DPMs are not lightweight enough for resource-constrained devices. Second, DPMs require many denoising steps in inference, which increases latency. In this work, we present LightGrad, a lightweight DPM for TTS. LightGrad is equipped with a lightweight U-Net diffusion decoder and a training-free fast sampling technique, reducing both model parameters and inference latency. Streaming inference is also implemented in LightGrad to reduce latency further. Compared with Grad-TTS, LightGrad achieves 62.2% reduction in paramters, 65.7% reduction in latency, while preserving comparable speech quality on both Chinese Mandarin and English in 4 denoising steps.Comment: Accepted by ICASSP 202

ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs

In this paper, we present ZeroPrompt (Figure 1-(a)) and the corresponding Prompt-and-Refine strategy (Figure 3), two simple but effective \textbf{training-free} methods to decrease the Token Display Time (TDT) of streaming ASR models \textbf{without any accuracy loss}. The core idea of ZeroPrompt is to append zeroed content to each chunk during inference, which acts like a prompt to encourage the model to predict future tokens even before they were spoken. We argue that streaming acoustic encoders naturally have the modeling ability of Masked Language Models and our experiments demonstrate that ZeroPrompt is engineering cheap and can be applied to streaming acoustic encoders on any dataset without any accuracy loss. Specifically, compared with our baseline models, we achieve 350 $\sim$ 700ms reduction on First Token Display Time (TDT-F) and 100 $\sim$ 400ms reduction on Last Token Display Time (TDT-L), with theoretically and experimentally equal WER on both Aishell-1 and Librispeech datasets.Comment: accepted by interspeech 202

Fast-U2++: Fast and Accurate End-to-End Speech Recognition in Joint CTC/Attention Frames

Recently, the unified streaming and non-streaming two-pass (U2/U2++) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy and latency. In this paper, we present fast-U2++, an enhanced version of U2++ to further reduce partial latency. The core idea of fast-U2++ is to output partial results of the bottom layers in its encoder with a small chunk, while using a large chunk in the top layers of its encoder to compensate the performance degradation caused by the small chunk. Moreover, we use knowledge distillation method to reduce the token emission latency. We present extensive experiments on Aishell-1 dataset. Experiments and ablation studies show that compared to U2++, fast-U2++ reduces model latency from 320ms to 80ms, and achieves a character error rate (CER) of 5.06% with a streaming setup.Comment: 5 pages, 3 figure

TrimTail: Low-Latency Streaming ASR with Simple but Effective Spectrogram-Level Length Penalty

In this paper, we present TrimTail, a simple but effective emission regularization method to improve the latency of streaming ASR models. The core idea of TrimTail is to apply length penalty (i.e., by trimming trailing frames, see Fig. 1-(b)) directly on the spectrogram of input utterances, which does not require any alignment. We demonstrate that TrimTail is computationally cheap and can be applied online and optimized with any training loss or any model architecture on any dataset without any extra effort by applying it on various end-to-end streaming ASR networks either trained with CTC loss [1] or Transducer loss [2]. We achieve 100 $\sim$ 200ms latency reduction with equal or even better accuracy on both Aishell-1 and Librispeech. Moreover, by using TrimTail, we can achieve a 400ms algorithmic improvement of User Sensitive Delay (USD) with an accuracy loss of less than 0.2.Comment: submitted to ICASSP 202

Analysis of the clinicopathological characteristics and prognosis of triple-positive breast cancer and HER2-positive breast cancer—A retrospective study

BackgroundAdjuvant chemotherapy and targeted therapy have become standard postoperative therapeutic modalities for human epidermal growth factor receptor 2 (HER2)-positive breast cancer(HER2-positive,HR-negative), including triple-positive breast cancer(HER2-positive,HR-positive). However, these two types of breast cancer differ in terms of pathogenesis. This article analyzes these two types of breast cancer by comparing their prognoses.MethodsThe clinicopathological characteristics of 135 patients, including 60 patients with triple-positive breast cancer and 75 patients with HER2-positive breast cancer, were analyzed to compare the disease-free survival (DFS) and overall survival (OS) of the two groups over a 5-year period. A multifactorial Cox risk model was constructed by grouping age, menstrual status, maximum tumor diameter, number of lymph node metastases, pathological staging, and Ki-67 staining results. All statistical data were analyzed in detail using SPSS25.0 statistical software.ResultsThe 5-year OS rates of patients with breast cancer in the triple-positive and HER2-positive groups were 96.7% and 82.7%, respectively, and the 5-year DFS rates were 90% and 73.3%, respectively. The Cox results revealed that molecular staging was an independent factor affecting recurrent metastasis and survival of breast cancer patients (hazard ratio [HR] =2.199, 95% confidence interval [CI], 1.296-8.266; HR = 9.994, 95% CI, 2.019-49.465).ConclusionThe 5-year DFS and OS rates were significantly better in the triple-positive group than in the HER2-positive group. Subgroups received different prognosis for different chemotherapy regimens. Breast cancer patients should be treated according to the risk of recurrence with symptomatic treatment and precise regulation