Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data

Creating synthetic voices with found data is challenging, as real-world recordings often contain various types of audio degradation. One way to address this problem is to pre-enhance the speech with an enhancement model and then use the enhanced data for text-to-speech (TTS) model training. This paper investigates the use of conditional diffusion models for generalized speech enhancement, which aims at addressing multiple types of audio degradation simultaneously. The enhancement is performed on the log Mel-spectrogram domain to align with the TTS training objective. Text information is introduced as an additional condition to improve the model robustness. Experiments on real-world recordings demonstrate that the synthetic voice built on data enhanced by the proposed model produces higher-quality synthetic speech, compared to those trained on data enhanced by strong baselines. Code and pre-trained parameters of the proposed enhancement model are available at \url{https://github.com/dmse4tts/DMSE4TTS

Micro-Crack Formation and Controlling of Inconel625 Parts Fabricated by Selective Laser Melting

Micro-crack is one of the most serious defects in selective laser melting (SLM), which impair the mechanical properties of the fabricated parts. In this study, Inconel625 superalloy specimens were fabricated by SLM process with progressive alternative scan strategy. The morphology of the cracks, elements distribution were detected by optical microscope (OM), scanning electron microscope (SEM) and electron back scattered diffraction (EBSD). The results showed that a large numbers of micro-cracks occurred at room temperature, with the average length of approximately 100 µm. It was found that crack formation was attribute to the local segregation of Nb and Mo element in the process of rapid solidification, resulting in the generation of low melting temperature eutectic solidification (γ+Laves). Micro-cracks grows along the interface of (γ+Laves) under the thermal stress. Base-plate preheating shows an efficient method to reduce the scales and number of cracks. The residual stress was reduced by more than 50% when preheating at 300℃.Mechanical Engineerin

Simulating large-size quantum spin chains on cloud-based superconducting quantum computers

Quantum computers have the potential to efficiently simulate large-scale quantum systems for which classical approaches are bound to fail. Even though several existing quantum devices now feature total qubit numbers of more than one hundred, their applicability remains plagued by the presence of noise and errors. Thus, the degree to which large quantum systems can successfully be simulated on these devices remains unclear. Here, we report on cloud simulations performed on several of IBM's superconducting quantum computers to simulate ground states of spin chains having a wide range of system sizes up to one hundred and two qubits. We find that the ground-state energies extracted from realizations across different quantum computers and system sizes reach the expected values to within errors that are small (i.e. on the percent level), including the inference of the energy density in the thermodynamic limit from these values. We achieve this accuracy through a combination of physics-motivated variational Ansatzes, and efficient, scalable energy-measurement and error-mitigation protocols, including the use of a reference state in the zero-noise extrapolation. By using a 102-qubit system, we have been able to successfully apply up to 3186 CNOT gates in a single circuit when performing gate-error mitigation. Our accurate, error-mitigated results for random parameters in the Ansatz states suggest that a standalone hybrid quantum-classical variational approach for large-scale XXZ models is feasible.Comment: 21 pages, 12 figures, 4 tables; title change; substantial revisio

Comparison of hair from rectum cancer patients and from healthy persons by Raman microspectroscopy and imaging

AbstractIn this work, Raman microspectroscopy and imaging was employed to analyze cancer patients’ hair tissue. The comparison between the hair from rectum cancer patients and the hair from healthy people reveals some remarkable differences, such as for the rectum cancer patients, there are more lipids but less content of α-helix proteins in the hair medulla section. Though more statistic data are required to establish universary rules for practical and accurate diagnosis, this work based on case study demonstrates the possibility of applying Raman microspectroscopy to reveal abnormality in non-cancer tissues such as hair in order to predict and diagnose cancers

On the Generation of Medical Question-Answer Pairs

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. A series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system.Comment: AAAI 202

Research on Two-stage Rotary Compressor with Refrigerant Injection for Cold Climate Heat Pump

As an promising heating application of environmental conservation and energy conservation, air-source heat pump systems has been spreading. However, conventional heat pump systems have problems remaining, such as inadequate heating capacity and reduced performance under low environmental temperature condition. To solve these problems, we developed a two-stage rotary compressor for household R32 air-source heat pump system. We analyzed the thermodynamic characteristics of two-stage rotary compressor with refrigerant injection used in heat pump system with economizer. It is found that the two-stage rotary compressor can enhance heating capacity and performance of R32 heat pump under cold climate markedly

Parameter Optimization for Preparing Carbon Fiber/Epoxy Composites by Selective Laser Sintering

Carbon fiber (CF) reinforced thermosetting resin composites offer a wide range of high performance features including excellent strength, modulus and thermal resistance and light weight. Consequently, they are increasingly demanded by aerospace and automotive industries due to the tighter requirements of the transport vehicles for lightweight as well as higher payloads. Although thermoplastics and their composites have been widely used in additive manufacturing (AM), to date it is difficult to manufacture carbon fibers reinforced thermosetting composite parts via AM technologies. Therefore, this study developed a novel method based on selective laser sintering (SLS) to fabricate high-performance carbon fiber/epoxy resin composites. The response surface method was employed to study the processing parameters affecting the quality of final parts, and an optimized processing condition was obtained.Mechanical Engineerin