Three-stage training and orthogonality regularization for spoken language recognition

Abstract Spoken language recognition has made significant progress in recent years, for which automatic speech recognition has been used as a parallel branch to extract phonetic features. However, there is still a lack of a better training strategy for such architectures of two individual branches. In this paper, we analyze the mostly used two-stage training strategies and reveal a trade-off between the recognition accuracy and the generalization ability. Based on the analysis, we propose a three-stage training strategy and an orthogonality regularization method. The former adds a multi-task learning stage to the traditional two-stage training strategy to extract hybrid-level and noiseless features, which can improve the recognition accuracy on the basis of maintaining the generalization ability, while the latter constrains the orthogonality of base vectors and introduces prior knowledge to improve the recognition accuracy. Experiments on the Oriental Language Recognition (OLR) dataset indicate that these two proposed methods can improve both the language recognition accuracy and the generalization ability, especially in complex challenge tasks, such as cross-channel or noisy conditions. Also, our model, which combines these two proposed methods, performs better than the top three teams in the OLR20 challenge

Associations between body mass index and mortality in acute-on-chronic liver failure patients

Introduction and objectives: The association between the level of body mass index (BMI) and the mortality of patients with critical liver disease remains unclear. This study aimed to examine the association between BMI and hospital mortality of patients with acute-on-chronic liver failure (ACLF). Methods: Clinical data from 146 ACLF patients were collected and analyzed. BMI was categorized into three groups: lower BMI (<18.5 kg/m2), normal BMI (18.5–24.9 kg/m2), and overweight (25.0–32.0 kg/m2). BMI and laboratory parameters were measured one day before, or on the day of the start of the treatment. Values of BMI and laboratory parameters were compared between survivors and non-survivors, and then hospital mortality rates were compared among patients with different BMI levels. Results: The prognosis of ACLF patients was significantly correlated with international normalized ratio (INR), albumin and BMI. The ACLF patients with low albumin level and high INR values tend to have a high mortality rate. Also, survival time was significantly shorter in the ACLF patients with lower BMI, while patients with normal and overweight values had longer survival time. Conclusions: A graded association between BMI and hospital mortality with a strong significant trend was found in ACLF patients in China

Effects of Chemical Curing Temperature and Time on the Properties of Liquefied Wood based As-cured Precursors and Carbon Fibers

Liquefied wood based as-cured precursors and carbon fibers prepared by different chemical curing processes were carried out to investigate the effects of curing temperature and time on the thermostability and microstructure of liquefied wood based precursors, the tensile strength of carbon fibers as well. The primary fibers can be converted into precursors with high performance by directly heating at target curing temperature. With the temperature and duration increasing, the numbers of methylene bonds in precursors increased, resulting in the enhancement of cross-linkages among molecular chains and then the improvement of thermostability of precursors. Carbon fibers prepared from as-cured precursors (curing temperature 95 oC, curing time 3h) had the minimum value of the average interlayer spacing (d002), it also showed the highest tensile strength, almost 800 MPa, which can be classified as fibers of general grade

A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning

Abstract Background Rectal temperature (RT) is an important index of core temperature, which has guiding significance for the diagnosis and treatment of pet diseases. Objectives Development and evaluation of an alternative method based on machine learning to determine the core temperatures of cats and dogs using surface temperatures. Animals 200 cats and 200 dogs treated between March 2022 and May 2022. Methods A group of cats and dogs were included in this study. The core temperatures and surface body temperatures were measured. Multiple machine learning methods were trained using a cross-validation approach and evaluated in one retrospective testing set and one prospective testing set. Results The machine learning models could achieve promising performance in predicting the core temperatures of cats and dogs using surface temperatures. The root mean square errors (RMSE) were 0.25 and 0.15 for cats and dogs in the retrospective testing set, and 0.15 and 0.14 in the prospective testing set. Conclusion The machine learning model could accurately predict core temperatures for companion animals of cats and dogs using easily obtained body surface temperatures

An unconventional VH1-2 antibody tolerates escape mutations and shows an antigenic hotspot on SARS-CoV-2 spike

Summary: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein continues to evolve antigenically, impacting antibody immunity. D1F6, an affinity-matured non-stereotypic VH1-2 antibody isolated from a patient infected with the SARS-CoV-2 ancestral strain, effectively neutralizes most Omicron variants tested, including XBB.1.5. We identify that D1F6 in the immunoglobulin G (IgG) form is able to overcome the effect of most Omicron mutations through its avidity-enhanced multivalent S-trimer binding. Cryo-electron microscopy (cryo-EM) and biochemical analyses show that three simultaneous epitope mutations are generally needed to substantially disrupt the multivalent S-trimer binding by D1F6 IgG. Antigenic mutations at spike positions 346, 444, and 445, which appeared in the latest variants, have little effect on D1F6 binding individually. However, these mutations are able to act synergistically with earlier Omicron mutations to impair neutralization by affecting the interaction between D1F6 IgG and the S-trimer. These results provide insight into the mechanism by which accumulated antigenic mutations facilitate evasion of affinity-matured antibodies