Re-evaluation of Urinary Trypsin Inhibitor on Pregnancy Course in Patients with Threatened Preterm Delivery : A Single-Center Retrospective Study

Background: We evaluated the necessity of urinary trypsin inhibitor for patients with threatened premature labor. Methods: We enrolled 146 women with singleton pregnancies who were treated for threatened premature labor as inpatients. The uterine cervical length of each patient was ? 25 mm at 22?35 weeks of gestation on transvaginal ultrasonography. The patients were divided into two groups: the urinary trypsin inhibitor group (91 patients treated with urinary trypsin inhibitor daily) or non-urinary trypsin inhibitor group (55 patients not treated with urinary trypsin inhibitor). The childbirth outcomes were retrospectively assessed. Results: The median cervical length measured on the day of admission was almost similar between the urinary trypsin inhibitor and non-urinary trypsin inhibitor groups. Depending on the symptoms of uterine contractions, we determined whether ritodrine hydrochloride and/or magnesium sulfate would be appropriate for treatment. The median gestational week at birth was 38 weeks in the urinary trypsin inhibitor group, and no obvious differences were observed when compared with the non-urinary trypsin inhibitor group. With regard to birth weight, no significant difference was found between the two groups (urinary trypsin inhibitor group, 2776 g; non-urinary trypsin inhibitor group, 2800 g). Conclusion: Our data showed no significant beneficial effects of urinary trypsin inhibitor in the maternal course and delivery outcomes

Noise robust automatic charge state recognition in quantum dots by machine learning and pre-processing, and visual explanations of the model with Grad-CAM

Charge state recognition in quantum dot devices is important in preparation of quantum bits for quantum information processing. Towards auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. In this work, we propose a simpler method using machine learning and pre-processing. We demonstrate the operation of the charge state recognition and evaluated an accuracy high as 96%. We also analyze the explainability of the trained machine learning model by gradient-weighted class activation mapping (Grad-CAM) which identifies class-discriminative regions for the predictions. It exhibits that the model predicts the state based on the change transition lines, indicating human-like recognition is realized.Comment: 15 pages, 6 figure

Visual explanations of machine learning model estimating charge states in quantum dots

Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. For further development of this technology, an understanding of the operation of the machine learning model, which is usually a black box, will be useful. In this study, we analyze the explainability of the machine learning model estimating charge states in quantum dots by gradient weighted class activation mapping. This technique highlights the important regions in the image for predicting the class. The model predicts the state based on the change transition lines, indicating that human-like recognition is realized. We also demonstrate improvements of the model by utilizing feedback from the mapping results. Due to the simplicity of our simulation and pre-processing methods, our approach offers scalability without significant additional simulation costs, demonstrating its suitability for future quantum dot system expansions