The evangelization of secular young adults in South Korea: effective principles for conversion growth among Protestant churches

https://place.asburyseminary.edu/ecommonsatsdissertations/1727/thumbnail.jp

Tau functions as Widom constants

We define a tau function for a generic Riemann-Hilbert problem posed on a union of non-intersecting smooth closed curves with jump matrices analytic in their neighborhood. The tau function depends on parameters of the jumps and is expressed as the Fredholm determinant of an integral operator with block integrable kernel constructed in terms of elementary parametrices. Its logarithmic derivatives with respect to parameters are given by contour integrals involving these parametrices and the solution of the Riemann-Hilbert problem. In the case of one circle, the tau function coincides with Widom's determinant arising in the asymptotics of block Toeplitz matrices. Our construction gives the Jimbo-Miwa-Ueno tau function for Riemann-Hilbert problems of isomonodromic origin (Painlev\'e VI, V, III, Garnier system, etc) and the Sato-Segal-Wilson tau function for integrable hierarchies such as Gelfand-Dickey and Drinfeld-Sokolov.Comment: 26 pages, 6 figure

Comparison of volume-controlled and pressure-controlled ventilation using a laryngeal mask airway during gynecological laparoscopy

Background: Several publications have reported the successful, safe use of Laryngeal Mask Airway (LMA)-Classic devices in patients undergoing laparoscopic surgery. However, there have been no studies that have examined the application of volume-controlled ventilation (VCV) or pressure-controlled ventilation (PCV) using a LMA during gynecological laparoscopy. The aim of this study is to compare how the VCV and PCV modes and using a LMA affect the pulmonary mechanics, the gas exchange and the cardiovascular responses in patients who are undergoing gynecological laparoscopy. Methods: Sixty female patients were randomly allocated to one of two groups, (the VCV or PCV groups). In the VCV group, baseline ventilation of the lung was performed with volume-controlled ventilation and a tidal volume of 10 ml/kg ideal body weight (IBW). In the PCV group, baseline ventilation of the lung using pressure-controlled ventilation was initiated with a peak airway pressure that provided a tidal volume of 10 ml/kg IBW and an upper limit of 35 cmH2O. The end-tidal CO2, the peak airway pressures (Ppeak), the compliance, the airway resistance and the arterial oxygen saturation were recorded at T1: 5 minutes after insertion of the laryngeal airway, and at T2 and T3: 5 and 15 minutes, respectively, after CO2 insufflation. Results: The Ppeak at 5 minutes and 15 minutes after CO2 insufflation were significantly increased compared to the baseline values in both groups. Also, at 5 minutes and 15 minutes after CO2 insufflation, there were significant differences of the Ppeak between the two groups. The compliance decreased in both groups after creating the pneumopertoneim (P < 0.05). Conclusions: Our results demonstrate that PCV may be an effective method of ventilation during gynecological laparoscopy, and it ensures oxygenation while minimizing the increases of the peak airway pressure after CO2 insufflation. ��� the Korean Society of Anesthesiologists, 2011

Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-shot In-Context Learners

Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. However, the ICL performance does not scale well with the number of available training samples as it is limited by the inherent input length constraint of the underlying language model. Meanwhile, many studies have revealed that language models are also powerful feature extractors, allowing them to be utilized in a black-box manner and enabling the linear probing paradigm, where lightweight discriminators are trained on top of the pre-extracted input representations. This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. Throughout in-depth investigations on various datasets, we verified that PALP significantly enhances the input representations closing the gap between ICL in the data-hungry scenario and fine-tuning in the data-abundant scenario with little training overhead, potentially making PALP a strong alternative in a black-box scenario.Comment: AAAI 202

The role of PET/CT for evaluating breast cancer

Positron emission tomography combined with computed tomography (PET/CT) has been receiving increasing attention during the recent years for making the diagnosis, for determining the staging and for the follow-up of various malignancies. The PET/CT findings of 58 breast cancer patients (age range: 34-79 years old, mean age: 50 years) were retrospectively compared with the PET or CT scans alone. PET/CT was found to be better than PET or CT alone for detecting small tumors or multiple metastases, for accurately localizing lymph node metastasis and for monitoring the response to chemotherapy in breast cancer patients

Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP

When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs. In order to address these requirements, Universal Domain Adaptation (UniDA) has emerged as a novel research area in computer vision, focusing on achieving both adaptation ability and robustness (i.e., the ability to detect out-of-distribution samples). While UniDA has led significant progress in computer vision, its application on language input still needs to be explored despite its feasibility. In this paper, we propose a comprehensive benchmark for natural language that offers thorough viewpoints of the model's generalizability and robustness. Our benchmark encompasses multiple datasets with varying difficulty levels and characteristics, including temporal shifts and diverse domains. On top of our testbed, we validate existing UniDA methods from computer vision and state-of-the-art domain adaptation techniques from NLP literature, yielding valuable findings: We observe that UniDA methods originally designed for image input can be effectively transferred to the natural language domain while also underscoring the effect of adaptation difficulty in determining the model's performance.Comment: Findings of EMNLP 202