Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning

As the size of the pre-trained language model (PLM) continues to increase, numerous parameter-efficient transfer learning methods have been proposed recently to compensate for the tremendous cost of fine-tuning. Despite the impressive results achieved by large pre-trained language models (PLMs) and various parameter-efficient transfer learning (PETL) methods on sundry benchmarks, it remains unclear if they can handle inputs that have been distributionally shifted effectively. In this study, we systematically explore how the ability to detect out-of-distribution (OOD) changes as the size of the PLM grows or the transfer methods are altered. Specifically, we evaluated various PETL techniques, including fine-tuning, Adapter, LoRA, and prefix-tuning, on three different intention classification tasks, each utilizing various language models with different scales.Comment: *SEM 202

Automated volumetric segmentation method for computerized-diagnosis of pure nodular ground-glass opacity in high-resolution CT

While accurate diagnosis of pure nodular ground glass opacity (PNGGO) is important in order to reduce the number of unnecessary biopsies, computer-aided diagnosis of PNGGO is less studied than other types of pulmonary nodules (e.g., solid-type nodule). Difficulty in segmentation of GGO nodules is one of technical bottleneck in the development of CAD of GGO nodules. In this study, we propose an automated volumetric segmentation method for PNGGO using a modeling of ROI histogram with a Gaussian mixture. Our proposed method segments lungs and applies noise-filtering in the pre-processing step. And then, histogram of selected ROI is modeled as a mixture of two Gaussians representing lung parenchyma and GGO tissues. The GGO nodule is then segmented by region-growing technique that employs the histogram model as a probability density function of each pixel belonging to GGO nodule, followed by the elimination of vessel-like structure around the nodules using morphological image operations. Our results using a database of 26 cases indicate that the automated segmentation method have a promising potential

Classification of Benign/Malignant PNGGOs using K-means algorithm in MDCT Images: A Preliminary Study

Lung cancer is one of the most prevalent diseases in the world. Recently, PNGGOs (Pure nodular ground-glass opacity) have been reported to increasing aspect for all CT-detected pulmonary nodules. Moreover, the malignancy rate of PNGGOs is a considerable proportion of benign diseases. In this study, we have developed a computerized classification scheme of PNGGOs malignancy. Segmentation of PNGGOs was performed semi-automatically. After that, the histogram based statistical features and region based features of benign and malignant GGO was extracted. Finally, K-means classifier was applied. Experiment was performed employing 12 CT image sets and 91.67% of accuracy was achieved

Pulmonary Metastases of Alveolar Soft-Part Sarcoma: CT Findings in Three Patients

Alveolar soft-part sarcoma is a rare soft tissue sarcoma of young adults with unknown histogenesis, and the organ most frequently involved in metastasis is the lung. We report the CT findings of three patients of pulmonary metastases of alveolar soft-part sarcoma, which manifested as clearly enhanced pulmonary nodules or masses. On enhanced scans, some of the masses were seen to contain dilated and tortuous intratumoral vessels

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

Association between Workplace Risk Factor Exposure and Sleep Disturbance: Analysis of the 2nd Korean Working Conditions Survey

OBJECTIVES: Sleep is essential for human beings to live and work properly. This study was conducted to investigate the relationship between occupational exposures to workplace risk factors and sleep disturbance in Korean workers. METHODS: The data were drawn from the second Korean Working Conditions Survey (KWCS); a total of 7,112 paid workers were analyzed. The independent variables were occupational exposures such as physical, chemical, biological, and psychosocial risk factor in the workplace, and psychosocial risk factor was divided into five categories (job demand, job control, social support, job insecurity, lack of reward). We estimated the relationship between various occupational exposures and sleep disturbance using multivariate logistic regression analysis. RESULTS: The results showed that people who exposed to physical, chemical, biological, and psychosocial (high job demand, inadequate social support, lack of reward) risk factors were more likely to increase the risk of sleep disturbance. Furthermore, after adjusting for general and occupational characteristics, we found significant positive associations between exposures to physical (odds ratios [OR] 1.47, 95% confidence interval [CI] 1.05-2.07) and psychosocial (high job demand (OR 2.93, 95% CI 2.16-3.98), inadequate social support (OR 1.57, 95% CI 1.14-2.15), lack of reward (OR 1.45, 95% CI 1.08-1.96)) risk factors and sleep disturbance. CONCLUSION: These results suggest that occupational exposures to physical and psychosocial workplace risk factors are significantly related to sleep disturbance

Antiatherosclerotic Effect of Korean Red Ginseng Extract Involves Regulator of G-Protein Signaling 5

Regulator of G-protein signaling 5 (RGS5), an inhibitor of Gα(q) and Gα(i) activation, has been reported to have antiatherosclerosis. Previous studies showed antiatherosclerotic effect of Korean red ginseng water extract (KRGE) via multiple signaling pathways. However, potential protective effect of KRGE through RGS5 expression has not been elucidated. Here, we investigated the antiatherosclerotic effect of KRGE in vivo and in vitro and its role on RGS5 mRNA expression. Elevated levels of total cholesterol, lactate dehydrogenase (LDH), and triglyceride (TG) in western diet groups of low-density lipoprotein receptor deficient LDLr−/− mice were reversed by oral administration of KRGE. KRGE suppressed transcriptional activity of tumor necrotic factor alpha (TNF-α), interleukin-6 (IL-6), and leptin in adipose tissue. It also potently repressed western diet-induced atheroma formation in aortic sinus. While KRGE showed reduced mRNA expression of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), IL-1β, IL-6, and TNF-α in LPS-stimulated RAW264.7 cells, it enhanced mRNA expression of RGS5. Moreover, RGS5 siRNA transfection of microglia cells pretreated with KRGE reversed its inhibitory effect on the expression of iNOS, COX-2, and IL-1β mRNA. In conclusion, KRGE showed antiatherosclerotic and anti-inflammatory effects in western diet fed LDLr−/− mice and this effect could partly be mediated by RGS5 expression

Warm Sitz Bath: Are There Benefits after Transurethral Resection of the Prostate?

PURPOSE: We aimed to evaluate the efficacy of warm water sitz baths in patients who have undergone transurethral resection of the prostate (TURP) owing to lower urinary tract symptoms secondary to benign prostatic hyperplasia. MATERIALS AND METHODS: We reviewed the records of 1,783 patients who had undergone TURP between 2001 and 2009. In the warm water sitz bath group, patients were instructed to sit in a tub containing lukewarm water at 40-45degrees C for 10 minutes each time. Patients were advised to perform the procedure for at least 5 days immediately after the removal of a Foley urethral catheter. The differences in post-TURP complications between the warm water sitz bath group and the no sitz bath group were compared. RESULTS: After TURP, 359 of the 1,561 patients performed a warm water sitz bath. Complications after TURP, such as hemorrhage, urinary tract infection, urethral stricture, and acute urinary retention were found in 19 (5.3%) and 75 (6.2%) patients in the sitz bath and no sitz bath groups, respectively (p=0.09). There was a significant difference in postoperative complications such as urethral stricture between the warm sitz bath group and the no sitz bath group (p=0.04). The group that did not undergo warm water sitz bath treatment showed a 1.13-fold increased risk of rehospitalization within 1 month after TURP due to postoperative complications compared with the warm water sitz bath group (odds ratio [OR]=1.134; 95% confidence interval [CI], 1.022 to 1.193; p=0.06). CONCLUSIONS: Warm water sitz bath treatment reduced postoperative complications such as urethral stricture. These results suggest that large-scale prospective studies are needed to establish an ideal method and optimal duration of sitz baths.ope

Production of Mutated Porcine Embryos Using Zinc Finger Nucleases and a Reporter-based Cell Enrichment System

To facilitate the construction of genetically-modified pigs, we produced cloned embryos derived from porcine fibroblasts transfected with a pair of engineered zinc finger nuclease (ZFN) plasmids to create targeted mutations and enriched using a reporter plasmid system. The reporter expresses RFP and eGFP simultaneously when ZFN-mediated site-specific mutations occur. Thus, double positive cells (RFP+/eGFP(+)) were selected and used for somatic cell nuclear transfer. Two types of reporter based enrichment systems were used in this study; the cloned embryos derived from cells enriched using a magnetic sorting-based system showed better developmental competence than did those derived from cells enriched by flow cytometry. Mutated sequences, such as insertions, deletions, or substitutions, together with the wild-type sequence, were found in the cloned porcine blastocysts. Therefore, genetic mutations can be achieved in cloned porcine embryos reconstructed with ZFN-treated cells that were enriched by a reporter-based system.