Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration

Research interests in the robustness of deep neural networks against domain shifts have been rapidly increasing in recent years. Most existing works, however, focus on improving the accuracy of the model, not the calibration performance which is another important requirement for trustworthy AI systems. Temperature scaling (TS), an accuracy-preserving post-hoc calibration method, has been proven to be effective in in-domain settings, but not in out-of-domain (OOD) due to the difficulty in obtaining a validation set for the unseen domain beforehand. In this paper, we propose consistency-guided temperature scaling (CTS), a new temperature scaling strategy that can significantly enhance the OOD calibration performance by providing mutual supervision among data samples in the source domains. Motivated by our observation that over-confidence stemming from inconsistent sample predictions is the main obstacle to OOD calibration, we propose to guide the scaling process by taking consistencies into account in terms of two different aspects -- style and content -- which are the key components that can well-represent data samples in multi-domain settings. Experimental results demonstrate that our proposed strategy outperforms existing works, achieving superior OOD calibration performance on various datasets. This can be accomplished by employing only the source domains without compromising accuracy, making our scheme directly applicable to various trustworthy AI systems.Comment: Accepted at AAAI-24 (The 38th AAAI Conference on Artificial Intelligence, February 2024

The effect of beta1-adrenergic receptor gene polymorphism on prolongation of corrected QT interval during endotracheal intubation under sevoflurane anesthesia

BACKGROUND: The hemodynamic responses to endotracheal intubation are associated with sympathoadrenal activity. Polymorphisms in the beta1-adrenergic receptor (β(1)AR) gene can alter the pathophysiology of specific diseases. The aim of this study is to investigate whether the Ser49Gly and Arg389Gly polymorphism of the β(1)AR gene have different cardiovascular responses during endotracheal intubation under sevoflurane anesthesia. METHODS: Ninety-one healthy patients undergoing general anesthesia were enrolled. Patients underwent slow inhalation induction of anesthesia using sevoflurane in 100% oxygen. Vecuronium 0.15 mg/kg was given for muscle relaxation. Endotracheal intubation was performed by an anesthesiologist. The mean arterial pressure (MAP), heart rate (HR), and the corrected QT (QTc) interval were measured before induction, before laryngoscopy, and immediately after tracheal intubation. Genomic DNA was isolated from the patients' peripheral blood and then evaluated for the β(1)AR-49 and β(1)AR-389 genes using an allele-specific polymerase chain reaction method. RESULTS: No differences were found in the baseline values of MAP, HR, and the QTc interval among β(1)AR-49 and β(1)AR-389, respectively. In the case of β(1)AR-49, the QTc interval change immediately after tracheal intubation was significantly greater in Ser/Ser genotypes than in Ser/Gly genotypes. No differences were observed immediately after tracheal intubation in MAP and HR for β(1)AR-49 and β(1)AR-389. CONCLUSIONS: We found an association between the Ser49 homozygote gene of β(1)AR-49 polymorphism and increased QTc prolongation during endotracheal intubation with sevoflurane anesthesia. Thus, β(1)AR-49 polymorphism may be useful in predicting the risk of arrhythmia during endotracheal intubation in patients with long QT syndrome.ope

Towards Few-shot Out-of-Distribution Detection

Out-of-distribution (OOD) detection is critical for ensuring the reliability of open-world intelligent systems. Despite the notable advancements in existing OOD detection methodologies, our study identifies a significant performance drop under the scarcity of training samples. In this context, we introduce a novel few-shot OOD detection benchmark, carefully constructed to address this gap. Our empirical analysis reveals the superiority of ParameterEfficient Fine-Tuning (PEFT) strategies, such as visual prompt tuning and visual adapter tuning, over conventional techniques, including fully fine-tuning and linear probing tuning in the few-shot OOD detection task. Recognizing some crucial information from the pre-trained model, which is pivotal for OOD detection, may be lost during the fine-tuning process, we propose a method termed DomainSpecific and General Knowledge Fusion (DSGF). This approach is designed to be compatible with diverse fine-tuning frameworks. Our experiments show that the integration of DSGF significantly enhances the few-shot OOD detection capabilities across various methods and fine-tuning methodologies, including fully fine-tuning, visual adapter tuning, and visual prompt tuning. The code will be released

A Score-Based Evaluation Model for Rehabilitation of Existing Pumped Storage Hydropower Plant Construction

As the proportion of new and renewable energy increases, power control demands are becoming more frequent due to variability in power generation. As a complementary means against this, the pumped storage hydropower plants (PSHP) are attracting attention as energy storage systems (ESS), but it has high construction costs. Therefore, this study aims to improve the economic feasibility by developing the evaluation model of the existing infrastructure into an upper/lower dam suitable for PSHP. The concept of upper dam capacity is newly defined, and the evaluation index is constructed using normalization. A new evaluation system is presented for five factors: environment, stability, energy, capacity, and economy. Finally, it is tested in the pilot area in Korea. Several candidates, including the PSHP in operation, are found to have been distributed with higher scores. These results will help to satisfy the selection of candidates during the preliminary feasibility study phase, and programming them will enable more accurate and rapid assessment

Experimental Investigation for Tensile Performance of GFRP-Steel Hybridized Rebar

Tensile performance of the recently developed “FRP Hybrid Bar” at Korea Institute of Civil Engineering and Building Technology (KICT) is experimentally evaluated by the authors. FRP Hybrid Bar is introduced to overcome the low elastic modulus of the existing GFRP bars to be used as a structural member in reinforced concrete structures. The concept of material hybridization is applied to increase elastic modulus of GFRP bars by using steel. This hybridized GFRP bar can be used in concrete structures as a flexural reinforcement with a sufficient level of elastic modulus. In order to verify the effect of material hybridization on tensile properties, tensile tests are conducted. The test results for both FRP Hybrid Bar and the existing GFRP bars are compared. The results indicate that the elastic modulus of FRP Hybrid Bar can be enhanced by up to approximately 250 percent by the material hybridization with a sufficient tensile strength. To ensure the long-term durability of FRP Hybrid Bar to corrosion resistance, the individual and combined effects of environmental conditions on FRP Hybrid Bar itself as well as on the interface between rebar and concrete are currently under investigation