Gradient Estimation for Unseen Domain Risk Minimization with Pre-Trained Models

Domain generalization aims to build generalized models that perform well on unseen domains when only source domains are available for model optimization. Recent studies have shown that large-scale pre-trained models can enhance domain generalization by leveraging their generalization power. However, these pre-trained models lack target task-specific knowledge yet due to discrepancies between the pre-training objectives and the target task. Although the task-specific knowledge could be learned from source domains by fine-tuning, this hurts the generalization power of pre-trained models due to gradient bias toward the source domains. To alleviate this problem, we propose a new domain generalization method that estimates unobservable gradients that reduce potential risks in unseen domains using a large-scale pre-trained model. These estimated unobservable gradients allow the pre-trained model to learn task-specific knowledge further while preserving its generalization ability by relieving the gradient bias. Our experimental results show that our method outperforms baseline methods on DomainBed, a standard benchmark in domain generalization. We also provide extensive analyses to demonstrate that the pre-trained model can learn task-specific knowledge without sacrificing its generalization power.Comment: ICCV2023 Workshop Versio

Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild

Social media platforms struggle to protect users from harmful content through content moderation. These platforms have recently leveraged machine learning models to cope with the vast amount of user-generated content daily. Since moderation policies vary depending on countries and types of products, it is common to train and deploy the models per policy. However, this approach is highly inefficient, especially when the policies change, requiring dataset re-labeling and model re-training on the shifted data distribution. To alleviate this cost inefficiency, social media platforms often employ third-party content moderation services that provide prediction scores of multiple subtasks, such as predicting the existence of underage personnel, rude gestures, or weapons, instead of directly providing final moderation decisions. However, making a reliable automated moderation decision from the prediction scores of the multiple subtasks for a specific target policy has not been widely explored yet. In this study, we formulate real-world scenarios of content moderation and introduce a simple yet effective threshold optimization method that searches the optimal thresholds of the multiple subtasks to make a reliable moderation decision in a cost-effective way. Extensive experiments demonstrate that our approach shows better performance in content moderation compared to existing threshold optimization methods and heuristics.Comment: WSDM2023 (Oral Presentation

Direct Observation of Radical States and the Correlation with Performance Degradation in Organic Light-Emitting Diodes During Device Operation

Microscopic characterization of radical states in organic light‐emitting diodes (OLEDs) during device operation is useful for elucidating the degradation mechanism because the radical formation has been considered as non‐radiative recombination centers. Electron spin resonance (ESR) spectroscopy is suitable for such characterization because it can directly observe radicals in OLEDs. In this work, the detailed ESR investigation into the radical states in OLEDs during device operation is firstly reported using a typical light‐emitting Alq3‐based OLEDs. The simultaneous measurements of the ESR signal and the luminance of the same OLED are performed to study the direct correlation between the radical states and the performance degradation. These characteristics show that the luminance monotonically decreases and an ESR signal concomitantly increases as the duration of the device operation increases after operating the OLED. Using the analysis of density functional theory (DFT) calculation, the origin of the newly emerged ESR signal is ascribed to the cationic species due to decomposed Alq3 molecules. The elucidation of the radical species formed in OLEDs during device operation has been demonstrated at a molecular level for the first time. This ESR analysis would provide useful knowledge for understanding the degradation mechanism in the OLEDs at the molecular level

Who Initiates Layoffs and Affects Firm Performance? Conflict of Interests between Active Foreign Institutional Investors and Outside Directors

Expanding upon the existing literature on agency theory, which often overlooks potential conflicting interests among monitoring mechanisms, this study investigates the divergent effects of active foreign institutional investors and outside directors on firm strategy and performance. Specifically, in light of inconclusive findings on the relationship between layoffs and performance, this study examines the initiators of layoffs and their impact on firm performance, comparing the roles of active foreign institutional investors and outside directors. Through panel data analysis of 2,516 firm-year observations from South Korea spanning 2010 to 2014, the findings reveal that firms with active foreign institutional investors are more inclined to implement large-scale layoffs, which positively moderate the relationship between active foreign institutional investors and firm performance metrics such as return on assets (ROA) and market returns. However, the relationships between active foreign institutional investors, layoffs, and performance are contingent upon the presence of outside directors on the board. Outside directors are not actively involved in initiating layoffs but tend to approve them in the context of operational performance, such as ROA, while showing less interest in layoffs as a means of external evaluation, such as market returns. These findings highlight the heterogeneous characteristics of outside directors and active foreign institutional investors with regard to layoffs and performance, and subsequently propose theoretical and managerial implications for the design of corporate governance

Effect of pulmonary rehabilitation on patients with acute COVID-19: a single-center case series

The coronavirus disease 2019 (COVID-19) pandemic has been ongoing for more than 2 years. Many patients who recover from severe acute respiratory syndrome coronavirus 2 infection continue to have aftereffects such as dyspnea and fatigue, which may lead to functional decline. Therefore, the need for managing these symptoms using methods such as pulmonary rehabilitation (PR) has emerged. The purpose of this study was to report the effectiveness of PR in five patients with acute COVID-19. PR was performed in patients with persistent dyspnea and oxygen demand after COVID-19. All five patients were able to maintain an independent functional status before COVID-19. However, after acute COVID-19, they were unable to walk independently and needed assistance for activities of daily living due to dyspnea and fatigue. Therefore, they were referred to rehabilitation units, and PR was performed. The modified Medical Research Council dyspnea scale, maximal expiratory pressure (MEP), 6-minute walking test, forced vital capacity, and grip strength were assessed before and after PR, and the results were compared. After PR, the parameters improved, except for the MEP in one patient (patient 3) and the grip strength in another patient (patient 4). After PR, two out of five patients returned to work and the other three returned home. Therefore, we conclude that PR is necessary for patients with acute COVID-19 with activity limitations

Mechanistic investigation into the light soaking effect observed in inverted polymer solar cells containing chemical bath deposited titanium oxide

In the glass-indium tin oxide (ITO)/titanium oxide (TiOx)/regioregular poly(3-hexylthiophene) (P3HT):[6,6]-phenyl C61 butyric acid methyl ester (PCBM)/poly(3,4-ethylenedioxylenethiophene):poly(4-styrenesulfonic acid) (PEDOT:PSS)/Au cell (TiOx cell), which contains amorphous titanium oxide prepared by chemical bath deposition and dried at 150 °C, a light soaking effect has been observed upon irradiation with white light. In contrast, in ITO/titanium oxide (TiO2)/P3HT:PCBM/PEDOT:PSS/Au cell (TiO2 cell), which contains anatase titanium oxide prepared by heat treatment at 450 °C, the maximum power conversion efficiency was obtained just after irradiation with white light. The number of P3HT+• cation radicals in the quartz-ITO/TiOx and TiO2/P3HT:PCBM substrates was estimated by ESR measurements at room temperature upon irradiation with white light. It increased gradually with an increase in irradiation time for the TiOx substrate but increased only slightly just after light irradiation for the TiO2 substrate. Upon irradiation with UV-cut light, the performance of the TiOx cell was inferior to that of the TiO2 cell. This could be related to the resistances of the P3HT:PCBM layers which were estimated by alternating current impedance spectroscopy. The resistance of the P3HT:PCBM layer in the TiOx cell was much larger than that in the TiO2 cell, though the difference between the two cells was merely heat treatment temperature of titanium oxide films using as electron collection layers. That is, the concentration of photocarriers in the P3HT:PCBM of the TiOx cell was significantly less than that in the P3HT:PCBM of the TiO2 cell. From these experimental results, the light soaking effect could be reasonably explained by assuming the existence of charge recombination centers in the TiOx near the TiOx/P3HT:PCBM interface

Utility of targeted deep sequencing for detecting circulating tumor DNA in pancreatic cancer patients.

Targeted deep sequencing across broad genomic regions has been used to detect circulating tumor DNA (ctDNA) in pancreatic ductal adenocarcinoma (PDAC) patients. However, since most PDACs harbor a mutation in KRAS, sequencing of broad regions needs to be systemically compared to analyzing only KRAS mutations for PDAC. Using capture-based targeted deep sequencing, we detected somatic tumor mutations in 17 fine needle aspiration biopsy and 69 longitudinal cell-free DNA (cfDNA) samples from 17 PDAC patients. KRAS mutations were detected in 10 out of 17 pretreatment patient plasma samples. Next, interrogation of genetic alterations in matched primary tumor samples detected ctDNA in 12 of 17 pretreatment plasma samples and cfDNA sequencing across the 83 target genes identified ctDNA in 15 of 17 cases (88.2% sensitivity). This improved sensitivity of ctDNA detection resulted in enhanced tumor burden monitoring when we analyzed longitudinal plasma samples. We found that cfDNA sequencing detected the lowest mutant allelic fractions and number of variants when complete response or partial response to chemotherapy was achieved. We demonstrated that ctDNA levels measured by targeted deep sequencing sensitively indicate the presence of cancer and correlate well with clinical responses to therapy and disease progression in PDAC patients