On Exact Inversion of DPM-Solvers

Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly, but have posed challenges to find the exact inverse (i.e., finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and propose algorithms to perform them when samples are generated by the first-order as well as higher-order DPM-solvers. For each explicit denoising step in DPM-solvers, we formulated the inversions using implicit methods such as gradient descent or forward step method to ensure the robustness to large classifier-free guidance unlike the prior approach using fixed-point iteration. Experimental results demonstrated that our proposed exact inversion methods significantly reduced the error of both image and noise reconstructions, greatly enhanced the ability to distinguish invisible watermarks and well prevented unintended background changes consistently during image editing. Project page: \url{https://smhongok.github.io/inv-dpm.html}.Comment: 16 page

The intratumoral administration of ferucarbotran conjugated with doxorubicin improved therapeutic effect by magnetic hyperthermia combined with pharmacotherapy in a hepatocellular carcinoma model

BACKGROUND: Local hyperthermia of tumor in conjunction with chemotherapy is a promising strategy for cancer treatment. The aim of this study was to evaluate the efficacy of intratumoral delivery of clinically approved magnetic nanoparticles (MNPs) conjugated with doxorubicin to simultaneously induce magnetic hyperthermia and drug delivery in a hepatocellular carcinoma (HCC) model. MATERIALS AND METHODS: HCC cells expressing luciferase were implanted into the flank of BALB/c-nu mice (n = 19). When the tumor diameter reached 7–8 mm, the animals were divided into four groups according to the injected agents: group A (normal saline, n = 4), group B (doxorubicin, n = 5), group C (MNP, n = 5), and group D (MNP/doxorubicin complex, n = 5). Animals were exposed to an alternating magnetic field (AMF) to receive magnetic hyperthermia, and intratumoral temperature changes were measured. Bioluminescence imagings (BLIs) were performed before treatment and at 3, 7, and 14 days after treatment to measure the tumoral activities. The relative signal intensity (RSI) of each tumor was calculated by dividing the BLI signal at each time point by the value measured before treatment. At day 14 post-treatment, all tumor tissues were harvested to assess the apoptosis rates by pathological examination. RESULTS: The rise in temperature of the tumors was 1.88 ± 0.21°C in group A, 0.96 ± 1.05°C in B, 7.93 ± 1.99°C in C, and 8.95 ± 1.31°C in D. The RSI of the tumors at day 14 post-treatment was significantly lower in group D (0.31 ± 0.20) than in group A (2.23 ± 1.14), B (0.94 ± 0.47), and C (1.02 ± 0.21). The apoptosis rates of the tumors were 11.52 ± 3.10% in group A, 23.0 ± 7.68% in B, 25.4 ± 3.36% in C, and 39.0 ± 13.2% in D, respectively. CONCLUSIONS: The intratumoral injection of ferucarbotran conjugated with doxorubicin shows an improved therapeutic effect compared with doxorubicin or ferucarbotran alone when the complex is injected into HCC tissues exposed to AMF for magnetic hyperthermia. This strategy of combining doxorubicin and MNP-induced magnetic hyperthermia exhibits a synergic effect on inhibiting tumor growth in an HCC model

Sleep disturbances, depressive symptoms, and cognitive efficiency as determinants of mistakes at work in shift and non-shift workers

IntroductionShift work is known to reduce productivity and safety at work. Previous studies have suggested that a variety of interrelated factors, such as mood, cognition, and sleep, can affect the performance of shift workers. This study aimed to identify potential pathways from depression, sleep, and cognition to work performance in shift and non-shift workers.Material and methodsOnline survey including the Center for Epidemiologic Studies Depression Scale (CES-D), Cognitive Failure Questionnaire (CFQ), and Pittsburgh Sleep Quality Index (PSQI), as well as two items representing work mistakes were administered to 4,561 shift workers and 2,093 non-shift workers. A multi-group structural equation model (SEM) was used to explore differences in the paths to work mistakes between shift and non-shift workers.ResultsShift workers had higher PSQI, CES-D, and CFQ scores, and made more mistakes at work than non-shift workers. The SEM revealed that PSQI, CES-D, and CFQ scores were significantly related to mistakes at work, with the CFQ being a mediating variable. There were significant differences in the path coefficients of the PSQI and CES-D between shift and non-shift workers. The direct effects of sleep disturbances on mistakes at work were greater in shift workers, while direct effects of depressive symptoms were found only in non-shift workers.DiscussionThe present study found that shift workers made more mistakes at work than non-shift workers, probably because of depressed mood, poor sleep quality, and cognitive inefficiency. Sleep influences work performance in shift workers more directly compared to non-shift workers

Depression, antidepressant use, and the risk of type 2 diabetes: a nationally representative cohort study

BackgroundPrevious studies have reported that depression can increase the risk of type 2 diabetes. However, they did not sufficiently consider antidepressants or comorbidity.MethodsThe National Health Insurance Sharing Service database was used. Among the sample population, 276,048 subjects who had been diagnosed with depression and prescribed antidepressants (DEP with antidepressants group) and 79,119 subjects who had been diagnosed with depression but not prescribed antidepressants (DEP without antidepressants group) were found to be eligible for this study. Healthy controls (HCs) were 1:1 matched with the DEP with antidepressants group for age and sex. We followed up with them for the occurrence of type 2 diabetes.ResultsIn the group of DEP with antidepressants, although the risk of type 2 diabetes increased compared to HCs in a crude analysis, it decreased when comorbidity was adjusted for. In the group of DEP without antidepressants, the risk of type 2 diabetes decreased both in the crude model and the adjusted models. The risk varied by age group and classes or ingredients of antidepressants, with young adult patients showing an increased risk even in the fully adjusted model.ConclusionOverall, those with depression had a reduced risk of type 2 diabetes. However, the risk varied according to the age at onset, comorbidity, and type of antidepressants

Brain structural correlates of subjective sleepiness and insomnia symptoms in shift workers

BackgroundStudies on the brain structures of shift workers are limited; thus, this cross-sectional study aimed to compare the brain structures and the brain structural correlates of subjective sleepiness and insomnia symptoms between shift workers and non-shift workers.MethodsShift workers (n = 63) and non-shift workers (n = 58) completed questionnaires assessing subjective sleepiness and insomnia symptoms. Cortical thickness, cortical surface area, and subcortical volumes were measured by magnetic resonance imaging. The brain morphometric measures were compared between the groups, and interaction analyses using the brain morphometric measures as the dependent variable were performed to test the interactions between the study group and measures of sleep disturbance (i.e., subjective sleepiness and insomnia symptoms).ResultsNo differences in cortical thickness, cortical surface area, or subcortical volumes were detected between shift workers and non-shift workers. A single cluster in the left motor cortex showed a significant interaction between the study group and subjective sleepiness in the cortical surface area. The correlation between the left motor cortex surface area and the subjective sleepiness level was negative in shift workers and positive in non-shift workers. Significant interaction between the study group and insomnia symptoms was present for the left/right putamen volumes. The correlation between the left/right putamen volumes and insomnia symptom levels was positive in shift workers and negative in non-shift workers.ConclusionLeft motor cortex surface area and bilateral putamen volumes were unique structural correlates of subjective sleepiness and insomnia symptoms in shift workers, respectively

Thermal Image Analysis for Fault Detection and Diagnosis of PV Systems

This research presents thermal image analysis for Fault Detection and Diagnosis (FDD) of Photovoltaic (PV) Systems. The traditional manual approach of PV inspection is generally more time-consuming, more dangerous, and less accurate than the modern approach of PV inspection using Aerial Thermography (AT). Thermal image analysis conducted in this research will contribute to utilizing thermography and UAVs for PV inspection by providing a more accurate and cost-efficient diagnosis of PV faults. In this research, PV module inspection was achieved through two steps: (i) PV monitoring and (ii) PV Fault Detection and Diagnosis (FDD). In the PV monitoring stage, PV cells were monitored by aerial thermography. In this stage, the thermal data was acquired for the next step. In the PV FDD stage, hot spot phenomenon and the condition of the PV modules were detected and measured. The FDD stage was conducted in three steps: (i) fault detection, (ii) fault isolation, and (iii) fault identification. The fault detection stage determined whether the PV module has an abnormal condition. Next, in the fault isolation stage, the location and the area of possible hot spots were identified. Lastly, the number of the hot spots were counted in the fault identification stage. The proposed research will help with the problems of the modern PV inspection and, eventually, contribute to the performance of PV power generation

Daily Emotional Labor, Negative Affect State, and Emotional Exhaustion: Cross-Level Moderators of Affective Commitment

Employees’ emotional-labor strategies, experienced affects, and emotional exhaustion in the workplace may vary over time within individuals, even within the same day. However, previous studies on these relationships have not highlighted their dynamic properties of these relationships. In addition, although the effects of surface and deep acting on emotional exhaustion have been investigated in emotional-labor research, empirical studies on these relationships still report mixed results. Thus, we suggest that moderators may affect the relationship between emotional labor and emotional exhaustion. Also, this study examines the relationship between emotional labor and emotional exhaustion within individuals by repeated measurements, and verifies the mediating effect of a negative affect state. Finally, our study confirms the moderating effects that affective commitment has on the relationship between emotional labor and emotional exhaustion. Data was collected from tellers who had a high degree of interaction with clients at banks based in South Korea. A total of 56 tellers participated in the survey and responded for five working days. A total of 616 data entries were collected from the 56 respondents. We used a hierarchical linear model (HLM) to examine our hypothesis. The results showed that surface-acting emotional labor increases emotional exhaustion; furthermore, the relationship between surface acting emotional labor and emotional exhaustion is mediated by a negative affect state within individuals. In addition, this study verified that affective commitment buffers the negative effects that surface acting emotional labor has on emotional exhaustion. These results suggest that emotional labor is a dynamic process within individuals, and that emotional exhaustion caused by emotional labor differs among individuals, and is dependent upon factors such as the individual’s level of affective commitment

Hand gesture recognition with out-of-distribution gesture detection using a soft sensor embedded glove

Hand gesture recognition has been applied to many applications, such as sign language translation and virtual reality. Soft sensor embedded gloves have been widely used to collect gesture data for hand gesture recognition. Using a soft sensor embedded glove has an advantage compared to vision-based approaches, because it is less affected by the environment and is not restricted to the angle of camera sensors, especially when it is applied to virtual reality industry. One of the existing challenges in machine learning-based hand gesture recognition is that new gestures, which are not seen in the training stage, are often discovered in the testing stage. In order to overcome this challenge, in this work, a hand gesture recognition model is proposed based on one-dimensional convolutional neural networks (1D CNN) and long short-term memory (LSTM) as well as a clustering method. In particular, a clustering method is used to detect out-of-distribution gestures, which are not contained in the clusters that are consisted of hand gestures used in the training stage. The experiment results validate that the proposed hand gesture recognition model performs better than existing hand gesture recognition methods and a proposed clustering methods detects out-of-distribution gestures with high accuracy