Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video

We propose a self-supervised visual learning method by predicting the variable playback speeds of a video. Without semantic labels, we learn the spatio-temporal visual representation of the video by leveraging the variations in the visual appearance according to different playback speeds under the assumption of temporal coherence. To learn the spatio-temporal visual variations in the entire video, we have not only predicted a single playback speed but also generated clips of various playback speeds and directions with randomized starting points. Hence the visual representation can be successfully learned from the meta information (playback speeds and directions) of the video. We also propose a new layer dependable temporal group normalization method that can be applied to 3D convolutional networks to improve the representation learning performance where we divide the temporal features into several groups and normalize each one using the different corresponding parameters. We validate the effectiveness of our method by fine-tuning it to the action recognition and video retrieval tasks on UCF-101 and HMDB-51.Comment: Accepted by IEEE Access on May 19, 202

Space zoning concept-based scheduling model for repetitive construction process

Many researchers have studied effective space zoning to reduce the duration of a construction project and interference among work tasks. These studies, however, attempted to plan the construction schedule using the space zoning concept based on network-based scheduling methods. Accordingly, it was difficult to reflect the representative characteristics of space zoning, such as iteration and overlapping. To overcome such limitations of existing methodologies and to achieve schedule reduction of a construction project by maximizing productivity, a Space zoning Concept-based scHEduling ModEl (SCHEME) for repetitive construction processes that adopt simulation techniques was developed in this study. The result of the application of the developed model to actual construction cases shows that the model reflects well the space-zoning characteristics, and in terms of the reduction of the construction duration, the model yielded a superior outcome in nonspace-zoning cases. The model developed in this study is expected to produce an excellent effect on the repetitive construction processes, in terms of construction duration

D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection

Domain adaptation for object detection typically entails transferring knowledge from one visible domain to another visible domain. However, there are limited studies on adapting from the visible to the thermal domain, because the domain gap between the visible and thermal domains is much larger than expected, and traditional domain adaptation can not successfully facilitate learning in this situation. To overcome this challenge, we propose a Distinctive Dual-Domain Teacher (D3T) framework that employs distinct training paradigms for each domain. Specifically, we segregate the source and target training sets for building dual-teachers and successively deploy exponential moving average to the student model to individual teachers of each domain. The framework further incorporates a zigzag learning method between dual teachers, facilitating a gradual transition from the visible to thermal domains during training. We validate the superiority of our method through newly designed experimental protocols with well-known thermal datasets, i.e., FLIR and KAIST. Source code is available at https://github.com/EdwardDo69/D3T .Comment: Accepted by CVPR 2024. Link: https://github.com/EdwardDo69/D3

Development of an advanced composite system form for constructability improvement through a Design for Six Sigma process

System form is widely used when constructing concrete buildings and structures because it has high productivity and good concrete casting quality compared with traditional hand-set form. However, from a worker’s perspective, system form is still very harsh to handle because of its heavy weight, noise generation, and use of releasing agent, and it also attenuates the productivity of system formwork. Therefore, this study proposes the use of an advanced composite material-based concrete form for workers using a Design for Six Sigma (DFSS) process to improve constructability of system formwork. User requirements are systematically reflected in the technical characteristics of concrete form, and innovative principles are scientifically organized through the DFSS process that mainly consists of quality function deployment and theory of creative problem-solving methods. The proposed composite form showed improved performance in deriving high-quality formwork and worker-friendly working conditions compared with previous system forms. Additionally, this study demonstrated how the DFSS will be a valuable tool for technology development and systematic decision-making in building construction

Engineered lentivector targeting of dendritic cells for in vivo immunization

We report a method of inducing antigen production in dendritic cells by in vivo targeting with lentiviral vectors that specifically bind to the dendritic cell–surface protein DC-SIGN. To target dendritic cells, we enveloped the lentivector with a viral glycoprotein from Sindbis virus engineered to be DC-SIGN–specific. In vitro, this lentivector specifically transduced dendritic cells and induced dendritic cell maturation. A high frequency (up to 12%) of ovalbumin (OVA)-specific CD8+ T cells and a significant antibody response were observed 2 weeks after injection of a targeted lentiviral vector encoding an OVA transgene into naive mice. This approach also protected against the growth of OVA-expressing E.G7 tumors and induced regression of established tumors. Thus, lentiviral vectors targeting dendritic cells provide a simple method of producing effective immunity and may provide an alternative route for immunization with protein antigens

Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

Objectives To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratorysounds recorded during polysomnography during all sleep stages between sleep onset and offset. Methods Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audiorecordings were performed with an air-conduction microphone during polysomnography. Analyses included allsleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmentedinto 5-s windows and sound features were extracted. Prediction models were established and validated with10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for threedifferent threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, includingaccuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under thecurve (AUC) of the receiver operating characteristic were computed. Results A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2, and23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughoutsleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Predictionperformances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%,81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30. Conclusion This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificityof >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithmsbased on respiratory sounds may have a high value for prescreening OSA with mobile devices

Multivariate analysis of prognostic factors in patients with pulmonary actinomycosis

BACKGROUND: There have been few studies of pulmonary actinomycosis, which is an uncommon anaerobic infection. Consequently, the optimal therapeutic regimen, appropriate duration of treatment, long-term prognosis, and factors predicting prognosis are not well established. METHODS: We retrospectively reviewed the medical records of histopathologically confirmed cases of pulmonary actinomycosis seen between November 2003 and December 2012. RESULTS: The study included 68 patients with a mean age of 58.4 ± 11.6 years. Of the 68, initial surgery was performed in 15 patients (22.1%), while the remaining 53 (77.9%) received antibiotic therapy initially. In the initial antibiotic group, 45/53 (84.9%) were cured without relapse (median antibiotic duration 5.3 months). 5/53 (9.4%) patients were refractory medically (median antibiotic duration 9.7 months), and 3/53 (5.7%) experienced a recurrence (median time to relapse 35.3 months). In the initial surgery group, 14/15 (93.3%) were cured and treatment failure occurred in one (6.7%). In the multivariate analysis, the absence of an antibiotic response at 1 month was the only independent factor associated with a poor treatment outcome, with an adjusted odds ratio of 49.2 (95% CI, 3.34–724.30). There was no significant difference in treatment outcome based on the size of the parenchymal lesion, comorbidities, whether intravenous antibiotics were used, antibiotic therapy duration, or whether the initial treatment was surgical. CONCLUSIONS: Antibiotic treatment with or without surgery was effective for treatment of pulmonary actinomycosis. Nevertheless, treatment failure or recurrence occurred in a considerable proportion of patients, especially those resistant to the initial antibiotic treatment