Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach

Collecting paired training data is difficult in practice, but the unpaired samples broadly exist. Current approaches aim at generating synthesized training data from the unpaired samples by exploring the relationship between the corrupted and clean data. This work proposes LUD-VAE, a deep generative method to learn the joint probability density function from data sampled from marginal distributions. Our approach is based on a carefully designed probabilistic graphical model in which the clean and corrupted data domains are conditionally independent. Using variational inference, we maximize the evidence lower bound (ELBO) to estimate the joint probability density function. Furthermore, we show that the ELBO is computable without paired samples under the inference invariant assumption. This property provides the mathematical rationale of our approach in the unpaired setting. Finally, we apply our method to real-world image denoising and super-resolution tasks and train the models using the synthetic data generated by the LUD-VAE. Experimental results validate the advantages of our method over other learnable approaches

Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification

Multi-spectral vehicle re-identification aims to address the challenge of identifying vehicles in complex lighting conditions by incorporating complementary visible and infrared information. However, in harsh environments, the discriminative cues in RGB and NIR modalities are often lost due to strong flares from vehicle lamps or sunlight, and existing multi-modal fusion methods are limited in their ability to recover these important cues. To address this problem, we propose a Flare-Aware Cross-modal Enhancement Network that adaptively restores flare-corrupted RGB and NIR features with guidance from the flare-immunized thermal infrared spectrum. First, to reduce the influence of locally degraded appearance due to intense flare, we propose a Mutual Flare Mask Prediction module to jointly obtain flare-corrupted masks in RGB and NIR modalities in a self-supervised manner. Second, to use the flare-immunized TI information to enhance the masked RGB and NIR, we propose a Flare-Aware Cross-modal Enhancement module that adaptively guides feature extraction of masked RGB and NIR spectra with prior flare-immunized knowledge from the TI spectrum. Third, to extract common informative semantic information from RGB and NIR, we propose an Inter-modality Consistency loss that enforces semantic consistency between the two modalities. Finally, to evaluate the proposed FACENet in handling intense flare, we introduce a new multi-spectral vehicle re-ID dataset, called WMVEID863, with additional challenges such as motion blur, significant background changes, and particularly intense flare degradation. Comprehensive experiments on both the newly collected dataset and public benchmark multi-spectral vehicle re-ID datasets demonstrate the superior performance of the proposed FACENet compared to state-of-the-art methods, especially in handling strong flares. The code and dataset will be released soon

Calcium–magnesium–alumina–silicate (CMAS) resistance of LaPO4 thermal barrier coatings

Nanostructured LaPO4 thermal barrier coatings (TBCs) were prepared by air plasma spraying, and their resistance to calcium–magnesium–alumina–silicate (CMAS) attack at 1250 °C, 1300 °C and 1350 °C was investigated. The reaction products were characterized by X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy and transmission electron microscopy. Exposed to CMAS attack for 0.5 h, a continuous dense reaction layer formed, which was mainly composed of P–Si apatite based on Ca2+xLa8-x(PO4)x(SiO4)6-xO2, anorthite and spinel phases. Beneath the reaction layer, little evidence of CMAS trace could be found. With the increase in temperature and heat treatment duration, the reaction layer became thick, while penetration depth of the molten CMAS changed slightly. Due to the formation of a reaction layer suppressing CMAS further infiltration, LaPO4 TBCs are highly resistant to CMAS attack

Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles

The single-particle cryo-EM field faces the persistent challenge of preferred orientation, lacking general computational solutions. We introduce cryoPROS, an AI-based approach designed to address the above issue. By generating the auxiliary particles with a conditional deep generative model, cryoPROS addresses the intrinsic bias in orientation estimation for the observed particles. We effectively employed cryoPROS in the cryo-EM single particle analysis of the hemagglutinin trimer, showing the ability to restore the near-atomic resolution structure on non-tilt data. Moreover, the enhanced version named cryoPROS-MP significantly improves the resolution of the membrane protein NaX using the no-tilted data that contains the effects of micelles. Compared to the classical approaches, cryoPROS does not need special experimental or image acquisition techniques, providing a purely computational yet effective solution for the preferred orientation problem. Finally, we conduct extensive experiments that establish the low risk of model bias and the high robustness of cryoPROS

Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes

Cognitive decline among older adults with heart diseases before and during the COVID-19 pandemic: A longitudinal cohort study

BackgroundLittle is known about the impact induced by the COVID-19 pandemic on the cognitive function of older adults with heart diseases. This study aimed to examine whether older adults with heart diseases suffered larger cognitive deterioration during the COVID-19 pandemic.MethodsThis study leveraged longitudinal data from the Health and Retirement Study (HRS), a nationally representative U.S. aging cohort with objective cognitive assessments measured before and during the pandemic. The interval from HRS waves 13 to 14 (April 2016 to June 2019) was defined as the pre-pandemic period to control the pre-existed cognitive difference between participants with and without heart diseases, and the interval from waves 14 to 15 (June 2019 to June 2021) was defined as the pandemic period. The HRS wave 14 survey was considered the baseline. The heart disease status was defined by a self-reported diagnosis. Linear mixed models were performed to evaluate and compare the cognitive differences during different periods.ResultsA total of 9,304 participants (women: 5,655, 60.8%; mean age: 65.8 ± 10.8 years) were included, and 2,119 (22.8%) had heart diseases. During the pre-pandemic period, there was no significant difference (−0.03, 95% CI: −0.22 to 0.15, P = 0.716) in the changes in global cognitive scores between participants with and without heart disease. During the pandemic period, a larger decreased change in the global cognitive score was observed in the heart disease group compared with the non-heart disease group (−0.37, 95% CI: −0.55 to −0.19, P < 0.001). An enlarged difference in global cognitive score was observed during the pandemic period (−0.33, 95% CI: −0.65 to −0.02, P = 0.036).ConclusionThe findings demonstrated that the population with heart diseases suffered more cognitive decline related to the pandemic, underscoring the necessity to provide immediate cognitive monitoring and interventions for the population with heart diseases

Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance

Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries. If so, how should we address the cognitive acceptance and ambient control of elderly assistive robots through design? In this paper, we proposed an explorative design of an ambient SuperLimb (Supernumerary Robotic Limb) system that involves a pneumatically-driven robotic cane for at-home motion assistance, an inflatable vest for compliant human-robot interaction, and a depth sensor for ambient intention detection. The proposed system aims at providing active assistance during the sit-to-stand transition for at-home usage by the elderly at the bedside, in the chair, and on the toilet. We proposed a modified biomechanical model with a linear cane robot for closed-loop control implementation. We validated the design feasibility of the proposed ambient SuperLimb system including the biomechanical model, our result showed the advantages in reducing lower limb efforts and elderly fall risks, yet the detection accuracy using depth sensing and adjustments on the model still require further research in the future. Nevertheless, we summarized empirical guidelines to support the ambient design of elderly-assistive SuperLimb systems for lower limb functional augmentation.Comment: 8 pages, 9 figures, accepted for IEEE RoboSoft 202