Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery

We explore the "hidden" ability of large-scale pre-trained image generation models, such as Stable Diffusion and Imagen, in non-visible light domains, taking Synthetic Aperture Radar (SAR) data for a case study. Due to the inherent challenges in capturing satellite data, acquiring ample SAR training samples is infeasible. For instance, for a particular category of ship in the open sea, we can collect only few-shot SAR images which are too limited to derive effective ship recognition models. If large-scale models pre-trained with regular images can be adapted to generating novel SAR images, the problem is solved. In preliminary study, we found that fine-tuning these models with few-shot SAR images is not working, as the models can not capture the two primary differences between SAR and regular images: structure and modality. To address this, we propose a 2-stage low-rank adaptation method, and we call it 2LoRA. In the first stage, the model is adapted using aerial-view regular image data (whose structure matches SAR), followed by the second stage where the base model from the first stage is further adapted using SAR modality data. Particularly in the second stage, we introduce a novel prototype LoRA (pLoRA), as an improved version of 2LoRA, to resolve the class imbalance problem in SAR datasets. For evaluation, we employ the resulting generation model to synthesize additional SAR data. This augmentation, when integrated into the training process of SAR classification as well as segmentation models, yields notably improved performance for minor classe

Efficient Offline Policy Optimization with a Learned Model

MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations, thus costing huge computing time. This paper investigates a few hypotheses where MuZero Unplugged may not work well under the offline RL settings, including 1) learning with limited data coverage; 2) learning from offline data of stochastic environments; 3) improperly parameterized models given the offline data; 4) with a low compute budget. We propose to use a regularized one-step look-ahead approach to tackle the above issues. Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout. Policy improvements are towards the direction that maximizes the estimated advantage with regularization of the dataset. We conduct extensive empirical studies with BSuite environments to verify the hypotheses and then run our algorithm on the RL Unplugged Atari benchmark. Experimental results show that our proposed approach achieves stable performance even with an inaccurate learned model. On the large-scale Atari benchmark, the proposed method outperforms MuZero Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e., 1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM normalized score with the same hardware and software stacks. Our implementation is open-sourced at https://github.com/sail-sg/rosmo.Comment: ICLR202

Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies

The reversible effects of free fatty acids on sulfonylurea-stimulated insulin secretion are related to the expression and dynamin-mediated endocytosis of KATP channels in pancreatic β cells

Objective: Lipotoxicity-induced pancreatic β cell-dysfunction results in decreased insulin secretion in response to multiple stimulus. In this study, we i nvestigated the reversible effects of palmitate (PA) or oleate (OA) on insulin secretion and the relationship with pancreatic β-cell ATP-sensitive potassium (KATP) channels. Methods: MIN6 cells were treated with PA and OA for 48 h and then washed out for 24 h to determine the changes in expression and endocytosis of the KATP channels and glucose-stimulated insulin secretion (GSIS) and sulfonylurea-stimulated insulin secretion (SU-SIS). Results: MIN6 cells exposed to PA or OA showed both impaired GSIS and SU -SIS; the former was not restorable, while the latter was reversible with washout of PA or OA. Decreased expressions of both total and surface Kir6.2 and SUR1 and endocytosis of KATP channels were observed, which were also recoverable after wash out. When MIN6 cells exposed to free fatty acids (FFAs) were cotreated wi th 5-aminoimidazole- 4-carboxamide ribonucleotide (AICAR) or dynasore, we found that endocytosis of KATP channels did not change significantly by AICAR but was almost co mpletely blocked by dynasore. Meanwhile, the inhibition of endocytosis of K ATP channels after washout could be activated by PIP2. The recovery of SU-SIS after washout was significantly weakened by PIP2, but the decrease of SU-SIS induced by FFAs was not allevi ated by dynasore. Conclusions: FFAs can cause reversible impairment of SU-SIS on pancreatic β cells. The reversibility of the effects is partial because of the changes o f expression and endocytosis of Kir6.2 and SUR1 which was mediated by dynamin

Maternal supplementation with Limosilactobacillus reuteri FN041 for preventing infants with atopic dermatitis: study protocol for a randomized controlled trial

BackgroundAtopic dermatitis (AD) has increased rapidly with rapid urbanization; however, the treatment options for AD are lacking because the commonly used therapies can only alleviate symptoms. Limosilactobacillus reuteri (L. reuteri), FN041 is a specific strain isolated from human breast milk, and its protective potential against AD has been confirmed. This study aims to assess the efficacy of maternal consumption of L. reuteri FN041 during late pregnancy and lactation in preventing infantile AD.MethodsFirst, a randomized, double-blind, placebo-controlled intervention study will be conducted on 340 pregnant females with babies at high risk for AD. These subjects will be randomly divided into four groups of different doses of L. reuteri FN041 (1 × 109, 5 × 109, and 1 × 1010 CFU/d) along with a placebo. The safety and efficacy of maternal use of L. reuteri FN041 for preventing infantile AD will be analyzed, and the most efficient dosage of L. reuteri FN041 will be determined. Subsequently, a multicenter cohort study of 500 pregnant females with babies at high risk for AD will be conducted to promote the maternal application of L. reuteri FN041. These subjects will be administered L. reuteri FN041 at the optimal dose determined during the first stage of late pregnancy and lactation, and their babies will be analyzed for AD development. Recruitment was initiated in October 2022.DiscussionThe primary outcome is the cumulative incidence of AD at 24 months after maternal consumption of L. reuteri FN041 during late pregnancy and lactation, whereas the secondary outcome is the efficiency of L. reuteri FN041 transfer from the mother’s gut to breast milk and then the infant’s gut after oral supplementation. This study will demonstrate the efficacy of edible probiotics isolated from breast milk in preventing or treating AD in infants. Accordingly, we provide population-based advice for administering specific probiotics for the primary prevention of AD in pregnant females. Understanding the underlying mechanisms of probiotic strains derived from breast milk can promote their application in preventing infant diseases associated with intestinal microbiota imbalance and immune disorders.Clinical trial registrationhttps://www.chictr.org.cn/, identifier [ChiCTR2300075611]

Exploring the Potential of Integrated Optical Sensing and Communication (IOSAC) Systems with Si Waveguides for Future Networks

Advanced silicon photonic technologies enable integrated optical sensing and communication (IOSAC) in real time for the emerging application requirements of simultaneous sensing and communication for next-generation networks. Here, we propose and demonstrate the IOSAC system on the silicon nitride (SiN) photonics platform. The IOSAC devices based on microring resonators are capable of monitoring the variation of analytes, transmitting the information to the terminal along with the modulated optical signal in real-time, and replacing bulk optics in high-precision and high-speed applications. By directly integrating SiN ring resonators with optical communication networks, simultaneous sensing and optical communication are demonstrated by an optical signal transmission experimental system using especially filtering amplified spontaneous emission spectra. The refractive index (RI) sensing ring with a sensitivity of 172 nm/RIU, a figure of merit (FOM) of 1220, and a detection limit (DL) of 8.2*10-6 RIU is demonstrated. Simultaneously, the 1.25 Gbps optical on-off-keying (OOK) signal is transmitted at the concentration of different NaCl solutions, which indicates the bit-error-ratio (BER) decreases with the increase in concentration. The novel IOSAC technology shows the potential to realize high-performance simultaneous biosensing and communication in real time and further accelerate the development of IoT and 6G networks.Comment: 11pages, 5 figutre

Influenza and COVID-19 co-infection and vaccine effectiveness against severe cases: a mathematical modeling study

BackgroundInfluenza A virus have a distinctive ability to exacerbate SARS-CoV-2 infection proven by in vitro studies. Furthermore, clinical evidence suggests that co-infection with COVID-19 and influenza not only increases mortality but also prolongs the hospitalization of patients. COVID-19 is in a small-scale recurrent epidemic, increasing the likelihood of co-epidemic with seasonal influenza. The impact of co-infection with influenza virus and SARS-CoV-2 on the population remains unstudied.MethodHere, we developed an age-specific compartmental model to simulate the co-circulation of COVID-19 and influenza and estimate the number of co-infected patients under different scenarios of prevalent virus type and vaccine coverage. To decrease the risk of the population developing severity, we investigated the minimum coverage required for the COVID-19 vaccine in conjunction with the influenza vaccine, particularly during co-epidemic seasons.ResultCompared to the single epidemic, the transmission of the SARS-CoV-2 exhibits a lower trend and a delayed peak when co-epidemic with influenza. Number of co-infection cases is higher when SARS-CoV-2 co-epidemic with Influenza A virus than that with Influenza B virus. The number of co-infected cases increases as SARS-CoV-2 becomes more transmissible. As the proportion of individuals vaccinated with the COVID-19 vaccine and influenza vaccines increases, the peak number of co-infected severe illnesses and the number of severe illness cases decreases and the peak time is delayed, especially for those >60 years old.ConclusionTo minimize the number of severe illnesses arising from co-infection of influenza and COVID-19, in conjunction vaccinations in the population are important, especially priority for the elderly