Super‐Resolution Confocal Microscopy Through Pixel Reassignment

Confocal microscopy has gained great popularity in the observation of biological microstructures and dynamic processes. Its resolution enhancement comes from shrinking the pinhole size, which, however, degrades imaging signal‐to‐noise ratio (SNR) severely. Recently developed super‐resolution method based on the pixel reassignment technique is capable of achieving a factor of 2 resolution improvement and further reaching twofold improvement by deconvolution, compared with the optical diffraction limit. More importantly, the approach allows better imaging SNR when its lateral resolution is similar to the standard confocal microscopy. Pixel reassignment can be realized both computationally and optically, but the optical realization demonstrates much faster acquisition of super‐resolution imaging. In this chapter, the development and advancement of super‐resolution confocal microscopy through the pixel realignment method are summarized, and its capabilities of imaging biological structures and interactions are represented

Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control

Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but current RL-based TSC methods are mainly trained in simulation and suffer from the performance gap between simulation and the real world. In this paper, we propose a simulation-to-real-world (sim-to-real) transfer approach called UGAT, which transfers a learned policy trained from a simulated environment to a real-world environment by dynamically transforming actions in the simulation with uncertainty to mitigate the domain gap of transition dynamics. We evaluate our method on a simulated traffic environment and show that it significantly improves the performance of the transferred RL policy in the real world.Comment: 8 pages, 3 figure

LLM Powered Sim-to-real Transfer for Traffic Signal Control

Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and mitigate congestion waste. In recent, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion headaches. However, there still exist performance gaps when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulator and the real-world environments. The Large Language Models (LLMs) are trained on mass knowledge and proved to be equipped with astonishing inference abilities. In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation. Accepting the cloze prompt template, and then filling in the answer based on accessible context, the pre-trained LLM's inference ability is exploited and applied to understand how weather conditions, traffic states, and road types influence traffic dynamics, being aware of this, the policies' action is taken and grounded based on realistic dynamics, thus help the agent learn a more realistic policy. We conduct experiments using DQN to show the effectiveness of the proposed PromptGAT's ability in mitigating the performance gap from simulation to reality (sim-to-real).Comment: 9 pages, 7 figure

CrowdGAIL: A spatiotemporal aware method for agent navigation

Agent navigation has been a crucial task in today's service and automated factories. Many efforts are to set specific rules for agents in a certain scenario to regulate the agent's behaviors. However, not all situations could be in advance considered, which might lead to terrible performance in a real-world application. In this paper, we propose CrowdGAIL, a method to learn from expert behaviors as an instructing policy, can train most 'human-like' agents in navigation problems without manually setting any reward function or beforehand regulations. First, the proposed model structure is based on generative adversarial imitation learning (GAIL), which imitates how humans take actions and move toward the target to a maximum extent, and by comparison, we prove the advantage of proximal policy optimization (PPO) to trust region policy optimization, thus, GAIL-PPO is what we base. Second, we design a special Sequential DemoBuffer compatible with the inner long short-term memory structure to apply spatiotemporal instruction on the agent's next step. Third, the paper demonstrates the potential of the model with an integrated social manner in a multi-agent scenario by considering human collision avoidance as well as social comfort distance. At last, experiments on the generated dataset from CrowdNav verify how close our model would act like a human being in the trajectory aspect and also how it could guide the multi-agents by avoiding any collision. Under the same evaluation metrics, CrowdGAIL shows better results compared with classic Social-GAN

LibSignal: An Open Library for Traffic Signal Control

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators.Comment: 11 pages + 6 pages appendix. Accepted by NeurIPS 2022 Workshop: Reinforcement Learning for Real Life. Website: https://darl-libsignal.github.io

Precipitation Behaviour of Carbonitrides in Ti-Nb-C-N Microalloyed Steels and an Engineering Application with Homogenously Precipitated Nano-particles

A thermodynamic model enabling calculation of equilibrium carbonitride composition and relative amounts as a function of steel composition and temperature has been developed previously based on the chemical equilibrium method. In the present work, actual carbonitride precipitation behaviour has been verified in the Ti-Nb-C-N microalloyed steels. The Ti microalloyed steel after refining with 0.012 % Nb exhibited highly improved tensile strength without sacrificing ductility. According to further detailed SEM and TEM analysis, the improved mechanical properties of Ti/Nb microalloyed steel could be attributed to the larger solubility of Nb and Ti, inducing fine dispersion of the carbonitrides with particle size of 2 – 10 nm in the ferrite matrix.DOI: http://dx.doi.org/10.5755/j01.ms.21.4.9622</p

Secondary Atomization: Drop Breakup in a Continuous Air Jet

Understanding drop breakup will optimize aircraft engine performance, reduce agro-chemical overspray, and improve pharmaceutical tablet efficacy. Large fuel fragments in engines lead to lowered fuel economy and higher pollutant emissions, while small drops yield more agro-spray drift into surrounding residential and environmental zones. Better pharmaceutical tablets will improve drug uptake and patient comfort. Engineers and scientists are currently unable to predict the number, size, and velocity of fragments formed during important drop breakup processes. Therefore, we are required to measure these quantities. We use digital inline holography (DIH) to record three-dimensional diameter and position data for fragments formed during multi-mode breakup. DIH provides 3D images at framing rates 300 times faster than in an IMAX theater. A laser is used as the light source and a high speed camera records the breakup events to video files. A MATLAB script is used to extract the diameters and positions of all fragments in the spray. The data is sorted into bins and histograms are produced which describe the probability of observing a fragment of any particular size and speed. Results show size histograms with more than one peak, a finding in direct contradiction to the last 40 years of spray research. Multiple peaks are indicative of fragmentation processes that occur due to multiple breakup mechanisms, with the number of histogram peaks corresponding to the number of mechanisms (some combination of bag, rim, and/or stamen breakup modes). The histograms will be useful to those modeling sprays in gas turbine engines and industrial sprayers

Clearing Persistent Extracellular Antigen of Hepatitis B Virus: An Immunomodulatory Strategy To Reverse Tolerance for an Effective Therapeutic Vaccination

Development of therapeutic vaccines/strategies to control chronic hepatitis B virus (HBV) infection (CHB) has been challenging due to HBV-induced tolerance. In this study, we explored strategies for breaking tolerance and restoring the immune response to the HBV surface antigen in tolerant mice. We demonstrated that immune tolerance status is attributed to the level and duration of circulating HBsAg in HBV carrier models. Removal of circulating HBsAg by a monoclonal anti-HBsAg antibody in tolerant mice could gradually reduce tolerance and reestablish B cell and CD4+ T cell responses to subsequent Engerix-B vaccination, producing protective IgG. Furthermore, HBsAg-specific CD8+ T cells induced by the addition of a TLR agonist, resulted in clearance of HBV in both serum and liver. Thus, generation of protective immunity can be achieved by clearing extracellular viral antigen with neutralizing antibodies followed by vaccination

Vaccines targeting preS1 domain overcome immune tolerance in hepatitis B virus carrier mice

Strong tolerance to hepatitis B virus (HBV) surface antigens limits the therapeutic effect of the conventional hepatitis B surface antigen (HBsAg) vaccination in both preclinical animal models and patients with chronic hepatitis B (CHB) infection. In contrast, we observed that clinical CHB patients presented less immune tolerance to the preS1 domain of HBV large surface antigen. To study whether targeting the weak tolerance of the preS1 region could improve therapy gain, we explored vaccination with the long peptide of preS1 domain for HBV virions clearance. Our study showed that this preS1-polypeptide rather than HBsAg vaccination induced robust immune responses in HBV carrier mice. The anti-preS1 rapidly cleared HBV virions in vivo and blocked HBV infection to hepatocytes in vitro. Intriguingly, vaccination of preS1-polypeptide even reduced the tolerized status of HBsAg, opening a therapeutic window for the host to respond to the HBsAg vaccine. A sequential administration of antigenically distinct preS1-polypeptide and HBsAg vaccines in HBV carrier mice could finally induce HBsAg/hepatitis B surface antibody serological conversion and clear chronic HBV infection in carrier mice. Conclusion: These results suggest that preS1 can function as a therapeutic vaccine for the control of CHB. (Hepatology 2017;66:1067-1082)