Nonlinear bayesian filtering with applications to estimation and navigation

In principle, general approaches to optimal nonlinear filtering can be described in a unified way from the recursive Bayesian approach. The central idea to this recur- sive Bayesian estimation is to determine the probability density function of the state vector of the nonlinear systems conditioned on the available measurements. However, the optimal exact solution to this Bayesian filtering problem is intractable since it requires an infinite dimensional process. For practical nonlinear filtering applications approximate solutions are required. Recently efficient and accurate approximate non- linear filters as alternatives to the extended Kalman filter are proposed for recursive nonlinear estimation of the states and parameters of dynamical systems. First, as sampling-based nonlinear filters, the sigma point filters, the unscented Kalman fil- ter and the divided difference filter are investigated. Secondly, a direct numerical nonlinear filter is introduced where the state conditional probability density is calcu- lated by applying fast numerical solvers to the Fokker-Planck equation in continuous- discrete system models. As simulation-based nonlinear filters, a universally effective algorithm, called the sequential Monte Carlo filter, that recursively utilizes a set of weighted samples to approximate the distributions of the state variables or param- eters, is investigated for dealing with nonlinear and non-Gaussian systems. Recentparticle filtering algorithms, which are developed independently in various engineer- ing fields, are investigated in a unified way. Furthermore, a new type of particle filter is proposed by integrating the divided difference filter with a particle filtering framework, leading to the divided difference particle filter. Sub-optimality of the ap- proximate nonlinear filters due to unknown system uncertainties can be compensated by using an adaptive filtering method that estimates both the state and system error statistics. For accurate identification of the time-varying parameters of dynamic sys- tems, new adaptive nonlinear filters that integrate the presented nonlinear filtering algorithms with noise estimation algorithms are derived. For qualitative and quantitative performance analysis among the proposed non- linear filters, systematic methods for measuring the nonlinearities, biasness, and op- timality of the proposed nonlinear filters are introduced. The proposed nonlinear optimal and sub-optimal filtering algorithms with applications to spacecraft orbit es- timation and autonomous navigation are investigated. Simulation results indicate that the advantages of the proposed nonlinear filters make these attractive alterna- tives to the extended Kalman filter

Two major gate-keepers in the self-renewal of neural stem cells: Erk1/2 and PLCγ1 in FGFR signaling

Neural stem cells are undifferentiated precursor cells that proliferate, self-renew, and give rise to neuronal and glial lineages. Understanding the molecular mechanisms underlying their self-renewal is an important aspect in neural stem cell biology. The regulation mechanisms governing self-renewal of neural stem cells and the signaling pathways responsible for the proliferation and maintenance of adult stem cells remain largely unknown. In this issue of Molecular Brain [Ma DK et al. Molecular genetic analysis of FGFR1 signaling reveals distinct roles of MAPK and PLCγ1 activation for self-renewal of adult neural stem cells. Molecular Brain 2009, 2:16], characterized the different roles of MAPK and PLCγ1 in FGFR1 signaling in the self-renewal of neural stem cells. These novel findings provide insights into basic neural stem cell biology and clinical applications of potential stem-cell-based therapy

MOVIN: Real-time Motion Capture using a Single LiDAR

Recent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full-body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible to end users due to their high cost, the requirement for special skills to operate them, or the discomfort associated with wearable devices. In this paper, we present MOVIN, the data-driven generative method for real-time motion capture with global tracking, using a single LiDAR sensor. Our autoregressive conditional variational autoencoder (CVAE) model learns the distribution of pose variations conditioned on the given 3D point cloud from LiDAR.As a central factor for high-accuracy motion capture, we propose a novel feature encoder to learn the correlation between the historical 3D point cloud data and global, local pose features, resulting in effective learning of the pose prior. Global pose features include root translation, rotation, and foot contacts, while local features comprise joint positions and rotations. Subsequently, a pose generator takes into account the sampled latent variable along with the features from the previous frame to generate a plausible current pose. Our framework accurately predicts the performer's 3D global information and local joint details while effectively considering temporally coherent movements across frames. We demonstrate the effectiveness of our architecture through quantitative and qualitative evaluations, comparing it against state-of-the-art methods. Additionally, we implement a real-time application to showcase our method in real-world scenarios. MOVIN dataset is available at \url{https://movin3d.github.io/movin_pg2023/}

Analysis of Long-Range Transport of Carbon Dioxide and Its High Concentration Events over East Asian Region Using GOSAT Data and GEOS-Chem Modeling

This study aims to evaluate the long-range transport of CO2 in East Asian region, using concentration data in a surface measurement site (Gosan Station), column averaged concentration data of satellite-borne instrument (GOSAT), and GEOS-Chem modeling results for the period of June 2009 to May 2011. We perform a validation of the data from GOSAT and GEOS-Chem with total column observations (TCCON). The analysis of the long-range transport and high concentration (HC) events using surface/satellite observations and modeling results is conducted. During the HC events, the concentrations in CO2 and other air pollutants such as SO2 and CO are higher than that of all episodes. It means that CO2, known as a globally well-mixed gas, may also act as a fingerprint of human activity with unique regional characteristics like other air pollutants. This comprehensive analysis, in particular with GOSAT CO2 observation data, shows that CO2 plume with high concentration can be long-range transported with 1-2 days' duration with regional scale. We can find out with GEOS-Chem tagging simulation that more than 45% of the elevated CO2 concentration over central/eastern China, Korea, and Japan on high concentration days can be explained by emission sources of East Asia mainland.open0

Concurrence of Stevens-Johnson Syndrome and Bilateral Parotitis after Minocycline Therapy

Minocycline is an antibiotic of tetracycline derivatives that is commonly used in the treatment of moderate to severe acne vulgaris. It has been reported to cause rare adverse events from mild cutaneous eruption to severe forms including drug-induced lupus, serum sickness-like reaction, and hypersensitivity reactions, etc. The risks of adverse events attributed to minocycline have not been ascertained reliably and there are concerns about the safety of minocycline which could possibly result in life-threatening events such as the Stevens-Johnson syndrome. Here we demonstrate an unusual case of Stevens-Johnson syndrome in conjunction with bilateral parotitis after the intake of minocycline in a Korean boy suggesting discreet use of the drug