Rationale: Fetuin-A is a liver-derived plasma protein involved in the regulation of calcified matrix metabolism.Biochemical studies showed that fetuin-A is essential for the formation of protein-mineral complexes, called calciprotein particles (CPPs). CPPs must be cleared from circulation to prevent local deposition and pathological calcification.Objective: We studied CPP clearance in mice and in cell culture to identify the tissues, cells, and receptors involved in the clearance.
Methods and Results:
High-temperature charge transport across an oxide-nitride-oxide sandwich of erasable programmable read only memories is mainly governed by the oxide conductivity as experimentally determined. It was verified in the examined devices that charge loss is not due to mobile ions. Since hole injection from the control gate into the nitride can be blocked by a 70-Å-thick top oxide we conjecture that charge loss is due to leakage of electrons; however, the observed leakage current is too large to be explained by pure electrode-limited charge transport (Richardson emission and direct tunneling). It was also verified that field gain on asperities and along edges cannot increase the charge loss current to the required range. Numerical evaluation of trap tunneling and resonant tunneling indicated that both mechanisms are weakly temperature dependent while charge loss has a typical activation energy of 1.2 eV in the range of 250–350 °C. Consequently, a multiphonon-assisted tunneling mechanism is proposed where electrons stored on the floating gate tunnel to oxide traps, then are emitted into the nitride. The coupling of the trap level to oxide phonons results in virtual energy levels in the oxide which allow for more effective transition paths. As a consequence of the electron-phonon coupling, the emission occurs close to the oxide conduction-band edge at temperatures between 250 and 350 °C, producing a strong temperature dependence for the mechanism.
Automated vehicles need to not only perceive their environment, but also predict the possible future behavior of all detected traffic participants in order to safely navigate in complex scenarios and avoid critical situations, ranging from merging on highways to crossing urban intersections. Due to the availability of datasets with large numbers of recorded trajectories of traffic participants, deep learning based approaches can be used to model the behavior of road users. This paper proposes a convolutional network that operates on rasterized actor-centric images which encode the static and dynamic actor-environment. We predict multiple possible future trajectories for each traffic actor, which include position, velocity, acceleration, orientation, yaw rate and position uncertainty estimates. To make better use of the past movement of the actor, we propose to employ temporal convolutional networks (TCNs) and rely on uncertainties estimated from the previous object tracking stage. We evaluate our approach on the public "Argoverse Motion Forecasting" dataset, on which it won the first prize at the Argoverse Motion Forecasting Challenge, as presented on the NeurIPS 2019 workshop on "Machine Learning for Autonomous Driving".
Distributed scenarios pose a big challenge to tracking and fusion systems. They require the prevention of repeatedly incorporating the same information, which originates from ring closures in the communication path and would affect optimality. Additionally, the multi-sensor multi-object Generalized Labeled Multi-Bernoulli filter update is NP-hard in principle. The method proposed in this paper tackles these problems, as it constitutes a divide and conquer strategy for distributed, synchronized multi-sensor systems with central fusion. Based on a common prediction, local sensor updates are calculated separately, sent back and fused centrally in order to start a new cycle. Thus, the intractable multi-sensor update is split into less complex local single-sensor updates and a novel, low-complexity fusion strategy. The proposed method enables a full parallelization of the optimal multi-sensor Generalized Labeled Multi-Bernoulli and δ-Generalized Labeled Multi-Bernoulli update. Our approach bases on the Bayes Parallel Combination Rule and can be seen as multi-sensor multi-object Information Matrix Fusion for synchronous sensors, which constitutes a perfect choice in centralized systems with distributed sensors. Finally, we compare the proposed method to the Iterator Corrector approach from literature in detailed simulations.
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