Consistency models imitate the multi-step sampling of score-based diffusion in a single forward pass of a neural network. They can be learned in two ways: consistency distillation and consistency training. The former relies on the true velocity field of the corresponding differential equation, approximated by a pre-trained neural network. In contrast, the latter uses a single-sample Monte Carlo estimate of this velocity field. The related estimation error induces a discrepancy between consistency distillation and training that, we show, still holds in the continuous-time limit. To alleviate this issue, we propose a novel flow that transports noisy data towards their corresponding outputs derived from a consistency model. We prove that this flow reduces the previously identified discrepancy and the noise-data transport cost. Consequently, our method not only accelerates consistency training convergence but also enhances its overall performance. The code is available at https://github.com/thibautissenhuth/consistency_GC.

Prediction of large-scale water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks can benefit from the high spatial resolution soil moisture (SM) data of satellite and modeled products because antecedent SM conditions in the topsoil layer govern the partitioning of precipitation into infiltration and runoff. SM data retrieved from Soil Moisture Active Passive (SMAP) have proved to be an effective method of monitoring SM content at different spatial resolutions: 1) radiometer-based product gridded at 36 km; 2) radiometer-only enhanced posting product gridded at 9 km; and 3) SMAP/Sentinel-1A/B products at 3 and 1 km. In this article, we focused on 9-, 3-, and 1-km SM products: three products were validated against in situ data using conventional and triple collocation analysis (TCA) statistics and were then merged with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) were applied for further evaluation of the three SM products, and the maximize-R method was applied to combine SMAP and NoahMP36 SM data. CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance: R and ubRMSD values of the CDF-matched SMAP products were 0.658, 0.626, and 0.570 and 0.049, 0.053, and 0.055 m3/m3, respectively. When SMAP and NoahMP36 were combined, the R-values for the 9-, 3-, and 1-km SMAP SM data were greatly improved: R-values were 0.825, 0.804, and 0.795, and ubRMSDs were 0.034, 0.036, and 0.037 m3/m3, respectively. These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for creating further applications of LSMs with improved accuracy.

This paper discusses a technique for analyzing real data from flash memory cells. The goal is to identify and isolate various sources that cause the shifts and variations of the read values with respect to the intended write values. The analysis reveals how the neighboring cells interfere with the victim cell. Using the proposed analysis technique, the contribution of a specified set of neighboring cells towards the random read variation of the victim cell can be also quantified accurately.

In fabricating microelectromechanical systems (MEMS), bulk micromachining using [100] and [110] single crystal silicon and surface micromachining using polycrystalline silicon are used. However, both micromachining methods have drawbacks, and micromachining actuating or sensing MEMS using single crystal silicon has been an active research topic in resent years. This paper presents electrostatic actuation of a resonator fabricated by the SBM (surface/bulk micromachining) process. The SBM process allows fabricating released structures in single crystal silicon. To fabricate electrodes and to electrically isolate them, a junction isolation method using reverse-biased diodes is developed. The breakdown voltage of this isolation method is measured to be larger than 150 volts. A SBM processed microresonator is actuated at 36 kHz. A displacement of several /spl mu/m is achieved in atmosphere with a 20 volts peak-to-peak supply.