249 research outputs found
Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM
In the past few years, more and more AI applications have been applied to
edge devices. However, models trained by data scientists with machine learning
frameworks, such as PyTorch or TensorFlow, can not be seamlessly executed on
edge. In this paper, we develop an end-to-end code generator parsing a
pre-trained model to C source libraries for the backend using MicroTVM, a
machine learning compiler framework extension addressing inference on bare
metal devices. An analysis shows that specific compute-intensive operators can
be easily offloaded to the dedicated accelerator with a Universal Modular
Accelerator (UMA) interface, while others are processed in the CPU cores. By
using the automatically generated ahead-of-time C runtime, we conduct a hand
gesture recognition experiment on an ARM Cortex M4F core.Comment: CODAI 2022 Workshop - Embedded System Week (ESWeek
Observation of zero resistance above 100 K in PbCu(PO)O
Room-temperature superconductivity has always been regarded as the ultimate
goal in the fields of solid-state physics and materials science, with its
realization holding revolutionary significance, capable of triggering
significant changes in energy transmission and storage. However, achieving it
poses various challenges. Recent research revealed that material
PbCu(PO)O displays room-temperature superconductivity
under atmospheric pressure, sparking global interest in further exploration.
Here, we utilized solid-phase synthesis to obtain a polycrystalline sample of
PbCu(PO)O. X-ray diffraction confirmed its structural
consistency with referenced literature. Zero resistance, which is important
evidence for superconductivity, was observed above 100 K under ambient
pressure in our experiment. Our finding indicates that
PbCu(PO)O is a possible candidate for searching
high-temperature superconductors.Comment: 7 pages, 3 figure
SiteFinding-PCR: a simple and efficient PCR method for chromosome walking
In this paper, we present a novel PCR method, termed SiteFinding-PCR, for gene or chromosome walking. The PCR was primed by a SiteFinder at a low temperature, and then the target molecules were amplified exponentially with gene-specific and SiteFinder primers, and screened out by another gene-specific primer and a vector primer. However, non-target molecules could not be amplified exponentially owing to the suppression effect of stem–loop structure and could not be screened out. This simple method proved to be efficient, reliable, inexpensive and time-saving, and may be suitable for the molecules for which gene-specific primers are available. More importantly, large DNA fragments can be obtained easily using this method. To demonstrate the feasibility and efficiency of SiteFinding-PCR, we employed this method to do chromosome walking and obtained 16 positive results from 17 samples
A 10 km daily-level ultraviolet radiation predicting dataset based on machine learning models in China from 2005 to 2020
Ultraviolet (UV) radiation is closely related to health, but limited measurements hindered further investigation of its health effects in China. Machine learning algorithm has been widely used in predicting environmental factors with high accuracy, but limited studies have done for UV radiation. This study aimed to develop UV radiation prediction model based on random forest method, and predict UV radiation at daily level and 10 km resolution in mainland China in 2005–2020. A random forest model was employed to predict UV radiation by integrating ground UV radiation measurements from monitoring stations and multiple predictors, such as UV radiation data from satellite. Missing data of satellite-based UV radiation was filled by three-day moving average method. The model's performance was evaluated through multiple cross-validation (CV) methods. The overall R2 (root mean square error, RMSE) between measured and predicted UV radiation from model development and model 10-fold CV was 0.97 (15.64 W m-2) and 0.83 (37.44 W m-2) at daily level, respectively. The model with OMI EDD performed higher predicting accuracy than the one without it. Based on predictions of UV radiation at daily level and 10 km spatial resolution and nearly 100 % spatiotemporal coverage, we found UV radiation increased by 4.20 % while PM2.5 levels decreased by 48.51 % and O3 levels rose by 22.70 % in 2013–2020, suggesting a potential correlation among these environmental factors. Uneven spatial distribution of UV radiation was found to be associated with factors such as latitude, elevation, meteorological factors and seasons. The eastern areas of China posed higher risk with both high population density and UV radiation intensity. Based on machine learning algorithm, this study generated a gridded dataset characterized by relatively high precision and extensive spatiotemporal coverage of UV radiation, which demonstrates the spatiotemporal variability of UV radiation levels in China and can facilitate health-related research in the future. This dataset is currently freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024)
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