PYTAF: A Python Tool for Spatially Resampling Earth Observation Data.

Earth observation data have revolutionized Earth science and significantly enhanced the ability to forecast weather, climate and natural hazards. The storage format of the majority of Earth observation data can be classified into swath, grid or point structures. Earth science studies frequently involve resampling between swath, grid and point data when combining measurements from multiple instruments, which can provide more insights into geophysical processes than using any single instrument alone. As the amount of Earth observation data increases each day, the demand for a high computational efficient tool to resample and fuse Earth observation data has never been greater. We present a software tool, called pytaf, that resamples Earth observation data stored in swath, grid or point structures using a novel block indexing algorithm. This tool is specially designed to process large scale datasets. The core functions of pytaf were implemented in C with OpenMP to enable parallel computations in a shared memory environment. A user-friendly python interface was also built. The tool has been extensively tested on supercomputers and successfully used to resample the data from five instruments on the EOS-Terra platform at a mission-wide scale.

Towards artificial general intelligence via a multimodal foundation model.

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".

Lens-free motion analysis via neuromorphic laser speckle imaging.

Laser speckle imaging (LSI) is a powerful tool for motion analysis owing to the high sensitivity of laser speckles. Traditional LSI techniques rely on identifying changes from the sequential intensity speckle patterns, where each pixel performs synchronous measurements. However, a lot of redundant data of the static speckles without motion information in the scene will also be recorded, resulting in considerable resources consumption for data processing and storage. Moreover, the motion cues are inevitably lost during the "blind" time interval between successive frames. To tackle such challenges, we propose neuromorphic laser speckle imaging (NLSI) as an efficient alternative approach for motion analysis. Our method preserves the motion information while excluding the redundant data by exploring the use of the neuromorphic event sensor, which acquires only the relevant information of the moving parts and responds asynchronously with a much higher sampling rate. This neuromorphic data acquisition mechanism captures fast-moving objects on the order of microseconds. In the proposed NLSI method, the moving object is illuminated using a coherent light source, and the reflected high frequency laser speckle patterns are captured with a bare neuromorphic event sensor. We present the data processing strategy to analyze motion from event-based laser speckles, and the experimental results demonstrate the feasibility of our method at different motion speeds.

Impact of anodophilic biofilm bioelectroactivity on the denitrification behavior of air-cathode microbial fuel cells.

Generally, high bioelectroactivity of anodophilic biofilm favors high power generation of microbial fuel cell (MFC); however, it is not clear whether it can promote denitrification of MFC synchronously. In this study, we studied the impact of anodophilic biofilm bioelectroactivity on the denitrification behavior of air-cathode MFC (AC-MFC) in steady state and found that high bioelectroactivity of anodophilic biofilm not only favored high power generation of the AC-MFC, but also promoted the growth of denitrifers at the anodes and strengthened denitrification. Anodophilic biofilms of AC-MFC with various bioelectroactivity were acclimated at conditions of open circuit (OC), Rext of 1000 Ω and 20 Ω (denoted as AC-MFC-OC, AC-MFC-1000Ω, and AC-MFC-20Ω, respectively) and performed for over 100 days. Electrochemical tests and microbial analysis results showed that the anode of the AC-MFC-20Ω delivered higher current response of both oxidation and denitrification and had higher abundance of electroactive bacteria than the AC-MFC-OC, AC-MFC-1000Ω, demonstrating a higher bioelectroactivity of the anodophilic biofilms. Moreover, these electroactive bacteria favored the accumulation of denitrifers, like Thauera and Alicycliphilus, probably by consuming trace oxygen through catalyzing oxygen reduction. The AC-MFC-20Ω not only delivered a 61.7% higher power than the AC-MFC-1000Ω, but also achieved a stable and high denitrification rate constant (kDN ) of 1.9 h-1 , which was 50% and 40% higher than that of the AC-MFC-OC and AC-MFC-1000Ω, respectively. It could be concluded that the high bioelectroactivity of the anodophilic biofilms not only favored high power generation of the AC-MFC, but also promoted the enrichment of denitrifers at the anodes and strengthened denitrification. This study provided an effective method for enhancing power generation and denitrification performance of the AC-MFC synchronously.

An Evaluation of Geo-located Twitter Data for Measuring Human Migration.

This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.

Event-based laser speckle correlation for micro motion estimation: erratum.

We present an erratum to our Letter [Opt. Lett.46, 3885 (2021)OPLEDP0146-959210.1364/OL.430419]. This erratum corrects an inadvertent error in Eq. (4). The corrections have no influence on the results and conclusions of the original Letter.

Event-based laser speckle correlation for micro motion estimation.

Micro motion estimation has important applications in various fields such as microfluidic particle detection and biomedical cell imaging. Conventional methods analyze the motion from intensity images captured using frame-based imaging sensors such as the complementary metal-oxide semiconductor (CMOS) and the charge-coupled device (CCD). Recently, event-based sensors have evolved with the special capability to record asynchronous light changes with high dynamic range, high temporal resolution, low latency, and no motion blur. In this Letter, we explore the potential of using the event sensor to estimate the micro motion based on the laser speckle correlation technique.