Spin-NeuroMem: A Low-Power Neuromorphic Associative Memory Design Based on Spintronic Devices

Biologically-inspired computing models have made significant progress in recent years, but the conventional von Neumann architecture is inefficient for the large-scale matrix operations and massive parallelism required by these models. This paper presents Spin-NeuroMem, a low-power circuit design of Hopfield network for the function of associative memory. Spin-NeuroMem is equipped with energy-efficient spintronic synapses which utilize magnetic tunnel junctions (MTJs) to store weight matrices of multiple associative memories. The proposed synapse design achieves as low as 17.4% power consumption compared to the state-of-the-art synapse designs. Spin-NeuroMem also encompasses a novel voltage converter with 60% less transistor usage for effective Hopfield network computation. In addition, we propose an associative memory simulator for the first time, which achieves a 5.05Mx speedup with a comparable associative memory effect. By harnessing the potential of spintronic devices, this work sheds light on the development of energy-efficient and scalable neuromorphic computing systems. The source code will be publicly available after the manuscript is reviewed

The plasma level changes of VEGF and soluble VEGF receptor-1 are associated with high-altitude pulmonary edema

Hypoxia-induced plasma levels of VEGF and sFlt-1 are responsible for increased vascular permeability occurred in both brain and pulmonary edema. Currently, it remains unclear the exact roles of VEGF and sFlt-1 in High Altitude Pulmonary Edema (HAPE) pathogenesis. In this study, plasma levels of VEGF and sFlt-1 from 10 HAPE and 10 non-HAPE subjects were measured and compared. The results showed that plasma levels of both VEGF and sFlt-1 in HAPE patients were significantly increased as compared to the non-HAPE group. Interestingly, increased plasma levels of these two protein factors were markedly reduced after treatments. As compared to VEGF, sFlt-1 was much more affected by hypoxia and treatments, suggesting this factor was a key factor contributed to HAPE pathogenesis. Importantly, the ratio of sFlt-1 and VEGF in group of either non-HAPE or HAPE after recovery was significantly lower than the ratio in HAPE patients prior to treatments. Our findings suggested that sFlt-1 was a key factor that involved in HAPE pathogenesis and the sFlt-1/VEGF ratio could be used as a sensitive diagnostic marker for HAPE

Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China

As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China

Advances in research of risk factors for acute high-altitude sickness

Acute high-altitude sickness, also known as acute mountain sickness (AMS) or acute mild altitude sickness, seriously affects the health of individuals entering the plateau and compromises their capacities for military operations. Multiple risk factors affect the occurrence and progression of AMS. Herein we analyze the factors contributing to AMS in light of the nervous and circulatory systems, psychological factors, altitudes and low temperatures, smoking and drinking, gender and age, and transportation methods. We reviewed the research progress in the risk factors of AMS to provide evidence for developing AMS prevention and treatment measures, thereby reducing the occurrence of AMS and improving the combat performance of military forces entering high-altitude areas

Altimeter Observation-Based Eddy Nowcasting Using an Improved Conv-LSTM Network

Eddies can be identified and tracked based on satellite altimeter data. However, few studies have focused on nowcasting the evolution of eddies using remote sensing data. In this paper, an improved Convolutional Long Short-Term Memory (Conv-LSTM) network named Prednet is used for eddy nowcasting. Prednet, which uses a deep, recurrent convolutional network with both bottom-up and top-down connects, has the ability to learn the temporal and spatial relationships associated with time series data. The network can effectively simulate and reconstruct the spatiotemporal characteristics of the future sea level anomaly (SLA) data. Based on the SLA data products provided by Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) from 1993 to 2018, combined with an SLA-based eddy detection algorithm, seven-day eddy nowcasting experiments are conducted on the eddies in South China Sea. The matching ratio is defined as the percentage of true eddies that can be successfully predicted by Conv-LSTM network. On the first day of the nowcasting, matching ratio for eddies with diameters greater than 100 km is 95%, and the average matching ratio of the seven-day nowcasting is approximately 60%. In order to verify the performance of nowcasting method, two experiments were set up. A typical anticyclonic eddy shedding from Kuroshio in January 2017 was used to verify this nowcasting algorithm’s performance on single eddy, with the mean eddy center error is 11.2 km. Moreover, compared with the eddies detected in the Hybrid Coordinate Ocean Model data set (HYCOM), the eddies predicted with Conv-LSTM networks are closer to the eddies detected in the AVISO SLA data set, indicating that deep learning method can effectively nowcast eddies