A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction

A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work. The Neuron-ADC leverages level-crossing sampling and a bio-inspired refractory circuit to compressively converts bio-signal to digital spikes and information-of-interest. The proposed design can not only avoid dissipating ADC energy on unnecessary data but also achieve reconfigurable sampling, making it appropriate for either low power operation or high accuracy conversion when dealing with various kinds of bio-signals. Moreover, the proposed dynamic comparator can reduce static power up to 41.1% when tested with a 10 kHz sinusoidal input. Simulation results of 40 nm CMOS process show that the Neuron-ADC achieves a maximum ENOB of 6.9 bits with a corresponding FoM of 97 fJ/conversion under 0.6 V supply voltage.Comment: Accepted to 2022 IEEE the 18th Asia Pacific Conference on Circuits and Systems (APCCAS

Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks

Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on substantial 1D sequences have shown burgeoning capabilities with scale in a broad range of applications. Several previous studies consider combining these different protein modalities to promote the representation power of geometric neural networks, but fail to present a comprehensive understanding of their benefits. In this work, we integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks and evaluate a variety of protein representation learning benchmarks, including protein-protein interface prediction, model quality assessment, protein-protein rigid-body docking, and binding affinity prediction. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin and can be generalized to complex tasks

Thermodynamics of Strained Vanadium Dioxide Single Crystals

Vanadium dioxide undergoes a metal-insulator transition, in which the strain condition plays an important role. To investigate the strain contribution, a phenomenological thermodynamic potential for the vanadium dioxide single crystal was constructed. The transformations under the uniaxial stress, wire, and thin film boundary conditions were analyzed, and the corresponding phase diagrams were constructed. The calculated phase diagrams agree well with existing experimental data, and show that the transformation temperature (and Curie temperature) strongly depends on the strain condition

New understandings of the June 24th 2017 Xinmo Landslide, Maoxian, Sichuan, China

On June 24, 2017 (21:39 UTC, June 23rd), a catastrophic landslide occurred at Xinmo village of Mao County, Sichuan Province, China. Soon after the event, some research teams carried out field investigations in order to both support the emergency operations and to understand the failure mechanism and possible evolutionary scenarios. Based on further in-depth interpretation of high-resolution remote-sensing images and detailed field surveys, it is newly found that there are at least six old rockfall deposits in the source area that prove the historic activity of the landslide scarp. Seismic data of the event and morphological evidences along the slope indicate that the landslide was preceded by a significant rockfall. Mechanical calculations show that the surface force due to pore water was far less than the impact force due to the rockfall. It means that the subsequent major rock avalanche was more likely due to the impact of the rockfall on the rock slope below, which broke the rock bridges and caused drop of shear resistance along the fractures. According to these new understandings, a different triggering mechanism for the landslide is proposed

Contactless Electrocardiogram Monitoring with Millimeter Wave Radar

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring