A Novel Non-Volatile Inverter-based CiM: Continuous Sign Weight Transition and Low Power on-Chip Training

In this work, we report a novel design, one-transistor-one-inverter (1T1I), to satisfy high speed and low power on-chip training requirements. By leveraging doped HfO2 with ferroelectricity, a non-volatile inverter is successfully demonstrated, enabling desired continuous weight transition between negative and positive via the programmable threshold voltage (VTH) of ferroelectric field-effect transistors (FeFETs). Compared with commonly used designs with the similar function, 1T1I uniquely achieves pure on-chip-based weight transition at an optimized working current without relying on assistance from off-chip calculation units for signed-weight comparison, facilitating high-speed training at low power consumption. Further improvements in linearity and training speed can be obtained via a two-transistor-one-inverter (2T1I) design. Overall, focusing on energy and time efficiencies, this work provides a valuable design strategy for future FeFET-based computing-in-memory (CiM)

Analytical Nonstationary 3D MIMO Channel Model for Vehicle-to-Vehicle Communication on Slope

Vehicle-to-vehicle communication plays a strong role in modern wireless communication systems, appropriate channel models are of great importance in future research, and propagation environment with slope is one special kind. In this study, a novel three-dimensional nonstationary multiple-input multiple-output channel model for the sub-6 GHz band is proposed. This model is a regular-shaped multicluster geometry-based analytical model, and it combines the line-of-sight component and multicluster scattering rays as the nonline-of-sight components. Each cluster of scatterers represents the influence of different moving vehicles on or near a slope, and scatterers are, respectively, distributed within two spheres around the transmitter and the receiver. In this model, it is considered that the azimuth and elevation angles of departure and arrival are jointly distributed and conform to the von Mises–Fisher distribution, which can easily control the range and concentration of the scatterers within spheres to mimic the real-world situation well. Moreover, the impulse response and the autocorrelation function of the corresponding channel is derived and proposed; then, the Doppler power spectrum density of the channel is simulated and analyzed. In addition, the nonstationary characteristics of the presented channel model are observed through simulations. Finally, the simulation results are compared with measurement data in order to validate the utility of the proposed model

LncRNA HULC mediates radioresistance via autophagy in prostate cancer cells

Prostate cancer (PCa) is the second leading cause of cancer death in men. Irradiation is one of the available options for treatment of PCa, however, approximately 10–45% of PCa are resistant to irradiation. We aimed to explore the role of long non-coding RNA highly upregulated in liver cancer (HULC) in the sensitivity of PCa cells to irradiation. Survival rate, cell apoptosis, cycle, expressions of related proteins, and caspase-3 activity were assessed to explore the effects of HULC on sensitivity of PCa cells to irradiation. Expression of HULC in DU-145, PC3, LNCaP, and RWPE-1 cells was determined and the influence of HULC on DU-145 cells was explored. Then, PC3 cells aberrantly expressing HULC were implanted into NOD-SCID mice for tumor xenograft study. Changes of autophagy after aberrant expression of HULC in vivo and in vitro were tested. Furthermore, the interacted protein of HULC and involved signaling pathway were investigated. In PC3 and LNCaP cells under irradiation, survival rate and cell cycle were decreased and apoptosis was increased by HULC knockdown. HULC knockdown arrested PC3 cells at G0/G1 phase. DU-145 was sensitive to irradiation, and resistance to irradiation of DU-145 cells was enhanced by HULC overexpression. Moreover, HULC knockdown enhanced the sensitivity of PC3 xenografts to irradiation. HULC knockdown promoted autophagy through interaction with Beclin-1 and inhibition of mTOR, resulting in increased apoptosis. HULC knockdown improved sensitivity of PCa cells to irradiation both in vivo and in vitro. HULC suppressed Beclin-1 phosphorylation, thereby reduced autophagy, involving the mTOR pathway

Top-Gate Short Channel Amorphous Indium-Gallium-Zinc-Oxide Thin Film Transistors with Sub-1.2 nm Equivalent Oxide Thickness

10.1109/jeds.2021.3116763IEEE Journal of the Electron Devices Society91125-113

Asymmetric Adaptive Fusion in a Two-Stream Network for RGB-D Human Detection

In recent years, human detection in indoor scenes has been widely applied in smart buildings and smart security, but many related challenges can still be difficult to address, such as frequent occlusion, low illumination and multiple poses. This paper proposes an asymmetric adaptive fusion two-stream network (AAFTS-net) for RGB-D human detection. This network can fully extract person-specific depth features and RGB features while reducing the typical complexity of a two-stream network. A depth feature pyramid is constructed by combining contextual information, with the motivation of combining multiscale depth features to improve the adaptability for targets of different sizes. An adaptive channel weighting (ACW) module weights the RGB-D feature channels to achieve efficient feature selection and information complementation. This paper also introduces a novel RGB-D dataset for human detection called RGBD-human, on which we verify the performance of the proposed algorithm. The experimental results show that AAFTS-net outperforms existing state-of-the-art methods and can maintain stable performance under conditions of frequent occlusion, low illumination and multiple poses

A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics

With the development of deep learning, researchers design deep network structures in order to extract rich high-level semantic information. Nowadays, most popular algorithms are designed based on the complexity of visible image features. However, compared with visible image features, infrared image features are more homogeneous, and the application of deep networks is prone to extracting redundant features. Therefore, it is important to prune the network layers where redundant features are extracted. Therefore, this paper proposes a pruning method for deep convolutional network based on heat map generation metrics. The ‘network layer performance evaluation metrics’ are obtained from the number of pixel activations in the heat map. The network layer with the lowest ‘network layer performance evaluation metrics’ is pruned. To address the problem that the simultaneous deletion of multiple structures may result in incorrect pruning, the Alternating training and self-pruning strategy is proposed. Using a cyclic process of pruning each model once and retraining the pruned model to reduce the incorrect pruning of network layers. The experimental results show that proposed method in this paper improved the performance of CSPDarknet, Darknet and Resnet

Fetal weight estimation based on deep neural network: a retrospective observational study

Abstract Background Improving the accuracy of estimated fetal weight (EFW) calculation can contribute to decision-making for obstetricians and decrease perinatal complications. This study aimed to develop a deep neural network (DNN) model for EFW based on obstetric electronic health records. Methods This study retrospectively analyzed the electronic health records of pregnant women with live births delivery at the obstetrics department of International Peace Maternity & Child Health Hospital between January 2016 and December 2018. The DNN model was evaluated using Hadlock’s formula and multiple linear regression. Results A total of 34824 live births (23922 primiparas) from 49896 pregnant women were analyzed. The root-mean-square error of DNN model was 189.64 g (95% CI 187.95 g—191.16 g), and the mean absolute percentage error was 5.79% (95%CI: 5.70%—5.81%), significantly lower compared to Hadlock’s formula (240.36 g and 6.46%, respectively). By combining with previously unreported factors, such as birth weight of prior pregnancies, a concise and effective DNN model was built based on only 10 parameters. Accuracy rate of a new model increased from 76.08% to 83.87%, with root-mean-square error of only 243.80 g. Conclusions Proposed DNN model for EFW calculation is more accurate than previous approaches in this area and be adopted for better decision making related to fetal monitoring

A van der Waals Synaptic Transistor Based on Ferroelectric Hf 0.5

10.1002/aelm.202000057Advanced Electronic Materials6

A van der Waals Synaptic Transistor Based on Ferroelectric Hf0.5Zr0.5O2 and 2D Tungsten Disulfide

10.1002/aelm.202000057Advanced Electronic Materials6