Review of computational methods for estimating cell potency from single-cell RNA-seq data, with a detailed analysis of discrepancies between method description and code implementation

In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden dynamic cellular processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring trajectories (or pseudotime) and RNA velocity, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as "cell potency." In this review, we provide a comprehensive summary of 11 computational methods that estimate cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: mean-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition of each method. Moreover, we use a unified set of mathematical notations to detail the 11 methods' methodologies and summarize their usage complexities, including the number of ad-hoc parameters, the number of required inputs, and the existence of discrepancies between the method description in publications and the method implementation in software packages. Realizing the conceptual contradictions of existing methods and the difficulty of fair benchmarking without single-cell-level ground truths, we conclude that accurate estimation of cell potency from scRNA-seq data remains an open challenge

High-Quality 3D Face Reconstruction with Affine Convolutional Networks

Recent works based on convolutional encoder-decoder architecture and 3DMM parameterization have shown great potential for canonical view reconstruction from a single input image. Conventional CNN architectures benefit from exploiting the spatial correspondence between the input and output pixels. However, in 3D face reconstruction, the spatial misalignment between the input image (e.g. face) and the canonical/UV output makes the feature encoding-decoding process quite challenging. In this paper, to tackle this problem, we propose a new network architecture, namely the Affine Convolution Networks, which enables CNN based approaches to handle spatially non-corresponding input and output images and maintain high-fidelity quality output at the same time. In our method, an affine transformation matrix is learned from the affine convolution layer for each spatial location of the feature maps. In addition, we represent 3D human heads in UV space with multiple components, including diffuse maps for texture representation, position maps for geometry representation, and light maps for recovering more complex lighting conditions in the real world. All the components can be trained without any manual annotations. Our method is parametric-free and can generate high-quality UV maps at resolution of 512 x 512 pixels, while previous approaches normally generate 256 x 256 pixels or smaller. Our code will be released once the paper got accepted.Comment: 9 pages, 11 figure

Quantifying the Individual Differences of Driver' Risk Perception with Just Four Interpretable Parameters

There will be a long time when automated vehicles are mixed with human-driven vehicles. Understanding how drivers assess driving risks and modelling their individual differences are significant for automated vehicles to develop human-like and customized behaviors, so as to gain people's trust and acceptance. However, the reality is that existing driving risk models are developed at a statistical level, and no one scenario-universal driving risk measure can correctly describe risk perception differences among drivers. We proposed a concise yet effective model, called Potential Damage Risk (PODAR) model, which provides a universal and physically meaningful structure for driving risk estimation and is suitable for general non-collision and collision scenes. In this paper, based on an open-accessed dataset collected from an obstacle avoidance experiment, four physical-interpretable parameters in PODAR, including prediction horizon, damage scale, temporal attenuation, and spatial attention, are calibrated and consequently individual risk perception models are established for each driver. The results prove the capacity and potential of PODAR to model individual differences in perceived driving risk, laying the foundation for autonomous driving to develop human-like behaviors.Comment: 14 pages, 9 figures, 1 tabl

Speed Adaptive Sliding Mode Control with an Extended State Observer for Permanent Magnet Synchronous Motor

The sliding mode control (SMC) strategy is employed to a permanent magnet synchronous motor (PMSM) vector control system in this study to improve system robustness against parameter variations and load disturbance. To decrease the intrinsic chattering behavior of SMC, a speed SMC with an adaptive law and an extended state observer (ESO) is proposed. In this method, based on the Lyapunov stability theorem, adaptive estimation laws are deduced to estimate uncertainties of a PMSM caused by parameter variations and unmodeled dynamics. Online estimated uncertainties can be used to eliminate the effect caused by the real uncertainties. In addition, an ESO is applied to observe the load disturbance in real time. The load disturbance observed value is then utilized to the output side of the speed adaptive SMC controller as feed-forward compensation. Both the simulation and experiment results demonstrate that the proposed approach effectively alleviates system chattering and enhances system robustness against uncertainty and load disturbance