Adaptive Graph-Based Feature Normalization for Facial Expression Recognition

Facial Expression Recognition (FER) suffers from data uncertainties caused by ambiguous facial images and annotators' subjectiveness, resulting in excursive semantic and feature covariate shifting problem. Existing works usually correct mislabeled data by estimating noise distribution, or guide network training with knowledge learned from clean data, neglecting the associative relations of expressions. In this work, we propose an Adaptive Graph-based Feature Normalization (AGFN) method to protect FER models from data uncertainties by normalizing feature distributions with the association of expressions. Specifically, we propose a Poisson graph generator to adaptively construct topological graphs for samples in each mini-batches via a sampling process, and correspondingly design a coordinate descent strategy to optimize proposed network. Our method outperforms state-of-the-art works with accuracies of 91.84% and 91.11% on the benchmark datasets FERPlus and RAF-DB, respectively, and when the percentage of mislabeled data increases (e.g., to 20%), our network surpasses existing works significantly by 3.38% and 4.52%

A novel adaptive back propagation neural network-unscented Kalman filtering algorithm for accurate lithium-ion battery state of charge estimation.

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process

HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm’s searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems

TMK1-mediated auxin signalling regulates differential growth of the apical hook

The plant hormone auxin has crucial roles in almost all aspects of plant growth and development. Concentrations of auxin vary across different tissues, mediating distinct developmental outcomes and contributing to the functional diversity of auxin. However, the mechanisms that underlie these activities are poorly understood. Here we identify an auxin signalling mechanism, which acts in parallel to the canonical auxin pathway based on the transport inhibitor response1 (TIR1) and other auxin receptor F-box (AFB) family proteins (TIR1/AFB receptors)1,2, that translates levels of cellular auxin to mediate differential growth during apical-hook development. This signalling mechanism operates at the concave side of the apical hook, and involves auxin-mediated C-terminal cleavage of transmembrane kinase 1 (TMK1). The cytosolic and nucleus-translocated C terminus of TMK1 specifically interacts with and phosphorylates two non-canonical transcriptional repressors of the auxin or indole-3-acetic acid (Aux/IAA) family (IAA32 and IAA34), thereby regulating ARF transcription factors. In contrast to the degradation of Aux/IAA transcriptional repressors in the canonical pathway, the newly identified mechanism stabilizes the non-canonical IAA32 and IAA34 transcriptional repressors to regulate gene expression and ultimately inhibit growth. The auxin–TMK1 signalling pathway originates at the cell surface, is triggered by high levels of auxin and shares a partially overlapping set of transcription factors with the TIR1/AFB signalling pathway. This allows distinct interpretations of different concentrations of cellular auxin, and thus enables this versatile signalling molecule to mediate complex developmental outcomes

MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pre-trained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries

Identification of antibodies with non-overlapping neutralization sites that target coxsackievirus A16

手足口病（Hand, Foot and Mouth Disease，HFMD）是一种由人肠道病毒引起的全球性传染病，主要发生于5岁以下的婴幼儿。2月5日，我校夏宁邵教授团队在《细胞》子刊《细胞•宿主与微生物》（Cell Host & Microbe）上在线发表题为“Identification of antibodies with non-overlapping neutralization sites that target coxsackievirus A16”的研究论文。该研究首次揭示了手足口病主要病原体柯萨奇病毒A组16型（CVA16）三种衣壳颗粒形式与三种不同类型的治疗性中和抗体的全面相互作用细节和非重叠的中和表位结构信息，阐明了CVA16成熟颗粒是疫苗候选主要保护性免疫原的理论基础，建立了可指导疫苗研制的免疫原特异检测方法，为CVA16疫苗及抗病毒药物研究提供关键基础。我校夏宁邵教授、李少伟教授、程通副教授和美国加州大学洛杉矶分校纳米系统研究所Z. Hong Zhou（周正洪）教授为该论文的共同通讯作者。我校博士生何茂洲、徐龙发博士后、郑清炳高级工程师、博士生朱瑞和尹志超为该论文共同第一作者。【Abstract】Hand, foot, and mouth disease is a common childhood illness primarily caused by coxsackievirus A16 (CVA16), for which there are no current vaccines or treatments. We identify three CVA16-specific neutralizing monoclonal antibodies (nAbs) with therapeutic potential: 18A7, 14B10, and NA9D7. We present atomic structures of these nAbs bound to all three viral particle forms—the mature virion, A-particle, and empty particle—and show that each Fab can simultaneously occupy the mature virion. Additionally, 14B10 or NA9D7 provide 100% protection against lethal CVA16 infection in a neonatal mouse model. 18A7 binds to a non-conserved epitope present in all three particles, whereas 14B10 and NA9D7 recognize broad protective epitopes but only bind the mature virion. NA9D7 targets an immunodominant site, which may overlap the receptor-binding site. These findings indicate that CVA16 vaccines should be based on mature virions and that these antibodies could be used to discriminate optimal virion-based immunogens.This work was supported by grants from the Major Program of National Natural Science Foundation of China ( 81991490 ), the National Science and Technology Major Projects for Major New Drugs Innovation and Development ( 2018ZX09711003-005-003 ), the National Science and Technology Major Project of Infectious Diseases ( 2017ZX10304402-002-003 ), the National Natural Science Foundation of China ( 31670933 and 81801646 ), the China Postdoctoral Science Foundation ( 2018M640599 and 2019T120557 ), the Principal Foundation of Xiamen University ( 20720190117 ), and the National Institutes of Health ( R37-GM33050 , GM071940 , DE025567 , and AI094386 ). 该研究获得了国家自然科学基金、新药创制国家科技重大专项、传染病防治国家科技重大专项和美国国立卫生研究院基金的资助