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

Stage-Specific Deletion of Olig2 Conveys Opposing Functions on Differentiation and Maturation of Oligodendrocytes

The temporal and spatial patterning involved in the specification, differentiation, and myelination by oligodendroglia is coordinated in part by the activation and repression of various transcriptional programs. Olig2 is a basic helix-loop-helix transcription factor necessary for oligodendroglial development and expressed continuously throughout the lineage. Despite evidence for the critical role of Olig2 in oligodendroglial specification and differentiation, the function for Olig2 during later stages of oligodendroglial development, namely, the transition into mature oligodendrocytes (OLs) and the formation of the myelin sheath, remains unclear. To address the possibility for a stage-specific role, we deleted Olig2 in oligodendrocyte precursor cells (OPCs) under the control of the CNPase-promoter or in immature OLs under the inducible proteolipid protein promoter. As expected, ablation of Olig2 in OPCs significantly inhibits differentiation, resulting in hypomyelination. However, deletion of the Olig2 gene in immature OLs significantly enhances the maturation process and accelerates the kinetics of myelination/remyelination. Underlying the stage-specific roles for Olig2 is the compensatory expression and function of Olig1, a transcription factor that promotes OL maturation and (re)myelination. Olig1 expression is significantly reduced upon Olig2 deletion in OPCs but is dramatically increased by nearly threefold when deleted in immature OLs. By enforcing expression of Olig1 into OPCs in a null Olig2 background, we demonstrate that overexpression of Olig1 is sufficient to rescue the differentiation phenotype and partially compensates for the Olig2 deletion in vitro. Our results suggest a stage-specific regulatory role for Olig2, mediated by Olig1 that conveys opposing functions on the differentiation and maturation of oligodendrocytes

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