Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation

A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the capability to characterize the prediction error, generate feedback information from the test sample, and correct the prediction error on the fly for each individual test sample, which results in degraded performance in generalization. In this work, we introduce a self-correctable and adaptable inference (SCAI) method to address the generalization challenge of network prediction and use human pose estimation as an example to demonstrate its effectiveness and performance. We learn a correction network to correct the prediction result conditioned by a fitness feedback error. This feedback error is generated by a learned fitness feedback network which maps the prediction result to the original input domain and compares it against the original input. Interestingly, we find that this self-referential feedback error is highly correlated with the actual prediction error. This strong correlation suggests that we can use this error as feedback to guide the correction process. It can be also used as a loss function to quickly adapt and optimize the correction network during the inference process. Our extensive experimental results on human pose estimation demonstrate that the proposed SCAI method is able to significantly improve the generalization capability and performance of human pose estimation.Comment: Accepted by CVPR 202

Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation

Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradation problem of deep neural networks. We take inspiration from the biological plausibility learning where the neuron responses are tuned based on a local synapse-change procedure and activated by competitive lateral inhibition rules. Based on these feed-forward learning rules, we design a soft Hebbian learning process which provides an unsupervised and effective mechanism for online adaptation. We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer. It is able to fine-tune the neuron responses based on the external feedback generated by the error back-propagation from the top inference layers. This leads to our proposed neuro-modulated Hebbian learning (NHL) method for fully test-time adaptation. With the unsupervised feed-forward soft Hebbian learning being combined with a learned neuro-modulator to capture feedback from external responses, the source model can be effectively adapted during the testing process. Experimental results on benchmark datasets demonstrate that our proposed method can significantly improve the adaptation performance of network models and outperforms existing state-of-the-art methods.Comment: CVPR2023 accepte

Expression analysis of m6A-related genes in various tissues of Meishan pigs at different developmental stages

ABSTRACT To characterize the N6-methyladenosine (m6A)-related gene expression profiles in various tissues of Meishan pigs at different stages, m6A modification-related genes (METTL3, METTL14, METTL16, WTAP, RBM15, and FTO) were detected from newborn to physical maturity of Meishan pigs at eight important developmental stages (1, 7, 14, 21, 28, 35, 134, and 158 days old). The expression of m6A-related genes was tissue-specific. Furthermore, the level of METTL3 messenger RNA (mRNA) was higher on day 35 than in other stages in most tissues, and the expression of METTL14 increased after day 35, and FTO exhibited a peak on day 14 in muscle, intestine, lymph nodes, thymus, and kidney. This study provided a reference for an in-depth study of the expression patterns of m6A modification-related genes in Meishan pigs

Comprehensive transcriptomic and metabolomic analysis of porcine intestinal epithelial cells after PDCoV infection

IntroductionPorcine deltacoronavirus (PDCoV), an emerging swine enteropathogenic coronavirus with worldwide distribution, mainly infects newborn piglets with severe diarrhea, vomiting, dehydration, and even death, causing huge economic losses to the pig industry. However, the underlying pathogenic mechanisms of PDCoV infection and the effects of PDCoV infection on host transcripts and metabolites remain incompletely understood.MethodsThis study investigated a combined transcriptomic and metabolomic analysis of porcine intestinal epithelial cells (IPEC-J2) following PDCoV infection by LC/MS and RNA-seq techniques. A total of 1,401 differentially expressed genes and 254 differentially accumulated metabolites were detected in the comparison group of PDCoV-infected vs. mock-infected.Results and discussionWe found that PDCoV infection regulates gene sets associated with multiple signaling pathways, including the neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, MAPK signaling pathway, chemokine signaling pathway, ras signaling pathway and so on. Besides, the metabolomic results showed that biosynthesis of cofactors, nucleotide metabolism, protein digestion and absorption, and biosynthesis of amino acid were involved in PDCoV infection. Moreover, integrated transcriptomics and metabolomics analyses revealed the involvement of ferroptosis in PDCoV infection, and exogenous addition of the ferroptosis activator erastin significantly inhibited PDCoV replication. Overall, these unique transcriptional and metabolic reprogramming features may provide a better understanding of PDCoV-infected IPEC-J2 cells and potential targets for antiviral treatment

Operating characteristics analysis and capacity configuration optimization of wind-solar-hydrogen hybrid multi-energy complementary system

Wind and solar energy are the important renewable energy sources, while their inherent natures of random and intermittent also exert negative effect on the electrical grid connection. As one of multiple energy complementary route by adopting the electrolysis technology, the wind-solar-hydrogen hybrid system contributes to improving green power utilization and reducing its fluctuation. Therefore, the moving average method and the hybrid energy storage module are proposed, which can smooth the wind-solar power generation and enhance the system energy management. Moreover, the optimization of system capacity configuration and the sensitive analysis are implemented by the MATLAB program platform. The results indicate that the 10-min grid-connected volatility is reduced by 38.7% based on the smoothing strategy, and the internal investment return rate can reach 13.67% when the electricity price is 0.04 $/kWh. In addition, the annual coordinated power and cycle proportion of the hybrid energy storage module are 80.5% and 90%, respectively. The developed hybrid energy storage module can well meet the annual coordination requirements, and has lower levelized cost of electricity. This method provides reasonable reference for designing and optimizing the wind-solar-hydrogen complementary system

Fluorite type oxide-ion conductors : new approaches

The development of electrolyte materials for the high temperature (800-1000 ∘C) solid oxide fuel cell (SOFC) is mostly based on anion-deficient fluorite oxides, especially cubic stabilised zirconia and ceria. The 8 mol % yttria stabilised zirconia (8YSZ) is so far the most commonly used one owing to its fairly high ionic conductivity and good stability at high temperature cell operation. However, new materials with higher ionic conductivities are demanded to reduce the cell ohmic loss at high temperatures. Although doped ceria provides a higher ionic conductivity than doped zirconia, the reduction of Ce⁴⁺ in the fuel cell reducing atmosphere introduces electronic conduction and lattice expansion which can be detrimental to cell performance. The perovskite structured strontium-magnesium doped lanthanum gallate (LSGM) has high ionic conductivity that is comparable to doped ceria. The limitation for this material comes from its interaction with the traditional Ni-YSZ anode. This means developments of new anode materials, or interlayer designs are required. The scandia stabilised zirconia (SSZ) in the doped zirconia family is a very attractive candidate as it offers the highest ionic conductivity amount the doped zirconia systems. However, 11SSZ, which offers the highest ionic conductivity in the Sc₂O₃ − ZrO₂ system that is comparable to doped ceria and LSGM in the high temperature operating range, undergoes a rhombohedral-cubic phase transition at about 600 ∘C, with the cubic phase existing only at T > 600 ∘C. Apart from this phase stability issue, most SSZ compositions show a significant conductivity degradation behaviour over time. Accordingly, this thesis is to investigate new electrolyte materials, with a particular focus on the co-doped 11SSZ systems, that may offer higher ionic conductivities, and improved phase and conductivity stabilities for the high temperature fuel cell application. The material's properties are all related to its underlying chemistry. As a matter of fact, this thesis provides new approaches in evaluating the observed properties and conductivity behaviours in the fluorite materials, links the experimental evidence to its underlying chemistry. This thesis aims to provide a deep level understanding on the fundamental of zirconia and stabilised zirconia: its chemistry and defect structure, in order to uncover the fundamental of phase stabilisation, factors that limit the maximum ionic conductivity and driving forces for the conductivity degradation, etc., in doped zirconia systems. This is extended to all the fluorite-based and related systems. It follows that the electrolyte performance is closely related to its microdomain structural changes. In particular, the problem of conductivity degradation is tackled by the elimination of short-range ordering of oxygen vacancies. Apart from the microdomain structure, the total ionic conductivity is also closely related to the crystal phase assembly and microstructures. The elimination of conductivity degradation in one of the Mg, In co-doped zirconia composition (IMSSZ) significantly improves the long-term conductivity stability, together with a stable, simple crystal structure and a high ionic conductivity (0.14 S cm⁻¹ at 850 ∘C and the ionic conductivity can reach 0.4 S cm⁻¹ at 1000 ∘C). This will contribute to an overall improved cell performance when integrating into the SOFC. The theory, concepts and characterisation methods developed in this study for fluorite and fluorite-related materials, especially those related to microdomain structural studies and characterisations, can be applied further to any energy material with a certain adaptation. This is hoped to provide some insights into new material design, in particular the electrolyte