Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Reduced Order Modelling of Soil Structure Interaction Effects via LSTM and Autoencoder Neural Networks

In the field of structural health monitoring (SHM), inverse problems which require repeated analyses are common. With the increase in the use of nonlinear models, the development of nonlinear reduced order modelling techniques is of paramount interest. Of considerable research interest, is the use of flexible and scalable machine learning methods which can learn to approximate the behaviour of nonlinear dynamic systems using input and output data. One such nonlinear system of interest, in the context of wind turbine structures, is the soil structure interaction (SSI) problem. Soil demonstrates strongly nonlinear behaviour with regards to its restoring force and has been shown to considerably influence the dynamic response of wind turbine structures. In this work, we demonstrate the application of a recently developed nonlinear reduced order modelling method, which leverages Autoencoder and LSTM neural networks, to a nonlinear soil structure interaction problem of a wind turbine monopile subject to realistic loading at the seabed level. The accuracy and efficiency of the methodology is compared to full order simulations carried out using Abaqus. The ROM was shown to have good fidelity and a considerable reduction in computational time for the system considered.
Morphisms and Minimisation of Weighted Automata

This paper studies the algorithms for the minimisation of weighted automata. It starts with the definition of morphisms — which generalises and unifies the notion of bisimulation to the whole class of weighted automata — and the unicity of a minimal quotient for every automaton, obtained by partition refinement. From a general scheme for the refinement of partitions, two strategies are considered for the computation of the minimal quotient: the Domain Split and the Predecesor Class Split algorithms. They correspond respectivly to the classical Moore and Hopcroft algorithms for the computation of the minimal quotient of deterministic Boolean automata. We show that these two strategies yield algorithms with the same quadratic complexity and we study the cases when the second one can be improved in order to achieve a complexity similar to the one of Hopcroft algorithm.
Sparse to Dense Dynamic 3D Facial Expression Generation

In this paper, we propose a solution to the task of generating dynamic 3D facial expressions from a neutral 3D face and an expression label. This involves solving two sub-problems: (i) modeling the temporal dynamics of expressions, and (ii) deforming the neutral mesh to obtain the expressive counterpart. We represent the temporal evolution of expressions using the motion of a sparse set of 3D landmarks that we learn to generate by training a manifold-valued GAN (Motion3DGAN). To better encode the expression-induced deformation and disentangle it from the identity information, the generated motion is represented as per-frame displacement from a neutral configuration. To generate the expressive meshes, we train a Sparse2Dense mesh Decoder (S2D-Dec) that maps the landmark displacements to a dense, per-vertex displacement. This allows us to learn how the motion of a sparse set of landmarks influences the deformation of the overall face surface, independently from the identity. Experimental results on the CoMA and D3DFACS datasets show that our solution brings significant improvements with respect to previous solutions in terms of both dynamic expression generation and mesh reconstruction, while retaining good generalization to unseen data. Code and models are available at https://github.com/CRISTAL-3DSAM/Sparse2Dense.
A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving

Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability, whereas the fully end-to-end paradigm has demonstrated considerable simplicity and learnability along with the rise of deep learning. As a way of marrying the advantages of both approaches, learning a semantically meaningful representation and then use in the downstream driving policy learning tasks provides a viable and attractive solution. However, several key challenges remain to be addressed, including identifying the most effective representation, alleviating the sim-to-real generalization issue as well as balancing model training cost. In this study, we propose a versatile and efficient reinforcement learning framework and build a fully functional autonomous vehicle for real-world validation. Our framework shows great generalizability to various complicated real-world scenarios and superior training efficiency against the competing baselines.
Private High-Dimensional Hypothesis Testing

We provide improved differentially private algorithms for identity testing of high-dimensional distributions. Specifically, for d -dimensional Gaussian distributions with known covariance Σ , we can test whether the distribution comes from N ( µ ∗ , Σ) for some fixed µ ∗ or from some N ( µ, Σ) with total variation distance at least α from N ( µ ∗ , Σ) with ( ε, 0) -differential privacy, using only samples if the algorithm is allowed to be computationally inefficient, and only samples for a computationally efficient algorithm. We also provide a matching lower bound showing that our computationally inefficient algorithm has optimal sample complexity. We also extend our algorithms to various related problems, including mean testing of Gaussians with bounded but unknown covariance, uniformity testing of product distributions over {− 1 , 1 } d , and tolerant testing. Our results improve over the previous best work of Canonne et al. (2020b) for both computationally efficient and inefficient algorithms, and even our computationally efficient algorithm matches the optimal non-private sample complexity of O (cid:16) √ dα 2 (cid:17) in many standard parameter settings. In addi-tion, our results show that, surprisingly, private identity testing of d -dimensional Gaussians can be done with fewer samples than private identity testing of discrete distributions over a domain of size d (Acharya et al., 2018b), which refutes a conjectured lower bound of Canonne et al. (2020b).
Identification in Tree-shaped Linear Structural Causal Models

Linear structural equation models represent direct causal effects as directed edges and confounding factors as bidirected edges. An open problem is to identify the causal parameters from correlations between the nodes. We investigate models, whose directed component forms a tree, and show that there, besides classical instrumental variables, missing cycles of bidirected edges can be used to identify the model. They can yield systems of quadratic equations that we explicitly solve to obtain one or two solutions for the causal parameters of adjacent directed edges. We show how multiple missing cycles can be combined to obtain a unique solution. This results in an algorithm that can identify instances that previously required approaches based on Gr\"obner bases, which have doubly-exponential time complexity in the number of structural parameters.
Generalized scale behavior and renormalization group for data analysis

Some recent results showed that the renormalization group (RG) can be considered as a promising framework to address open issues in data analysis. In this work, we focus on one of these aspects, closely related to principal component analysis (PCA) for the case of large dimensional data sets with covariance having a nearly continuous spectrum. In this case, the distinction between ‘noise-like’ and ‘non-noise’ modes becomes arbitrary and an open challenge for standard methods. Observing that both RG and PCA search for simplification for systems involving many degrees of freedom, we aim to use the RG argument to clarify the turning point between noise and information modes. The analogy between coarse-graining renormalization and PCA has been investigated in Bradde and Bialek (2017 J. Stat. Phys. 167 462–75), from a perturbative framework, and the implementation with real sets of data by the same authors showed that the procedure may reflect more than a simple formal analogy. In particular, the separation of sampling noise modes may be controlled by a non-Gaussian fixed point, reminiscent of the behaviour of critical systems. In our analysis, we go beyond the perturbative framework using nonperturbative techniques to investigate non-Gaussian fixed points and propose a deeper formalism allowing us to go beyond power-law assumptions for explicit computations.
Changes of Self-Rated Health Status, Overweight and Physical Activity During Childhood and Adolescence—The Ratchet Effect of High Parental Socioeconomic Status

Childhood and adolescence are important life periods for the development of health status and physical activity (PA) behaviours. This study analyses the stability and potential changes of self-rated health status, overweight and PA behaviour over time, specifically focusing on the age and the socioeconomic status of children and adolescents. We employ representative longitudinal data for German children and adolescents from the Motorik-Modul Study and the German Health Interview and Examination Survey. Using four different dichotomous health status and PA indicators (self-rated health status [SRHS]; overweight; moderate-to-vigorous PA; and leisure sports engagement), we report within-person transition rates across the panel waves when the survey was taken (2003–2006, 2009–2012, and 2014–2017). Additionally, we report results of logistic regressions estimating the impact of children's age, gender, migration background, and their parents' socioeconomic status on these transition rates. The transition rates show mixed results. While children and adolescents from highly problematic states reporting bad SRHS and no leisure sports engagement at an early stage tend to improve later on, overweight children mostly stay overweight. Age and social inequality indicators correlate with some of the chances of improving or worsening the health and PA states. Most clearly, high parental status prevents the health status and PA from worsening over all transitions, particularly becoming overweight, representing a ratchet effect. The results of the present study underline that health policy needs to target specific groups to reduce social inequality in the health status and PA of children and adolescents.
Considerations of Timing Post-ovariectomy in Mice and Rats in Studying Anxiety- and Depression-Like Behaviors Associated With Surgical Menopause in Women

Citation: Rodríguez-Landa JF (2022) Considerations of Timing Post-ovariectomy in Mice and Rats in Studying Anxietyand Depression-Like Behaviors Associated With Surgical Menopause in Women. Front. Behav. Neurosci. 16:829274. doi: 10.3389/fnbeh.2022. 829274 Considerations of Timing Post-ovariectomy in Mice and Rats in Studying Anxietyand Depression-Like Behaviors Associated With Surgical Menopause in Women
Nep1-Like Proteins From the Biocontrol Agent Pythium oligandrum Enhance Plant Disease Resistance Independent of Cell Death and Reactive Oxygen Species

Microbial necrosis and ethylene-inducing peptide 1 (Nep1)-like proteins (NLPs) act as cytolytic toxins and immunogenic patterns in plants. Our previous work shows that cytolytic NLPs (i.e., PyolNLP5 and PyolNLP7) from the biocontrol agent Pythium oligandrum enhance plant resistance against Phytophthora pathogens by inducing the expression of plant defensins. However, the relevance between PyolNLP-induced necrosis and plant resistance activation is still unclear. Here, we find that the necrosis-inducing activity of PyolNLP5 requires amino acid residues D127 and E129 within the conserved “GHRHDLE” motif. However, PyolNLP5-mediated plant disease resistance is irrelevant to its necrosis-inducing activity and the accumulation of reactive oxygen species (ROS). Furthermore, we reveal the positive role of non-cytotoxic PyolNLPs in enhancing plant resistance against Phytophthora pathogens and the fugal pathogen Sclerotinia sclerotiorum. Similarly, non-cytotoxic PyolNLPs also activate plant defense in a cell death-independent manner and induce defensin expression. The functions of non-cytotoxic PyolNLP13/14 rely on their conserved nlp24-like peptide pattern. Synthetic Pyolnlp24s derived from both cytotoxic and non-cytotoxic PyolNLPs can induce plant defensin expression. Unlike classic nlp24, Pyolnlp24s lack the ability of inducing ROS burst in plants with the presence of Arabidopsis nlp24 receptor RLP23. Taken together, our work demonstrates that PyolNLPs enhance plant resistance in an RLP23-independent manner, which requires the conserved nlp24-like peptide pattern but is uncoupled with ROS burst and cell death.
Associations of Fentanyl, Sufentanil, and Remifentanil With Length of Stay and Mortality Among Mechanically Ventilated Patients: A Registry-Based Cohort Study

Background: As the first-line treatment for mechanically ventilated patients with critical illness, fentanyl and its analogs (e.g., sufentanil and remifentanil) are commonly used in the intensive care unit (ICU). However, the pharmacokinetics, metabolism, and potency of these agents differed. Their effects on clinical outcomes have not been well-understood. Materials and Methods: Using a well-established registry, we conducted a cohort study. Patients who consistently underwent mechanical ventilation (MV) for more than 24 h were identified. We used a time-varying exposure definition, in which we coded each type of opioids as prescribed or not prescribed on each day from initiation of MV to extubation and ICU discharge. We used Fine-Gray competing risk models to compare the effects of fentanyl, sufentanil, and remifentanil on hazards for extubation, ventilator mortality, ICU discharge, and ICU mortality. All models were adjusted using a combination of fixed-time and time-varying covariates. Missing data were imputed using multiple imputation by chained equations. Results: A total of 8,165 patients were included. There were, respectively, 4,778, 4,008, and 2,233 patients receiving at least 1 day of fentanyl, sufentanil, and remifentanil dose. Compared to fentanyl, sufentanil was associated with shorter duration to extubation (hazard ratio 1.31, 95% CI, 1.20–1.41) and ICU discharge (hazard ratio 1.63, 95% CI, 1.38–1.92), and remifentanil was associated with shorter duration to extubation (hazard ratio 1.60, 95% CI, 1.40–1.84) and ICU discharge (hazard ratio 2.02, 95% CI, 1.43–2.84). No significant differences in time to extubation (Hazard ratio 1.14, 95% CI, 0.92–1.41) and ICU discharge (Hazard ratio 1.31, 95% CI, 0.81–2.14) were found between sufentanil and remifentanil. No differences were observed between any two of the agents regarding ventilator mortality or ICU mortality. The effects were similar in patients with versus without surgery. Conclusion: Sufentanil and remifentanil may be superior to fentanyl in shortening the time to extubation and ICU discharge. The effects on ventilator mortality and ICU mortality appeared similar across these agents, while further research is warranted.
Inhibition of Adult Hippocampal Neurogenesis Plays a Role in Sevoflurane-Induced Cognitive Impairment in Aged Mice Through Brain-Derived Neurotrophic Factor/Tyrosine Receptor Kinase B and Neurotrophin-3/Tropomyosin Receptor Kinase C Pathways

Sevoflurane anesthesia induces cognitive impairment, which may lead to perioperative neurocognitive disorders (PND). However, the factors and molecular mechanism underlying this impairment remains unclear. Adult hippocampal neurogenesis (AHN) in the subgranular zone of the hippocampus has been implicated in cognitive processes. Nonetheless, the direct role of AHN in sevoflurane-induced cognitive impairment has never been demonstrated. In this study, we explored the age and the concentration factors and the role of AHN inhibition in sevoflurane-induced cognitive impairment in sevoflurane inhalation model mice. We found that 3% sevoflurane exposure induced significant cognitive impairment and inhibition of AHN in aged mice but not adult mice. Expression of BDNF/TrkB and NT-3/TrkC was also decreased by 3% sevoflurane exposure in aged mice. Hippocampal brain-derived neurotrophic factor (BDNF) or Neurotrophin-3 (NT-3) microinjection could partially improve the sevoflurane-induced cognitive impairment and AHN inhibition, respectively. These results demonstrate that the cognitive impairment caused by sevoflurane inhalation is related to patient age and sevoflurane concentration. In conclusion, the molecular mechanism of cognitive impairment in the elderly is related to the inhibition of AHN through the BDNF/TrkB and NT-3/TrkC pathways. Thus, sevoflurane inhalation anesthesia may be safe for adult patients, but caution should be exercised when administering it to the elderly.
Proximity-Dependent Biotinylation Approaches to Explore the Dynamic Compartmentalized Proteome

In recent years, proximity-dependent biotinylation approaches, including BioID, APEX, and their derivatives, have been widely used to define the compositions of organelles and other structures in cultured cells and model organisms. The associations between specific proteins and given compartments are regulated by several post-translational modifications (PTMs); however, these effects have not been systematically investigated using proximity proteomics. Here, we discuss the progress made in this field and how proximity-dependent biotinylation strategies could elucidate the contributions of PTMs, such as phosphorylation, to the compartmentalization of proteins.
Elucidation of the Mechanisms and Effective Substances of Paeoniae Radix Rubra Against Toxic Heat and Blood Stasis Syndrome With a Stage-Oriented Strategy

In the clinical practice of traditional Chinese medicine, toxic heat and blood stasis syndrome (THBSS) is a common syndrome observed in various critical diseases. Paeoniae Radix Rubra (PRR) has known therapeutic effects on THBSS. However, its pharmacodynamic mechanisms and effective substances in the treatment of THBSS still need further elucidation. Our previous study indicated that THBSS had three stages of progression, and the abnormal biochemical indices of each stage were different. Therefore, this study aimed to elucidate the pharmacodynamic mechanisms and effective substances of PRR for the treatment of THBSS with a stage-oriented strategy. Specifically, research was performed separately in two stable stages of THBSS: the excessive heat and little blood stasis (EHLBS) and blood stasis (BS) stages. THBSS model rats, at different time periods after syndrome initiation (first 5 h for EHLBS and 24 h later for BS), were used to conduct the two-stage investigation. Targeted metabonomics analysis was performed to elucidate the pharmacodynamic mechanisms of PRR in the treatment of EHLBS or BS. Based on the relationship between the individual differences in blood drug concentrations and pharmacodynamic effects, partial least squares regression analysis was employed to screen for the effective substances from the original constituents and metabolites of PRR. We found that PRR could upregulate primary bile acid biosynthesis, glycerophospholipid metabolism, ether lipid metabolism, and five amino acid metabolic pathways (e.g., arginine and proline metabolism) to treat EHLBS. Meanwhile, PRR alleviated BS by upregulating primary bile acid biosynthesis and downregulating glycerophospholipid metabolism. But PRR had no obvious effects on ether lipid metabolism and amino acid metabolism in this stage. In total, 17 and 9 potential effective substances were found in the EHLBS and BS stages, respectively, among which there were only five common compounds between the two stages. To our knowledge, sixteen compounds were regarded as potential effective substances of PRR for the first time. Therefore, the pharmacodynamic mechanisms and effective substances of PRR in the treatment of EHLBS and BS were partly different. Overall, this stage-oriented strategy provides a new way to study the pharmacodynamic mechanisms and effective substances of traditional Chinese drugs.
Development and Validation of the Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients With Synovial Sarcoma

Background The study aimed to build and validate practical nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with synovial sarcoma (SyS). Methods A total of 893 eligible patients confirmed to have SyS between 2007 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training cohort (n = 448) and validation cohort (n = 445). Clinically independent prognostic and important factors were determined according to the Akaike information criterion in multivariate Cox regression models when developing the nomograms with the training cohort. The predictive accuracy of nomograms was bootstrapped validated internally and externally with the concordance index (C-index) and calibration curve. Decision curve analysis (DCA) was performed to compare the clinical usefulness between nomograms and American Joint Commission on Cancer (AJCC) staging system. Results Two nomograms shared common indicators including age, insurance status, tumor site, tumor size, SEER stage, surgery, and radiation, while marital status and tumor site were only included into the OS nomogram. The C-index of nomograms for predicting OS and CSS was 0.819 (0.873–0.764) and 0.821 (0.876–0.766), respectively, suggesting satisfactory predictive performance. Internal and external calibration curves exhibited optimal agreement between the nomogram prediction and the actual survival. Additionally, DCA demonstrated that our nomograms had obvious superiority over the AJCC staging system with more clinical net benefits. Conclusions Two nomograms predicting 3- and 5-year OS and CSS of SyS patients were successfully constructed and validated for the first time, with higher predictive accuracy and clinical values than the AJCC staging system regarding OS and CSS.
The Influence of Guanxi Between Boundary Spanners on Opportunistic Behaviors Based on the Theory of Reasoned Action Model

This study constructs a mechanism of the influence of Guanxi between boundary spanners on opportunistic behaviors in collaborative innovation projects based on the theory of reasoned action model. The study conducts a survey in the automobile industry in Changchun, Jilin Province, China, and analyzes the research data using the structural equation model. The findings show that Guanxi has a negative an significant influence on opportunistic behavior attitudes and subjective norms. , Guanxi has the greater influence on subjective norms than attitudes. Then, opportunistic behavior attitudes and subjective norms positively influence intentions. The influence of subjective norms is stronger. The attitudes and subjective norms of opportunistic behaviors also play mediation roles. Furthermore, opportunistic behavior intentions have a positive and significant influence on behaviors. In short, the study’s findings reveal a mechanism of Guanxi between boundary spanners influencing opportunistic behaviors of boundary spanners. It also provides a reference for corporate managers to govern opportunistic behaviors of collaborator while inhabiting opportunistic behaviors of their own boundary spanners.