Human factors quantification via boundary identification of flight performance margin

AbstractA systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled: information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight performance can be investigated through Monte Carlo (MC) simulation. Kernel density estimation algorithm is selected to find and rank the influential human factors. Subsequently, human factors are quantified through identifying the boundary of the flight performance margin by the k-nearest neighbor (k-NN) classifier. Simulation-based analysis shows that flight performance can be dramatically improved with the quantitative human factors

UnifiedGesture: A Unified Gesture Synthesis Model for Multiple Skeletons

The automatic co-speech gesture generation draws much attention in computer animation. Previous works designed network structures on individual datasets, which resulted in a lack of data volume and generalizability across different motion capture standards. In addition, it is a challenging task due to the weak correlation between speech and gestures. To address these problems, we present UnifiedGesture, a novel diffusion model-based speech-driven gesture synthesis approach, trained on multiple gesture datasets with different skeletons. Specifically, we first present a retargeting network to learn latent homeomorphic graphs for different motion capture standards, unifying the representations of various gestures while extending the dataset. We then capture the correlation between speech and gestures based on a diffusion model architecture using cross-local attention and self-attention to generate better speech-matched and realistic gestures. To further align speech and gesture and increase diversity, we incorporate reinforcement learning on the discrete gesture units with a learned reward function. Extensive experiments show that UnifiedGesture outperforms recent approaches on speech-driven gesture generation in terms of CCA, FGD, and human-likeness. All code, pre-trained models, databases, and demos are available to the public at https://github.com/YoungSeng/UnifiedGesture.Comment: 16 pages, 11 figures, ACM MM 202

Neurotrophic Signaling Factors in Brain Ischemia/Reperfusion Rats: Differential Modulation Pattern between Single-Time and Multiple Electroacupuncture Stimulation

Electroacupuncture (EA) treatment has been widely used for stroke-like disorders in traditional Chinese medicine. However, the underlying mechanisms remain unclear. Our previous studies showed that single-time EA stimulation at “Baihui” (GV 20) and “Shuigou” (GV 26) after the onset of ischemia can protect the brain against ischemic injury in rats with middle cerebral artery occlusion (MCAO). Here, we further investigated the differential effects between multiple EA and single-time EA stimulation on ischemic injury. In the present study, we found that both single-time EA and multiple EA stimulation significantly reduced MCAO-induced ischemic infarction, while only multiple EA attenuated sensorimotor dysfunctions. Also, with PCR array screening and ingenuity gene analysis, we revealed that multiple EA and single-time EA stimulation could differentially induce expression changes in neurotrophic signaling related genes. Meanwhile, with western blotting, we demonstrated that the level of glia maturation factor β (GMFβ) increased in the early stage (day 1) of reperfusion, and this upregulation was suppressed only by single-time EA stimulation. These findings suggest that the short-term effect of single-time EA stimulation differs from the cumulative effect of multiple EA, which possibly depends on their differential modulation on neurotrophic signaling molecules expression

Enhancing robustness of air transportation network

The design of robust air transportation networks is crucial for improving a network’s ability to sustain/withstand failures and attacks. The difficulty lies in quantifying and optimizing the robustness. A comparison experiment shows that the total effective resistance, a spectral measure, is a promising measure satisfying the criteria that are intuitively predefined. Designing suitable strategies are the key challenges of this dissertation. Two strategies that are explicitly formulated for the minimization of the total effective resistance under several constraints are proposed to tackle the challenges. First considered is the flight route selection problem, in which a set of routes is chosen from a set of candidate routes to minimize the total effective resistance of the network. The corresponding problem is a nonlinear combinatorial optimization problem. In order to enhance system performance and computational efficiency, two approaches are proposed to deal with the problem based on small, medium and large-scale networks in terms of airport number. For small/medium-scale networks, an efficient method is developed by using convex relaxation with the step-by-step rounding technique. For large-scale networks, a submodular greedy algorithm is proposed to allow the system to handle a network size far beyond the capabilities of the convex relaxation method and to guarantee a bounded optimality gap, based on the monotone submodular characteristic of the objective function. Numerical experiments are performed on some real air transportation networks of small, medium and large-scale. A more general problem of creating robust network design under budget constraint, the route budget allocation problem, is also investigated. In this problem, both the edges to be added and their edge weights are allowed to be determined. The problem is solved exactly by a brute-force method to demonstrate its difficulty in obtaining an optimal solution. In order to achieve better computational efficiency, a convex relaxation method is proposed for the medium-scale networks, making use of the problem properties. For large-scale networks, a clustering-based convex relaxation method is proposed, in which the network dimensions can be reduced via the selection of critical airports. Three metrics, namely hub connectivity, number of flights, and passenger volume, are combined to define the hub hierarchy, and then the Gaussian mixture model is chosen to cluster the airports hierarchically. These two problems and their associated algorithms are tested on three levels of air transportation networks of small, medium, and large-scale. The outcomes indicate the tradeoff between the performance and computation time of these algorithms. Accordingly, the decision makers in different levels of organization could select the suitable algorithms.Doctor of Philosophy (MAE

Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension

In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC). However, limited by the structure of BERT-based MRC models (for example, restrictions on word count), such models cannot effectively integrate significant features, such as syntax relations, semantic connections, and long-distance semantics between sentences, leading to the inability of the available models to better understand the intrinsic connections between text and questions to be answered based on it. In this paper, a multi-granularity syntax guidance (MgSG) module that consists of a “graph with dependence” module and a “graph with entity” module is proposed. MgSG selects both sentence and word granularities to guide the text model to decipher the text. In particular, syntactic constraints are used to guide the text model while exploiting the global nature of graph neural networks to enhance the model’s ability to construct long-range semantics. Simultaneously, named entities play an important role in text and answers and focusing on entities can improve the model’s understanding of the text’s major idea. Ultimately, fusing multiple embedding representations to form a representation yields the semantics of the context and the questions. Experiments demonstrate that the performance of the proposed method on the Stanford Question Answering Dataset is better when compared with the traditional BERT baseline model. The experimental results illustrate that our proposed “MgSG” module effectively utilizes the graph structure to learn the internal features of sentences, solve the problem of long-distance semantics, while effectively improving the performance of PrLM in machine reading comprehension

Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension

In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC). However, limited by the structure of BERT-based MRC models (for example, restrictions on word count), such models cannot effectively integrate significant features, such as syntax relations, semantic connections, and long-distance semantics between sentences, leading to the inability of the available models to better understand the intrinsic connections between text and questions to be answered based on it. In this paper, a multi-granularity syntax guidance (MgSG) module that consists of a “graph with dependence” module and a “graph with entity” module is proposed. MgSG selects both sentence and word granularities to guide the text model to decipher the text. In particular, syntactic constraints are used to guide the text model while exploiting the global nature of graph neural networks to enhance the model’s ability to construct long-range semantics. Simultaneously, named entities play an important role in text and answers and focusing on entities can improve the model’s understanding of the text’s major idea. Ultimately, fusing multiple embedding representations to form a representation yields the semantics of the context and the questions. Experiments demonstrate that the performance of the proposed method on the Stanford Question Answering Dataset is better when compared with the traditional BERT baseline model. The experimental results illustrate that our proposed “MgSG” module effectively utilizes the graph structure to learn the internal features of sentences, solve the problem of long-distance semantics, while effectively improving the performance of PrLM in machine reading comprehension