192 research outputs found
MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention
With the development of computational fluid dynamics, the requirements for
the fluid simulation accuracy in industrial applications have also increased.
The quality of the generated mesh directly affects the simulation accuracy.
However, previous mesh quality metrics and models cannot evaluate meshes
comprehensively and objectively. To this end, we propose MQENet, a structured
mesh quality evaluation neural network based on dynamic graph attention. MQENet
treats the mesh evaluation task as a graph classification task for classifying
the quality of the input structured mesh. To make graphs generated from
structured meshes more informative, MQENet introduces two novel structured mesh
preprocessing algorithms. These two algorithms can also improve the conversion
efficiency of structured mesh data. Experimental results on the benchmark
structured mesh dataset NACA-Market show the effectiveness of MQENet in the
mesh quality evaluation task
Glottal source parametrisation by multi-estimate fusion
Glottal source information has been proven useful in many applications such as speech synthesis, speaker characterisation, voice transformation and pathological speech diagnosis. However, currently no single algorithm can extract reliable glottal source estimates across a
wide range of speech signals. This thesis describes an investigation into glottal source parametrisation, including studies, proposals and evaluations on glottal waveform extraction, glottal source modelling by Liljencrants-Fant (LF) model fitting and a new multi-estimate fusion framework.
As one of the critical steps in voice source parametrisation, glottal waveform extraction techniques are reviewed. A performance study is carried out on three existing glottal inverse filtering approaches and results confirm that no single algorithm consistently outperforms
others and provide a reliable and accurate estimate for different speech signals.
The next step is modelling the extracted glottal flow. To more accurately estimate the glottal source parameters, a new time-domain LF-model fitting algorithm by extended Kalman filter is proposed.
The algorithm is evaluated by comparing it with a standard time-domain method and a spectral approach. Results show the proposed fitting method is superior to existing fitting methods.
To obtain accurate glottal source estimates for different speech signals, a multi-estimate (ME) fusion framework is proposed. In the framework different algorithms are applied in parallel to extract multiple sets of LF-model estimates which are then combined by quantitative data fusion. The ME fusion approach is implemented and tested in several ways.
The novel fusion framework is shown to be able to give more reliable glottal LF-model estimates than any single algorithm
Model Predictive Robustness of Signal Temporal Logic Predicates
The robustness of signal temporal logic not only assesses whether a signal
adheres to a specification but also provides a measure of how much a formula is
fulfilled or violated. The calculation of robustness is based on evaluating the
robustness of underlying predicates. However, the robustness of predicates is
usually defined in a model-free way, i.e., without including the system
dynamics. Moreover, it is often nontrivial to define the robustness of
complicated predicates precisely. To address these issues, we propose a notion
of model predictive robustness, which provides a more systematic way of
evaluating robustness compared to previous approaches by considering
model-based predictions. In particular, we use Gaussian process regression to
learn the robustness based on precomputed predictions so that robustness values
can be efficiently computed online. We evaluate our approach for the use case
of autonomous driving with predicates used in formalized traffic rules on a
recorded dataset, which highlights the advantage of our approach compared to
traditional approaches in terms of expressiveness. By incorporating our
robustness definitions into a trajectory planner, autonomous vehicles obey
traffic rules more robustly than human drivers in the dataset.Comment: 7 pages, 6 figures, conference paper in submissio
Public Leadership in the Chinese Government's Response to COVID-19: The synergy of governance, public participation and cultural foundations
Abstract
The country's sudden and abnormal public crisis is a test of the government's leadership (Wu, 2013). These crises include wars, natural disasters, pandemics, etc. As of January 18, 2022, COVID-19, which lasted about two years and two months, has caused 330 million infections and 5.5 million deaths globally (WHO, 2022). When a crisis occurs, governments of various countries are faced with formulating a thorough response plan within a short time and then passing the review of relevant departments, communicating with the public and responding to questions or accusations from the media. This dissertation adopts the qualitative case study method to explore China's performance and response to COVID-19 from the perspective of national culture and public leadership. This research takes the performance of the government and people in Wuhan from the time of lockdown to the lifting of restrictions as a case. It profoundly analyzes China's national culture, the institutional model and measures adopted by the Wuhan government and the Chinese central government in response to COVID-19. The author analyzes the factor of national culture mainly from the two aspects of power distance and collectivism. In order to explain the mechanism of action of the two elements of national culture and government leadership, this study added the public factor. In this way, it explains how the government can influence the public in both rigid and soft patterns. These three factors together constitute a model framework for the effective functioning of government leadership. On this basis, this research explains why the Chinese government can effectively control the spread of the epidemic in a short time.
Key words: Covid-19, public leadership, government, national culture, power distance, individualism – collectivism, institution, autocratic system publi
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
Along with the proliferation of electric vehicles (EVs), optimizing the use
of EV charging space can significantly alleviate the growing load on
intelligent transportation systems. As the foundation to achieve such an
optimization, a spatiotemporal method for EV charging demand prediction in
urban areas is required. Although several solutions have been proposed by using
data-driven deep learning methods, it can be found that these
performance-oriented methods may suffer from misinterpretations to correctly
handle the reverse relationship between charging demands and prices. To tackle
the emerging challenges of training an accurate and interpretable prediction
model, this paper proposes a novel approach that enables the integration of
graph and temporal attention mechanisms for feature extraction and the usage of
physic-informed meta-learning in the model pre-training step for knowledge
transfer. Evaluation results on a dataset of 18,013 EV charging piles in
Shenzhen, China, show that the proposed approach, named PAG, can achieve
state-of-the-art forecasting performance and the ability in understanding the
adaptive changes in charging demands caused by price fluctuations.Comment: Preprint. This work has been submitted to the IEEE Transactions on
ITS for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
Recommender systems are seen as an effective tool to address information
overload, but it is widely known that the presence of various biases makes
direct training on large-scale observational data result in sub-optimal
prediction performance. In contrast, unbiased ratings obtained from randomized
controlled trials or A/B tests are considered to be the golden standard, but
are costly and small in scale in reality. To exploit both types of data, recent
works proposed to use unbiased ratings to correct the parameters of the
propensity or imputation models trained on the biased dataset. However, the
existing methods fail to obtain accurate predictions in the presence of
unobserved confounding or model misspecification. In this paper, we propose a
theoretically guaranteed model-agnostic balancing approach that can be applied
to any existing debiasing method with the aim of combating unobserved
confounding and model misspecification. The proposed approach makes full use of
unbiased data by alternatively correcting model parameters learned with biased
data, and adaptively learning balance coefficients of biased samples for
further debiasing. Extensive real-world experiments are conducted along with
the deployment of our proposal on four representative debiasing methods to
demonstrate the effectiveness.Comment: Accepted Paper in WWW'2
Recent Advances of Continual Learning in Computer Vision: An Overview
In contrast to batch learning where all training data is available at once,
continual learning represents a family of methods that accumulate knowledge and
learn continuously with data available in sequential order. Similar to the
human learning process with the ability of learning, fusing, and accumulating
new knowledge coming at different time steps, continual learning is considered
to have high practical significance. Hence, continual learning has been studied
in various artificial intelligence tasks. In this paper, we present a
comprehensive review of the recent progress of continual learning in computer
vision. In particular, the works are grouped by their representative
techniques, including regularization, knowledge distillation, memory,
generative replay, parameter isolation, and a combination of the above
techniques. For each category of these techniques, both its characteristics and
applications in computer vision are presented. At the end of this overview,
several subareas, where continuous knowledge accumulation is potentially
helpful while continual learning has not been well studied, are discussed
Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series
Learning directed acyclic graphs (DAGs) to identify causal relations
underlying observational data is crucial but also poses significant challenges.
Recently, topology-based methods have emerged as a two-step approach to
discovering DAGs by first learning the topological ordering of variables and
then eliminating redundant edges, while ensuring that the graph remains
acyclic. However, one limitation is that these methods would generate numerous
spurious edges that require subsequent pruning. To overcome this limitation, in
this paper, we propose an improvement to topology-based methods by introducing
limited time series data, consisting of only two cross-sectional records that
need not be adjacent in time and are subject to flexible timing. By
incorporating conditional instrumental variables as exogenous interventions, we
aim to identify descendant nodes for each variable. Following this line, we
propose a hierarchical topological ordering algorithm with conditional
independence test (HT-CIT), which enables the efficient learning of sparse DAGs
with a smaller search space compared to other popular approaches. The HT-CIT
algorithm greatly reduces the number of edges that need to be pruned. Empirical
results from synthetic and real-world datasets demonstrate the superiority of
the proposed HT-CIT algorithm
Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors
Data-driven cooperative control of connected and automated vehicles (CAVs)
has gained extensive research interest as it can utilize collected data to
generate control actions without relying on parametric system models that are
generally challenging to obtain. Existing methods mainly focused on improving
traffic safety and stability, while less emphasis has been placed on energy
efficiency in the presence of uncertainties and diversities of human-driven
vehicles (HDVs). In this paper, we employ a data-enabled predictive control
(DeePC) scheme to address the eco-driving of mixed traffic flows with diverse
behaviors of human drivers. Specifically, by incorporating the physical
relationship of the studied system and the Hankel matrix update from the
generalized behavior representation to a particular one, we develop a new
Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle
human driver diversities. In particular, a power consumption term is added to
the DeePC cost function to reduce the holistic energy consumption of both CAVs
and HDVs. Simulation results demonstrate the effectiveness of our approach in
accurately capturing random human driver behaviors and addressing the complex
dynamics of mixed traffic flows, while ensuring driving safety and traffic
efficiency. Furthermore, the proposed optimization framework achieves
substantial reductions in energy consumption, i.e., average reductions of 4.83%
and 9.16% when compared to the benchmark algorithms
Heatmap Distribution Matching for Human Pose Estimation
For tackling the task of 2D human pose estimation, the great majority of the
recent methods regard this task as a heatmap estimation problem, and optimize
the heatmap prediction using the Gaussian-smoothed heatmap as the optimization
objective and using the pixel-wise loss (e.g. MSE) as the loss function. In
this paper, we show that optimizing the heatmap prediction in such a way, the
model performance of body joint localization, which is the intrinsic objective
of this task, may not be consistently improved during the optimization process
of the heatmap prediction. To address this problem, from a novel perspective,
we propose to formulate the optimization of the heatmap prediction as a
distribution matching problem between the predicted heatmap and the dot
annotation of the body joint directly. By doing so, our proposed method does
not need to construct the Gaussian-smoothed heatmap and can achieve a more
consistent model performance improvement during the optimization of the heatmap
prediction. We show the effectiveness of our proposed method through extensive
experiments on the COCO dataset and the MPII dataset.Comment: NeurIPS 202
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