511 research outputs found
Optimal Design of Bus Stop Locations Integrating Continuum Approximation and Discrete Models
Although transit stop location problem has been extensively studied, the two main categories of modeling methodologies, i.e., discrete models and continuum approximation (CA) ones, seem have little intersection. Both have strengths and weaknesses, respectively. This study intends to integrate them by taking the advantage of CA models’ parsimonious property and discrete models’ fine consideration of practical conditions. In doing so, we first employ the state-of-the-art CA models to yield the optimal design, which serves as the input to the next discrete model. Then, the stop location problem is formulated into a multivariable nonlinear minimization problem with a given number of stop location variables and location constraint. The interior-point algorithm is presented to find the optimal design that is ready for implementation. In numerical studies, the proposed model is applied to a variety of scenarios with respect to demand levels, spatial heterogeneity, and route length. The results demonstrate the consistent advantage of the proposed model in all scenarios as against its counterparts, i.e., two existing recipes that convert CA model-based solution into real design of stop locations. Lastly, a case study is presented using real data and practical constraints for the adjustment of a bus route in Chengdu (China). System cost saving of 15.79% is observed by before-and-after comparison.
Document type: Articl
An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing
The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks
Fucoxanthin attenuates LPS-induced acute lung injury via inhibition of the TLR4/MYD88 signaling axis
Acute lung injury (ALI) is a critical clinical condition with a high mortality rate. It is believed that the inflammatory storm is a critical contributor to the occurrence of ALI. Fucoxanthin is a natural extract from marine seaweed with remarkable biological properties, including antioxidant, anti-tumor, and anti-obesity. However, the anti-inflammatory activity of Fucoxanthin has not been extensively studied. The current study aimed to elucidate the effects and the molecular mechanism of Fucoxanthin on lipopolysaccharide-induced acute lung injury. In this study, Fucoxanthin efficiently reduced the mRNA expression of pro-inflammatory factors, including IL-10, IL-6, iNOS, and Cox-2, and down-regulated the NF-kappaB signaling pathway in Raw264.7 macrophages. Furthermore, based on the network pharmacological analysis, our results showed that anti-inflammation signaling pathways were screened as fundamental action mechanisms of Fucoxanthin on ALI. Fucoxanthin also significantly ameliorated the inflammatory responses in LPS-induced ALI mice. Interestingly, our results revealed that Fucoxanthin prevented the expression of TLR4/MyD88 in Raw264.7 macrophages. We further validated Fucoxanthin binds to the TLR4 pocket using molecular docking simulations. Altogether, these results suggest that Fucoxanthin suppresses the TLR4/MyD88 signaling axis by targeting TLR4, which inhibits LPS-induced ALI, and fucoxanthin inhibition may provide a novel strategy for controlling the initiation and progression of ALI
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Incomplete multi-view clustering is a hot and emerging topic. It is well
known that unavoidable data incompleteness greatly weakens the effective
information of multi-view data. To date, existing incomplete multi-view
clustering methods usually bypass unavailable views according to prior missing
information, which is considered as a second-best scheme based on evasion.
Other methods that attempt to recover missing information are mostly applicable
to specific two-view datasets. To handle these problems, in this paper, we
propose an information recovery-driven deep incomplete multi-view clustering
network, termed as RecFormer. Concretely, a two-stage autoencoder network with
the self-attention structure is built to synchronously extract high-level
semantic representations of multiple views and recover the missing data.
Besides, we develop a recurrent graph reconstruction mechanism that cleverly
leverages the restored views to promote the representation learning and the
further data reconstruction. Visualization of recovery results are given and
sufficient experimental results confirm that our RecFormer has obvious
advantages over other top methods.Comment: Accepted by TNNLS 2023. Please contact me if you have any questions:
[email protected]. The code is available at:
https://github.com/justsmart/RecForme
Factor Analysis Model Based on the Theory of the TOPSIS in the Application Research
In view of the existing literature panel data factor analysis model in practical application of the deficiency, this paper established the model of factor analysis based on TOPSIS method, which is applied to the analysis of the panel data factor in practice. Compared with the generalized dynamic factor analysis model, the model does not need to satisfy the 4 assumptions of the generalized dynamic factor analysis model at the same time. The model is calculated with regard to every year's cross section data factor composite scores the highest and lowest, respectively, for the best and worst vector. By TOPSIS theory, the optimal factor scheme approach degree of each research object is obtained. Take the development of China's service industry as an example; use the optimal factor scheme proximity of model degree to depict the eastern, central, and western development of service industry. The study found that the development of service industry in eastern provinces and in central and western regions differs greatly. In total, China's service industry has a great development space
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