The revealed preference theory of stable matchings with one-sided preferences

Published in Games and Economic Behavior, 2020. DOI: 10.1016/j.geb.2020.08.015</p

Learning Spiking Neural Network from Easy to Hard task

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL

中小学校园公共空间自然可视率量化研究

In order to solve the problem that the traditional method is very complex in safety assessment of primary and secondary school, which cannot get an objective result quickly. The method of calculating the natural visibility ratio within the students' effective range of visibility is studied based on graph theory, which can assess the safety level of public space in a quantitative way. Through this method, 42 primary and secondary schools were used to investigate to get the value of natural visibility ratio, and which was used to analyse the natural visibility level of those schools. The results show that natural visibility ratio can quality identify potential unsafe factors in campus. There is a strong negative correlation between campus average natural visibility ratio and building density. Therefore, the natural visibility ratio is beneficial to quality and objectively evaluate the safety of the campus, and to improve campus planning and design by predicting the level of visibility in public space

Preparation Method of Co 3

Co3O4 nanoparticles were fabricated by a novel, facile, and environment-friendly carbon-assisted method using degreasing cotton. Structural and morphological characterizations were performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The component of the sample obtained at different temperatures was measured by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Nitrogen adsorption and desorption isotherms were utilized to reveal the specific surface areas. The formation mechanism of Co3O4 nanoparticles was also proposed, demonstrating that the additive degreasing cotton played an indispensable role in the process of synthesizing the sample. The resultant Co3O4 sample calcined at 600°C exhibited superior electrochemical performance with better specific capacitance and long-term cycling life, due to its high specific surface areas and pores structures. Additionally, it has been proved that this facile synthetic strategy can be extended to produce other metal oxide materials (e.g., Fe3O4). As a consequence, the carbon-assisted method using degreasing cotton accompanied a promising prospect for practical application