GP-NAS-ensemble: a model for NAS Performance Prediction

It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is proposed to predict the performance of a neural network architecture with a small training dataset. We make several improvements on the GP-NAS model to make it share the advantage of ensemble learning methods. Our method ranks second in the CVPR2022 second lightweight NAS challenge performance prediction track

Financial Constraints, Cash Holdings and Investment Behaviours: An Empirical Investigation of US Manufacturing Industry

Financial constraints are important to firms’ cash holdings and investment activities. This article aims to estimate the relationships between financial constraints and the level of cash holdings and the relation between financial constraints and the degree of firms’ investment behaviours. Firms in US manufacturing industry are sampled during 2008 to 2016. Four models are conducted, which are the baseline model of cash flow sensitivity of cash, the robustness model of cash flow sensitivity of cash, the baseline model of investment cash flow sensitivity and the robustness model of investment cash flow sensitivity. Three criteria are used to differentiate firms, which are dividend pay-out ratio, firm size and KZ index. Using a series of tests, such as correlation test, White Test and Hausman Test, I conclude that constrained firms have more propensity on cash retention from cash flows and are more likely to reserve cash from the cash flow for the future investments. Additionally, the status of financial constraints can influence firms’ investment behaviours. Finally, the relationship between cash flows and investment is unsigned

Identifying Expressway Accident Black Spots Based on the Secondary Division of Road Units

For the purpose of reducing the harm of expressway traffic accidents and improving the accuracy of traffic accident black spots identification, this paper proposes a method for black spots identification of expressway accidents based on road unit secondary division and empirical Bayes method. Based on the modelling ideas of expressway accident prediction models in HSM (Highway Safety Manual), an expressway accident prediction model is established as a prior distribution and combined with empirical Bayes method safety estimation to obtain a Bayes posterior estimate. The posterior estimated value is substituted into the quality control method to obtain the black spots identification threshold. Finally, combining the Xi\u27an-Baoji expressway related data and using the method proposed in this paper, a case study of Xibao Expressway is carried out, and sections 9, 19, and 25 of Xibao Expressway are identified as black spots. The results show that the method of secondary segmentation based on dynamic clustering can objectively describe the concentration and dispersion of accident spots on the expressway, and the proposed black point recognition method based on empirical Bayes method can accurately identify accident black spots. The research results of this paper can provide a basis for decision-making of expressway management departments, take targeted safety improvement measures

Control of astrocyte progenitor specification, migration and maturation by Nkx6.1 homeodomain transcription factor.

Although astrocytes are the most abundant cell type in the central nervous system (CNS), little is known about their molecular specification and differentiation. It has previously been reported that transcription factor Nkx6.1 is expressed in neuroepithelial cells that give rise to astrocyte precursors in the ventral spinal cord. In the present study, we systematically investigated the function of Nkx6.1 in astrocyte development using both conventional and conditional Nkx6.1 mutant mice. At early postnatal stages, Nkx6.1 was expressed in a subpopulation of astrocytes in the ventral spinal cord. In the conventional Nkx6.1KO spinal cord, the initial specification of astrocyte progenitors was affected by the mutation, and subsequent migration and differentiation were disrupted in newborn mice. In addition, the development of VA2 subtype astrocytes was also inhibited in the white matter. Further studies with Nkx6.1 conditional mutants revealed significantly delayed differentiation and disorganized arrangement of fibrous astrocytes in the ventral white matter. Together, our studies indicate that Nkx6.1 plays a vital role in astrocyte specification and differentiation in the ventral spinal cord

An Efficient Universal Bee Colony Optimization Algorithm

The artificial bee colony algorithm is a global optimization algorithm. The artificial bee colony optimization algorithm is easy to fall into local optimal. We proposed an efficient universal bee colony optimization algorithm (EUBCOA). The algorithm adds the search factor u and the selection strategy of the onlooker bees based on local optimal solution. In order to realize the controllability of algorithm search ability, the search factor u is introduced to improve the global search range and local search range. In the early stage of the iteration, the search scope is expanded and the convergence rate is increased. In the latter part of the iteration, the algorithm uses the selection strategy to improve the algorithm accuracy and convergence rate. We select ten benchmark functions to testify the performance of the algorithm. Experimental results show that the EUBCOA algorithm effectively improves the convergence speed and convergence accuracy of the ABC algorithm

Recognition and Selection of Governance Modes of Private Listed Enterprises Based on BP Neural Network

Exploring the governance modes of private listed enterprises, this paper divides private listed enterprises into categories of small, medium and large. The governance modes of private listed enterprises can be divided into 9 categories according to two different management intensities of equity capital governance and manpower capital governance. The 9 categories of governance modes of private listed enterprises are identified by using the BP neural network mode. This paper analyses the evolution of private listed companies’ governance modes at different scales and analyses various governance modes. Therefore, small, medium and large private listed companies choose the “strong equity and weak manpower”, “moderate equity and moderate manpower” and “weak equity and moderate manpower” governance modes, respectively. This paper comparatively analyses the governance efficiency of governance modes at different scales. The results show that “moderate equity and moderate manpower” is the most effective management mode for all three scales

POD-based reduced-order modeling study for thermal analysis of gas-cooled microreactor core

Small modular reactors require multi-physics coupling calculations to balance economy and stability, due to their compact structures. Traditional tools used for light water reactors are not effective in addressing the several modeling challenges posed by these calculations. The lumped parameter method is commonly used in the thermal analysis for its high computational speed, but it lacks accuracy due to the thermal model is one-dimensional. While computational fluid dynamics software (CFD) can provide high-precision and high-resolution thermal analysis, its low calculation efficiency making it challenging to be coupled with other programs. Proper Orthogonal Decomposition (POD) is one of the Reduced Order Model (ROM) methods employed in this study to reduce the dimensionality of sample data and to improve the thermal modelling of gas-cooled microreactors. In this work, a non-inclusive POD with neural network method is proposed and verified using a transient heat conduction model for a two-dimensional plate. The method is then applied to build a reduced order model of the gas-cooled micro-reactor core for rapid thermal analysis. The results show that the root mean square error of the reactor core temperature is less than 1.02% and the absolute error is less than 8.2°C while the computational cost is reduced by several orders of magnitude, shortening the calculation time from 1.5-hour to real-time display. These findings proved the feasibility of using POD and neural network in the development of ROMs for gas-cooled microreactor, providing a novel approach for achieving precise thermal calculation with minimized computational costs