On the Approximation and Complexity of Deep Neural Networks to Invariant Functions

Recent years have witnessed a hot wave of deep neural networks in various domains; however, it is not yet well understood theoretically. A theoretical characterization of deep neural networks should point out their approximation ability and complexity, i.e., showing which architecture and size are sufficient to handle the concerned tasks. This work takes one step on this direction by theoretically studying the approximation and complexity of deep neural networks to invariant functions. We first prove that the invariant functions can be universally approximated by deep neural networks. Then we show that a broad range of invariant functions can be asymptotically approximated by various types of neural network models that includes the complex-valued neural networks, convolutional neural networks, and Bayesian neural networks using a polynomial number of parameters or optimization iterations. We also provide a feasible application that connects the parameter estimation and forecasting of high-resolution signals with our theoretical conclusions. The empirical results obtained on simulation experiments demonstrate the effectiveness of our method

A Method for Filling up the Missed Data in Information Table

Almost algorithms based on the rough sets, such as mean value method, maximum frequency method, mode method are week in supporting the hidden rules in the information tablel. By the breaking point sets in the information system, a new method for packing the missed attribute value is provided in the paper,. The method is more efficient for indicating the decision rules

Towards Understanding Theoretical Advantages of Complex-Reaction Networks

Complex-valued neural networks have attracted increasing attention in recent years, while it remains open on the advantages of complex-valued neural networks in comparison with real-valued networks. This work takes one step on this direction by introducing the \emph{complex-reaction network} with fully-connected feed-forward architecture. We prove the universal approximation property for complex-reaction networks, and show that a class of radial functions can be approximated by a complex-reaction network using the polynomial number of parameters, whereas real-valued networks need at least exponential parameters to reach the same approximation level. For empirical risk minimization, our theoretical result shows that the critical point set of complex-reaction networks is a proper subset of that of real-valued networks, which may show some insights on finding the optimal solutions more easily for complex-reaction networks

Mining User\u27s Preference Information through System Log toward a Personalized ERP System

This paper discusses how to mine the user\u27s preference information through the system log of the ERP system. Our findings support that the user\u27s preference degree is positively correlated with the usage frequency and negatively correlated with the latest waiting time. With a derived function of user\u27s preference degree, it can be applied to guide the automatic readjustments to the system interface and content accordingly. Taking a contract management subsystem as an example, the paper shows how to mine the system log toward a intelligent personalized ERP system

Constraint Programming Approach to Steelmaking-making Process Scheduling

This paper presents a constraint programming (CP) approach to optimal steelmaking process scheduling with constraints of processing time, limited waiting time between adjacent stages, serial batching, sequence independent setup time, release/due time, and with the objective of minimizing maximal total waiting time between adjacent charges in the same casts. The model and search strategies are proposed. Numerical experiments with the steel making process show that CP approach, under appropriate formulation and search strategies, can not only describe the problem exactly, but also can solve the problem more effectively and efficiently compared with classical exact algorithms and heuristic rule

Evolution and focal features of China’s agricultural green development policies: Text analysis based on attention perspective

[Objective] Green development is an important direction for the structural reform and transformation and upgrading of modern agriculture. Exploring the evolution of government attention to policies on agricultural green development can provide theoretical references for future policy formulation and implementation. [Methods] From the perspective of attention, combined with the theoretical model of punctuated equilibrium, this study applied text mining methods such as high-frequency word identification, co-occurrence word analysis, and keyword clustering through Python to analyze 1535 policy documents related to agricultural green development from 1984 to 2022. [Results] The study found that: (1) From the historical evolution of attention allocation, it can be seen that China’s agricultural green development policies have experienced three stages: exploration, construction, and consolidation and deepening. There were differences in the focus of government attention to agricultural green development policies in different periods; leading enterprises were important subjects for promoting agricultural transformation, upgrading, and green development. (2) From the perspective of focus configuration, China’s agricultural green development began with a top-down system engineering aimed at protecting the ecological environment. Technological innovation ran through the entire process of government attention to agricultural green development, and was the fundamental driving force for agricultural transformation, upgrading, and green development. [Conclusion] With the proposal of the rural revitalization strategy, the economic benefits in the process of agricultural green development are becoming increasingly prominent, and local governments should actively explore new paths and new methods of the integration of agricultural production and agricultural tourism under the premise of meeting the needs of ecological civilization construction, and take leading enterprises as the traction to help the industrialized operation of households in rural areas, and boost the green transformation and upgrading of China’s agriculture and high-quality development

Epigenetic regulation of CD271, a potential cancer stem cell marker associated with chemoresistance and metastatic capacity.

Cancer stem cells (CSCs) are considered to be the cause of tumor initiation, metastasis and recurrence. Additionally, CSCs are responsible for the failure of chemotherapy and radiotherapy. The isolation and identification of CSCs is crucial for facilitating the monitoring, therapy or prevention of cancer. We aimed to identify esophageal squamous cell carcinoma (ESCC) stem-like cells, the epigenetic mechanism and identify novel biomarkers for targeting ESCC CSCs. Sixty-three paired ESCC tissues and adjacent non-cancerous tissues were included in this study. CD271, which was identified as the CSC marker for melanoma, was assessed using quantitative PCR (qPCR). Using flow cytometry, we isolated CD271+ cells comprising 7.5% of cancer cells from the KYSE70 cell line. Sphere formation and anchorage-independent growth were analyzed in CD271+ and CD271- cancer cells, respectively. qPCR was used to detect stem-related genes and CCK-8 was performed to analyze the sensitivity to chemotherapy in the two groups. Bisulfite genomic sequencing was used to analyze the methylation status. CD271 expression was significantly higher in ESCC tissues than in adjacent non-cancerous tissues. Compared with CD271- cancer cells, CD271+ cancer cells showed a higher ability of sphere and colony formation, a high level expression of stem-related gene, and resistance to chemotherapy. The expression of CD271 was induced by a demethylation agent. In conclusion, CD271+ ESCC cells possess stem-like properties. CD271 can potentially act as a prognostic marker for ESCC, whose expression is regulated epigenetically