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## 1. chinaXiv:202105.00070 [pdf]

Subjects: Statistics >> Mathematical Statistics
Subjects: Computer Science >> Computer Application Technology
Subjects: Information Science and Systems Science >> Basic Disciplines of Information Science and Systems Science

 统计独立性是统计学和机器学习领域的基础性概念，如何表示和度量统计独立性是该领域的基本问题。Copula理论提供了统计相关性表示的理论工具，而Copula熵理论则给出了度量统计独立性的概念工具。本文综述了Copula熵的理论和应用，概述了其基本概念和定理，以及估计方法。介绍了Copula熵研究的最新进展，包括其在统计学四个基本问题（结构学习、关联发现、变量选择和时序因果发现等）上的理论应用。讨论了四个理论应用之间的关系，以及其对应的深层次的相关性和因果性概念之间的联系，并将Copula熵的（条件）独立性度量框架与基于核函数和距离的相关性度量框架进行了对比。简述了Copula熵在水文学、环境气象学、生态学、认知神经学、系统生物学、老年医学、公共卫生学，以及能源工程、制造工程和可靠性工程等领域的实际应用。

## 2. chinaXiv:202108.00103 [pdf]

Subjects: Computer Science >> Other Disciplines of Computer Science

 Starting from finding approximate value of a function, introduces the measure of approximation-degree between two numerical values, proposes the concepts of "strict approximation" and "strict approximation region", then, derives the corresponding one-dimensional interpolation methods and formulas, and then presents a calculation model called "sum-times-difference formula" for high-dimensional interpolation, thus develops a new interpolation approach ? ADB interpolation. ADB interpolation is applied to the interpolation of actual functions with satisfactory results. Viewed from principle and effect, the interpolation approach is of novel idea, and has the advantages of simple calculation, stable accuracy, facilitating parallel processing, very suiting for high-dimensional interpolation, and easy to be extended to the interpolation of vector valued functions. Applying the approach to instance-based learning, a new instance-based learning method ? learning using ADB interpolation ? is obtained. The learning method is of unique technique, which has also the advantages of definite mathematical basis, implicit distance weights, avoiding misclassification, high efficiency, and wide range of applications, as well as being interpretable, etc. In principle, this method is a kind of learning by analogy, which and the deep learning that belongs to inductive learning can complement each other, and for some problems, the two can even have an effect of “different approaches but equal results” in big data and cloud computing environment. Thus, the learning using ADB interpolation can also be regarded as a kind of “wide learning” that is dual to deep learning.

## 3. chinaXiv:202108.00061 [pdf]

Subjects: Computer Science >> Other Disciplines of Computer Science

 负荷预测是电网系统中很多应用的关键部分，具有重要作用。然而，由于电网负荷的非线性、时变性和不确定性，使得准确预测负荷具有一定的挑战。充分挖掘负荷序列的潜在特征是提升预测准确率的关键。本文认为在特征提取时应该充分利用负荷序列的位置信息、趋势性、周期性和时间信息，同时还应构建更深层次的神经网络框架进行特征挖掘。因此，本文提出了基于特征嵌入和Transformer框架的负荷预测模型，该模型由特征嵌入层，Transformer层和预测层组成。在特征嵌入层，模型首先对历史负荷的位置信息、趋势性、周期性和时间信息进行特征嵌入，然后再与天气信息进行融合，得到特征向量。Transformer层则接受历史序列的特征向量并挖掘序列的非线性时序依赖关系。预测层通过全连接网络实现负荷预测。从实验结果来看，本文模型的预测性能优于对比模型，体现了该模型的可行性和有效性。

## 4. chinaXiv:202107.00010 [pdf]

Subjects: Psychology >> Applied Psychology

 [目的]本研究利用DOTA2游戏行为数据，实现对DOTA2玩家黑暗人格三维度的无侵入识别。[方法]本文利用Clarity 2解析包对DOTA2的游戏日志文件进行解析，提取玩家的游戏行为特征，并利用黑暗十二条量表对玩家的行为特征进行标注，采用机器学习的方法实现对黑暗人格三维度的识别。 [结果]结果发现，在马基雅维利主义、自恋和精神病态三维度上，采用高斯过程回归算法建立的模型在效度和信度上表现最佳，模型预测值与真实值之间的相关系数在0.31-0.45之间，重测信度在0.33-0.53之间。 [局限]本研究未将被试的言语行为特征纳入到建模过程中，使得游戏行为特征不够全面。 [结论]研究结果发现游戏行为数据能够帮助预测个体的黑暗人格，并且通过高斯过程回归建立的模型具有最高信效度。

## 5. chinaXiv:202106.00114 [pdf]

Subjects: Computer Science >> Computer Software

 Evolution is the driving force behind the evolution of biological intelligence. Learning is the driving force behind human civilization. The combination of evolution and learning can form an entire natural world. Now, reinforcement learning has shown significant effects in many places. However, Currently, researchers in the field of optimization algorithms mainly focus on evolution strategies. And there is very little research on learning. Inspired by these ideas, this paper proposes a new particle swarm optimization algorithm Reinforcement learning based Ensemble particle swarm optimizer (RLEPSO) that combines reinforcement learning. The algorithm uses reinforcement learning for pre-training in the design phase to automatically find a more effective combination of parameters for the algorithm to run better and Complete optimization tasks faster. Besides, this algorithm integrates two robust particle swarm variants. And it sets the weight parameters for different algorithms to better adapt to the solution requirements of a variety of different optimization problems, which significantly improves the robustness of the algorithm. RLEPSO makes a certain number of sub-swarms to increase the probability of finding the global optimum and increasing the diversity of particle swarms. This proposed RLEPSO is evaluated on an optimization test functions benchmark set (CEC2013) with 28 functions and compared with other eight particle swarm optimization variants, including three state-of-the-art optimization algorithms. The results show that RLEPSO has better performance and outperforms all compared algorithms.

## 6. chinaXiv:202106.00111 [pdf]

Subjects: Computer Science >> Computer Software

 Intuitively there is drastic distinction between the “pure” decentralized block-chain systems like Defis and those that only utilizes block-chain as an enhancing technology but remains centralized with real-world business model and conventional technologies like database, application server etc. Our study explores extensively this distinction from a methodological point of view, classifies them into blockchain-complete and blockchain-partial, analyzes key features of the two types, and reveal the root cause of this distinction. We analyze the function or, in more strong words, the “ultimate purpose” of blockchain in the blockchain-partial systems, and present a conceptual model we named proof-chain that quite satisfactorily represented the general paradigm of blockchain in blockchain-partial systems. A universal tension between strength of proof-chain and privacy is then revealed and the zero-knowledge based proof-chain takes shape. Several case studies demonstrate the explaining power of our proof-chain methodology. We then apply proof-chain methodology to the analysis of the ecosystem of a collaborating group of blockchain-partial systems, representing the paradigm of public and private data domain whose border the proof-chain crosses. Finally, some derived guidelines from this methodology speaks usefulness of our methodology.

## 7. chinaXiv:202106.00009 [pdf]

Subjects: Computer Science >> Other Disciplines of Computer Science

 Complex evidence theory has been applied to several fields due to its advantages in modeling and processing uncertain information. However,to measure the uncertainty of the complex mass function is still an open issue. The main contribution of this paper is to propose a complex-valued Deng entropy. The complex-valued Deng entropy can effectively measure the uncertainty of the mass function in the complex-valued framework. Meanwhile, the complex-valued Deng entropy is a generalization of the Deng entropy and Shannon entropy. That is, the complex-valued Deng entropy can degenerate to classical Deng entropy when the complex-valued mass function degenerates to a mass function in real space. In addition, the proposed complex-valued Deng entropy can also degenerates to Shannon entropy when the complex-valued mass function degenerates to a probability distribution in real space. Some numerical examples demonstrate the compatibility and effectiveness of the complex-valued Deng entropy.

## 8. chinaXiv:202106.00005 [pdf]

Subjects: Computer Science >> Other Disciplines of Computer Science

 Complex-valued expression models have been widely used in the application of intelligent decision systems. However, there is a lack of entropy to measure the uncertain information of the complex-valued probability distribution. Therefore, how to reasonably measure the uncertain information of the complex-valued probability distribution is a gap to be filled. In this paper, inspired by the Renyi entropy, we propose the Complex-valued Renyi entropy, which can measure uncertain information of the complex-valued probability distribution under the framework of complex numbers, and is also the first time to measure uncertain information in the complex space. The Complex-valued Renyi entropy contains the features of the classical Renyi entropy, i.e., the Complex-valued Renyi Entropy corresponds to different information functions with different parameters q. Meanwhile, the Complex-valued Renyi entropy has some properties, such as non-negativity, monotonicity, etc. Some numerical examples can demonstrate the flexibilities and reasonableness of the Complex-valued Renyi entropy.

## 9. chinaXiv:202105.00090 [pdf]

Subjects: Computer Science >> Computer Application Technology

 本文提出了一个使用深度学习方法预测锂离子电池分容工序的容量的解决方案。该方案从化成和分容工序中提取部分工步的物理观测值记录作为特征，训练了一个深度神经网络（Deep Neural Network, DNN）实现了电池容量的精准预测。据测试，该模型预测的电池容量与真实值相比，平均百分比绝对误差（Mean Absolute Percentage Error, MAPE）仅为0.78%。将该模型与生产线结合，可以大大缩减生产时间与能耗，降低电池生产成本。