In this paper, the issue of the safety and power supply capability for the DC low-voltage distribution systems is investigated. The safety capability encompasses safe current threshold and safe voltage level. Based on the Chinese design standards, three key points concerning the safe current threshold, safe voltage level, and power supply capability are studied based on the practical data and the comparisons of DC systems with AC systems. For the safe current threshold, the hazards of DC and AC shock currents in dry environments are compared to derive appropriate values; for the safe voltage level, the sampling data of voltage/human resistance is adopted to deduce the safety voltage; while for the power supply capacity, the current-carrying capability of cables is analyzed to evaluate power delivery under different supply voltages. This paper can be referred to the practical applications of DC low-voltage distribution systems.

In this study, typical cases are combined to analyze the technical requirements for the TN-S system to prioritize the use of short-circuit overcurrent protection over ground fault protection. The necessity of adopting residual current action protection as ground fault protection is elucidated, as well as the importance of auxiliary equipotential bonding in reducing contact voltage. Various protective measures are proposed along with their respective roles and applicable conditions. The analytical process and resulting conclusions hold significant importance for grounding fault protection in engineering design and application.

Variational quantum algorithms have been acknowledged as the leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and optimization. When applied to tasks involving classical data, such algorithms generally begin with data encoding circuits and train quantum neural networks (QNNs) to minimize target functions. Although QNNs have been widely studied to improve these algorithms' performance on practical tasks, there is a gap in systematically understanding the influence of data encoding on the eventual performance. In this paper, we make progress in filling this gap by considering the common data encoding strategies based on parameterized quantum circuits. We prove that, under reasonable assumptions, the distance between the average encoded state and the maximally mixed state could be explicitly upper-bounded with respect to the width and depth of the encoding circuit. This result in particular implies that the average encoded state will concentrate on the maximally mixed state at an exponential speed on depth. Such concentration seriously limits the capabilities of quantum classifiers, and strictly restricts the distinguishability of encoded states from a quantum information perspective. To support our findings, we numerically verify these results on both synthetic and public data sets. Our results highlight the significance of quantum data encoding and may shed light on the future design of quantum encoding strategies.

Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables. Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.

As information sharing becomes more and more convenient, a lot of phenomena of plagiarism shows up. The study of cross-language plagiarism is an important problem that the whole academic circle tries to solve it collectively. In this paper, a multiple-features based cross-language plagiarism detection model is proposed, which includes cross-language plagiarism candidate retrieval based on multiple features and cross-language plagiarism detection based on dynamic text alignment. For cross-language plagiarism candidate retrieval, it is mainly based on the translation features. What's more, for cross-language plagiarism detection, a text-alignment based similarity analysis was used to filter the final results between the identified paragraphs. In this step, our approach doesn't use a machine translation system to convert longer text, but uses a dictionary to obtain the translation of a single word. Moreover, experimental results show that our method outperforms the previous methods and achieved the best results in four datasets.

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning. To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensortrain RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters. On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes 17,388 times fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6% in the Action Recognition task on the UCF11 dataset.

Standard recommender systems usually rely only on past user ratings as well as optional profiles of customers and products. In e-commerce settings, however, a more complete understanding of the corresponding bi-directional impact between the demands of customers and the supply capabilities of providers can be the key to success. This motivates us to design a recommendation model that explicitly reflects the supply and demand chains. We propose a Multi-relational Coupled Tensor and Matrix Factorization model, which jointly models user ratings as well as supply chain relationships for product recommendation. In addition, our model can predict the links between suppliers and manufacturers. We design an algorithm based on the Alternating Direction Method of Multipliers (ADMM) technique. Experiments on real-world datasets find that the proposed model outperforms traditional methods.

Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the trend. Whereas, the parallelization of PTTF still remains unexplored. In this paper, we propose a simple yet efficient Parallel Probabilistic Temporal Tensor Factorization, referred to as P2T2F, to provide a scalable PTTF solution. P2T2F is fundamentally disparate from existing parallel tensor factorizations by considering the probabilistic decomposition and the temporal effects of tensor data. It adopts a new tensor data split strategy to subdivide a large tensor into independent sub-tensors, the computation of which is inherently parallel. We train P2T2F with an efficient algorithm of stochastic Alternating Direction Method of Multipliers, and show that the convergence is guaranteed. Experiments on several real-word tensor datasets demonstrate that P2T2F is a highly effective and efficiently scalable algorithm dedicated for large scale probabilistic temporal tensor analysis.