Fast high-resolution nuclear magnetic resonance spectroscopy through indirect zero-quantum coherence detection in inhomogeneous fields

In many cases, high-resolution nuclear magnetic resonance （NMR） spectra are virtually impossible to obtain by con- ventional nuclear magnetic resonance methods because of inhomogeneity of magnetic field and inherent heterogeneity of sample. Although conventional intramolecular zero-quantum coherence （ZQC） can be used to obtain high-resolution spectrum in inhomogeneous field, the acquisition takes rather long time. In this paper, a spatially encoded intramolecular ZQC technique is proposed to fast acquire high-resolution NMR spectrum in inhomogeneous field. For the first time, the gradient-driven decoding technique is employed to selectively acquire intramolecular ZQC signals. Theoretical analyses and experimental observations demonstrate that high-resolution NMR spectral information can be retrieved within several scans even when the field inhomogeneity is severe enough to erase most spectral information. This work provides a new way to enhance the acquisition efficiency of high-resolution intramolecular ZQC spectroscopy in inhomogeneous fields.     

Hadamard NMR spectroscopy for two-dimensional quantum information processing and parallel search algorithms

Hadamard spectroscopy has earlier been used to speed-up multi-dimensional NMR experiments. In this work, we speed-up the two-dimensional quantum computing scheme, by using Hadamard spectroscopy in the indirect dimension, resulting in a scheme which is faster and requires the Fourier transformation only in the direct dimension. Two and three qubit quantum gates are implemented with an extra observer qubit. We also use one-dimensional Hadamard spectroscopy for binary information storage by spatial encoding and implementation of a parallel search algorithm.     

Photonic analog-to-digital converter based on the robust symmetrical number system

A novel approach to realizing photonic analog-to-digital conversion with Gray-code-like property is proposed and demonstrated. Instead of using Mach-Zehnder modulators (MZMs) with different half-wave voltages, an array of MZMs with identical half-wave voltages are applied to realize quantization and encoding, which greatly simplifies the implementation. Multiple comparators with preset thresholds are applied at the output of each MZM to improve the number of bits. Through properly setting the bias voltages of the MZMs, a photonic analog to digital converter (ADC) based on the robust symmetrical number system (RSNS) coding method is realized. As an example, a 3-channel structure with maximum quantization level of 17 (corresponding to 4.09 bits) is investigated in detail. We show that the differential encoding technique can be applied in the proposed structure, which increases the equivalent number of bits of the ADC system.     

A Fusion Transformer for Multivariable Time Series Forecasting: The Mooney Viscosity Prediction Case

Multivariable time series forecasting is an important topic of machine learning, and it frequently involves a complex mix of inputs, including static covariates and exogenous time series input. A targeted investigation of this input data is critical for improving prediction performance. In this paper, we propose the fusion transformer (FusFormer), a transformer-based model for forecasting time series data, whose framework fuses various computation modules for time series input and static covariates. To be more precise, the model calculation consists of two parallel stages. First, it employs a temporal encoder–decoder framework for extracting dynamic temporal features from time series data input, which analyzes and integrates the relative position information of sequence elements into the attention mechanism. Simultaneously, the static covariates are fed to the static enrichment module, which is inspired by gated linear units, to suppress irrelevant information and control the extent of nonlinear processing. Finally, the prediction results are calculated by fusing the outputs of the above two stages. Using Mooney viscosity forecasting as a case study, we demonstrate considerable forecasting performance improvements over existing methodologies and verify the effectiveness of each component of FusFormer via ablation analysis, and an interpretability use case is conducted to visualize temporal patterns of time series. The experimental results prove that FusFormer can achieve accurate Mooney viscosity prediction and improve the efficiency of the tire production process.     

An Optical Image Encryption Method Using Hopfield Neural Network

In this paper, aiming to solve the problem of vital information security as well as neural network application in optical encryption system, we propose an optical image encryption method by using the Hopfield neural network. The algorithm uses a fuzzy single neuronal dynamic system and a chaotic Hopfield neural network for chaotic sequence generation and then obtains chaotic random phase masks. Initially, the original images are decomposed into sub-signals through wavelet packet transform, and the sub-signals are divided into two layers by adaptive classification after scrambling. The double random-phase encoding in 4f system and Fresnel domain is implemented on two layers, respectively. The sub-signals are performed with different conversions according to their standard deviation to assure that the local information’s security is guaranteed. Meanwhile, the parameters such as wavelength and diffraction distance are considered as additional keys, which can enhance the overall security. Then, inverse wavelet packet transform is applied to reconstruct the image, and a second scrambling is implemented. In order to handle and manage the parameters used in the scheme, the public key cryptosystem is applied. Finally, experiments and security analysis are presented to demonstrate the feasibility and robustness of the proposed scheme.     

BHGAttN: A Feature-Enhanced Hierarchical Graph Attention Network for Sentiment Analysis

Recently, with the rise of deep learning, text classification techniques have developed rapidly. However, the existing work usually takes the entire text as the modeling object and pays less attention to the hierarchical structure within the text, ignoring the internal connection between the upper and lower sentences. To address these issues, this paper proposes a Bert-based hierarchical graph attention network model (BHGAttN) based on a large-scale pretrained model and graph attention network to model the hierarchical relationship of texts. During modeling, the semantic features are enhanced by the output of the intermediate layer of BERT, and the multilevel hierarchical graph network corresponding to each layer of BERT is constructed by using the dependencies between the whole sentence and the subsentence. This model pays attention to the layer-by-layer semantic information and the hierarchical relationship within the text. The experimental results show that the BHGAttN model exhibits significant competitive advantages compared with the current state-of-the-art baseline models.     

一种可用于原子全息光刻的二元计算全息新方法

原子全息光刻即采用二元计算全息片掩模来操纵原子,实现微细结构的制作。传统二元计算全息产生的全息片在重现时会产生不止一个实像,这对于原子全息光刻的操作是不利的。提出了一种非相位编码的方法,该方法利用基元函数叠加方式产生实的编码前全息图,再利用类似罗曼Ⅲ型的编码方式产生二元计算全息图。模拟结果表明,利用该方法产生的掩模板可以产生单一的同原始图案相对应的微细结构。     

Galois hulls of special Goppa codes and related codes with application to EAQECCs

Galois hulls of MDS codes can be applied to construst MDS entanglement-assisted quantum error-correcting codes (EAQECCs). Goppa codes and expurgated Goppa codes (resp., extended Goppa codes) over ${\mathbb{F}}_{q^m}$ are GRS codes (resp., extended GRS codes) when $m=1$. In this paper, we investigate the Galois dual codes of a special kind of Goppa codes and related codes and provide a necessary and sufficient condition for the Galois dual codes of such codes to be Goppa codes and related codes. Then we determine the Galois hulls of the above codes. In particular, we completely characterize Galois LCD, Galois self-orthogonal, Galois dual-containing and Galois self-dual codes among such family of codes. Moreover, we apply the above results to EAQECCs.