MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning

Attention mechanism enables the Graph Neural Networks(GNNs) to learn the attention weights between the target node and its one-hop neighbors, the performance is further improved. However, the most existing GNNs are oriented to homogeneous graphs and each layer can only aggregate the information of one-hop neighbors. Stacking multi-layer networks will introduce a lot of noise and easily lead to over smoothing. We propose a Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning method (MHNF). Specifically, we first propose a hybrid metapath autonomous extraction model to efficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level heterogeneous Information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is proposed, which can efficiently integrate different-hop and different-path neighborhood information respectively. This paper can solve the problem of aggregating the multi-hop neighborhood information and can learn hybrid metapaths for target task, reducing the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information of different levels. Experimental results on real datasets show that MHNF is superior to state-of-the-art methods in node classification and clustering tasks (10.94% - 69.09% and 11.58% - 394.93% relative improvement on average, respectively)

Can We Transfer Noise Patterns? An Multi-environment Spectrum Analysis Model Using Generated Cases

Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system's learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://github.com/Magnomic/CNST

Evidence of Noncollinear Spin Texture in Magnetic Moir\'e Superlattices

Moir\'e magnetism, parallel with moir\'e electronics that has led to novel correlated and topological electronic states, emerges as a new venue to design and control exotic magnetic phases in twisted magnetic two-dimensional(2D) crystals. Here, we report direct evidence of noncollinear spin texture in 2D twisted double bilayer (tDB) magnet chromium triiodide (CrI$_3$). Using magneto-optical spectroscopy in tDB CrI$_3$, we revealed the presence of a net magnetization, unexpected from the composing antiferromagnetic bilayers with compensated magnetizations, and the emergence of noncollinear spins, originated from the moir\'e exchange coupling-induced spin frustrations. Exploring the twist angle dependence, we demonstrated that both features are present in tDB CrI$_3$ with twist angles from 0.5$^o$ to 5$^o$, but are most prominent in the 1.1$^o$ tDB CrI$_3$. Focusing on the temperature dependence of the 1.1$^o$ tDB CrI$_3$, we resolved the dramatic suppression in the net magnetization onset temperature and the significant softening of noncollinear spins, as a result of the moir\'e induced frustration. Our results demonstrate the power of moir\'e superlattices in introducing novel magnetic phenomena that are absent in natural 2D magnets

Interface induced Zeeman-protected superconductivity in ultrathin crystalline lead films

Two dimensional (2D) superconducting systems are of great importance to exploring exotic quantum physics. Recent development of fabrication techniques stimulates the studies of high quality single crystalline 2D superconductors, where intrinsic properties give rise to unprecedented physical phenomena. Here we report the observation of Zeeman-type spin-orbit interaction protected superconductivity (Zeeman-protected superconductivity) in 4 monolayer (ML) to 6 ML crystalline Pb films grown on striped incommensurate (SIC) Pb layers on Si(111) substrates by molecular beam epitaxy (MBE). Anomalous large in-plane critical field far beyond the Pauli limit is detected, which can be attributed to the Zeeman-protected superconductivity due to the in-plane inversion symmetry breaking at the interface. Our work demonstrates that in superconducting heterostructures the interface can induce Zeeman-type spin-orbit interaction (SOI) and modulate the superconductivity

Ising Superconductivity and Quantum Phase Transition in Macro-Size Monolayer NbSe2

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have a range of unique physics properties and could be used in the development of electronics, photonics, spintronics and quantum computing devices. The mechanical exfoliation technique of micro-size TMD flakes has attracted particular interest due to its simplicity and cost effectiveness. However, for most applications, large area and high quality films are preferred. Furthermore, when the thickness of crystalline films is down to the 2D limit (monolayer), exotic properties can be expected due to the quantum confinement and symmetry breaking. In this paper, we have successfully prepared macro-size atomically flat monolayer NbSe2 films on bilayer graphene terminated surface of 6H-SiC(0001) substrates by molecular beam epitaxy (MBE) method. The films exhibit an onset superconducting critical transition temperature above 6 K, 2 times higher than that of mechanical exfoliated NbSe2 flakes. Simultaneously, the transport measurements at high magnetic fields reveal that the parallel characteristic field Bc// is at least 4.5 times higher than the paramagnetic limiting field, consistent with Zeeman-protected Ising superconductivity mechanism. Besides, by ultralow temperature electrical transport measurements, the monolayer NbSe2 film shows the signature of quantum Griffiths singularity when approaching the zero-temperature quantum critical point

Fast Estimation Method of Space-Time Two-Dimensional Positioning Parameters Based on Hadamard Product

The estimation speed of positioning parameters determines the effectiveness of the positioning system. The time of arrival (TOA) and direction of arrival (DOA) parameters can be estimated by the space-time two-dimensional multiple signal classification (2D-MUSIC) algorithm for array antenna. However, this algorithm needs much time to complete the two-dimensional pseudo spectral peak search, which makes it difficult to apply in practice. Aiming at solving this problem, a fast estimation method of space-time two-dimensional positioning parameters based on Hadamard product is proposed in orthogonal frequency division multiplexing (OFDM) system, and the Cramer-Rao bound (CRB) is also presented. Firstly, according to the channel frequency domain response vector of each array, the channel frequency domain estimation vector is constructed using the Hadamard product form containing location information. Then, the autocorrelation matrix of the channel response vector for the extended array element in frequency domain and the noise subspace are calculated successively. Finally, by combining the closed-form solution and parameter pairing, the fast joint estimation for time delay and arrival direction is accomplished. The theoretical analysis and simulation results show that the proposed algorithm can significantly reduce the computational complexity and guarantee that the estimation accuracy is not only better than estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm and 2D matrix pencil (MP) algorithm but also close to 2D-MUSIC algorithm. Moreover, the proposed algorithm also has certain adaptability to multipath environment and effectively improves the ability of fast acquisition of location parameters

Cross-motif Matching and Hierarchical Contrastive Learning for Recommendation

Recently, leveraging different channels to model social semantic information and using self-supervised learning tasks to boost recommendation performance has been proven to be a very promising work. However, how to deeply dig out the relationship between different channels and make full use of it while maintaining the uniqueness of each channel is a problem that has not been well studied and resolved in this field. Under such circumstances, this paper explores and verifies the deficiency of directly constructing contrastive learning tasks on different channels with practical experiments and proposes the scheme of interactive modeling and matching representation across different channels. This is the first attempt in the field of recommender systems, we believe the insight of this paper is inspirational to future self-supervised learning research based on multi-channel information. To solve this problem, we propose a cross-channel matching representation model based on attentive interaction, which realizes efficient modeling of the relationship between cross-channel information. Based on this, we also propose a hierarchical self-supervised learning model, which realizes two levels of self-supervised learning within and between channels, which improves the ability of self-supervised tasks to autonomously mine different levels of potential information. We have conducted abundant experiments, and various metrics on multiple public datasets show that the method proposed in this paper has a significant improvement compared with the state-of-the-art methods, no matter in the general or cold-start scenario. And in the experiment of model variant analysis, the benefits of the cross-channel matching representation model and the hierarchical self-supervised model proposed in this paper are also fully verified.Comment: Rename the paper,the full-text language was polished and part of the experiment content was revise