Real-complex transition driven by quasiperiodicity: a new universality class beyond $mathcal{PT}$ symmetric one

We study a one-dimensional lattice model subject to non-Hermitian quasiperiodic potentials. Firstly, we strictly demonstrate that there exists an interesting dual mapping relation between $|a|<1$ and $|a|>1$ with regard to the potential tuning parameter $a$. The localization property of $|a|<1$ can be directly mapping to that of $|a|>1$, the analytical expression of the mobility edge of $|a|>1$ is therefore obtained through spectral properties of $|a|<1$. More impressive, we prove rigorously that even if the phase $theta eq 0$ in quasiperiodic potentials, the model becomes non-$mathcal{PT}$ symmetric, however, there still exists a new type of real-complex transition driven by non-Hermitian disorder, which is a new universality class beyond $mathcal{PT}$ symmetric class.

Fine-Grained Element Identification in Complaint Text of Internet Fraud

Existing system dealing with online complaint provides a final decision without explanations. We propose to analyse the complaint text of internet fraud in a fine-grained manner. Considering the complaint text includes multiple clauses with various functions, we propose to identify the role of each clause and classify them into different types of fraud element. We construct a large labeled dataset originated from a real finance service platform. We build an element identification model on top of BERT and propose additional two modules to utilize the context of complaint text for better element label classification, namely, global context encoder and label refiner. Experimental results show the effectiveness of our model.

A prismatic approach to $(varphi, hat G)$-modules and $F$-crystals

We give a new construction of $(varphi, hat G)$-modules using the theory of prisms developed by Bhatt and Scholze. As an application, we give a different proof about the equivalence between the category of prismatic $F$-crystals in finite locally free $mathcal{O}_{Delta}$-modules over $mathrm{Spf}(mathcal{O}_K)$ and the category of lattices in crystalline representations of $G_K$, where $K$ is a complete discretely valued field of mixed characteristic with perfect residue field. We also propose a possible generalization of this result for semi-stable representations using the absolute logarithmic prismatic site.

A Signal Detection Scheme Based on Deep Learning in OFDM Systems

Channel estimation and signal detection are essential steps to ensure the quality of end-to-end communication in orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we develop a DDLSD approach, i.e., Data-driven Deep Learning for Signal Detection in OFDM systems. First, the OFDM system model is established. Then, the long short-term memory (LSTM) is introduced into the OFDM system model. Wireless channel data is generated through simulation, the preprocessed time series feature information is input into the LSTM to complete the offline training. Finally, the trained model is used for online recovery of transmitted signal. The difference between this scheme and existing OFDM receiver is that explicit estimated channel state information (CSI) is transformed into invisible estimated CSI, and the transmit symbol is directly restored. Simulation results show that the DDLSD scheme outperforms the existing traditional methods in terms of improving channel estimation and signal detection performance.

Anisotropic neutrinos and gravitational waves from black hole neutrino-dominated accretion flows in fallback core-collapse supernovae

Fallback in core-collapse supernovae (CCSNe) plays an important role in determining the properties of the central compact remnants, which might produce a black hole (BH) hyperaccretion system in the centre of a massive CCSN. When the accretion rate is extremely high and neutrino cooling is dominant, the hyperaccretion should be in the phase of the neutrino-dominated accretion flows (NDAFs), and thus a large number of anisotropic MeV neutrinos will be launched from the disc along with the strong gravitational waves (GWs). In this paper, we perform a series of one-dimensional CCSN simulations with the initial explosion energy in the range of $2-8$ B (1 B = $10^{51}$ erg) to investigate the fallback processes. By considering the evolution of the central BH mass and spin in the fallback accretion, we present the effects of the initial explosion energies, masses and metallicities of the massive progenitor stars on the spectra of anisotropic MeV neutrinos and the waveform of GWs from NDAFs. These neutrino or GW signals might be detected by operational or future detectors, and the multimessenger joint detections could constrain the properties of CCSNe and progenitor stars.

Multi-stage Pre-training over Simplified Multimodal Pre-training Models

Multimodal pre-training models, such as LXMERT, have achieved excellent results in downstream tasks. However, current pre-trained models require large amounts of training data and have huge model sizes, which make them difficult to apply in low-resource situations. How to obtain similar or even better performance than a larger model under the premise of less pre-training data and smaller model size has become an important problem. In this paper, we propose a new Multi-stage Pre-training (MSP) method, which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train the model in stages. We also design several different pre-training tasks suitable for the information granularity in different stage in order to efficiently capture the diverse knowledge from a limited corpus. We take a Simplified LXMERT (LXMERT- S), which has only 45.9% parameters of the original LXMERT model and 11.76% of the original pre-training data as the testbed of our MSP method. Experimental results show that our method achieves comparable performance to the original LXMERT model in all downstream tasks, and even outperforms the original model in Image-Text Retrieval task.

Co-doping effects on magnetism and superconductivity in the 112-type EuFeAs2 system

The discovery of EuFeAs2, currently the only charge-neutral parent phase of the 112-type iron-pnictide system, provides a new platform for the study of elemental doping effects on magnetism and superconductivity (SC). In this study, a series of polycrystalline EuFe1-yCoyAs2 and Eu0.9Pr0.1Fe1-yCoyAs2 samples are synthesized through solid-state reaction, and the evolutions of SC and magnetism with Co doping in EuFeAs2 and Eu0.9Pr0.1FeAs2 are investigated by electrical transport and magnetic susceptibility measurements. For EuFe1-yCoyAs2, the Eu-related antiferromagnetic (AFM) transition around 40 K is barely affected by Co doping, while the Fe-related spin density wave (SDW) transition temperature drops rapidly. Meanwhile, SC is induced by a trace amount of Co doping, with a highest transition temperature Tc ~ 28 K found in EuFe0.9Co0.1As2. For the Eu0.9Pr0.1Fe1-yCoyAs2 series, the magnetism and superconductivity show similar evolutions upon Co doping, and the highest Tc is enhanced to 30.6 K with an optimum doping level y ~ 0.07. Our results shed light on the competition between SC and SDW with Co doping in the 112-type EuFeAs2 system.

Multi-Level Graph Contrastive Learning

Graph representation learning has attracted a surge of interest recently, whose target at learning discriminant embedding for each node in the graph. Most of these representation methods focus on supervised learning and heavily depend on label information. However, annotating graphs are expensive to obtain in the real world, especially in specialized domains (i.e. biology), as it needs the annotator to have the domain knowledge to label the graph. To approach this problem, self-supervised learning provides a feasible solution for graph representation learning. In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs. Specifically, we introduce a novel contrastive view - topological and feature space views. The original graph is first-order approximation structure and contains uncertainty or error, while the $k$NN graph generated by encoding features preserves high-order proximity. Thus $k$NN graph generated by encoding features not only provide a complementary view, but is more suitable to GNN encoder to extract discriminant representation. Furthermore, we develop a multi-level contrastive mode to preserve the local similarity and semantic similarity of graph-structured data simultaneously. Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven datasets.

Mobility edges in $mathcal{PT}$-symmetric cross-stitch flat band lattices

We study the cross-stitch flat band lattice with a $mathcal{PT}$-symmetric on-site potential and uncover mobility edges with exact solutions. Furthermore, we study the relationship between the $mathcal{PT}$ symmetry broken point and the localization-delocalization transition point, and verify that mobility edges in this non-Hermitian model is available to signal the $mathcal{PT}$ symmetry breaking.

Anomalous mobility edges in one-dimensional quasiperiodic models

Mobility edges, separating localized from extended states, are known to arise in the single-particle energy spectrum of disordered systems in dimension strictly higher than two and certain quasiperiodic models in one dimension. Here we unveil a different class of mobility edges, dubbed anomalous mobility edges, that separate bands of localized states from bands of critical states in diagonal and off-diagonal quasiperiodic models. We first introduce an exactly solvable quasi-periodic diagonal model and analytically demonstrate the existence of anomalous mobility edges. Moreover, numerical multifractal analysis of the corresponding wave functions confirms the emergence of a finite band of critical states. We then extend the sudy to a quasiperiodic off-diagonal Su-Schrieffer-Heeger model and show numerical evidence of anomalous mobility edges. We finally discuss possible experimental realizations of quasi-periodic models hosting anomalous mobility edges. These results shed new light on the localization and critical properties of low-dimensional systems with aperiodic order.