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The interaction between Ribonucleic Acids (RNAs) and proteins, also called RNA Protein Interaction (RPI), plays an important role in the life activities of organisms, including in various regulatory processes, such as gene splicing, gene localization, and disease pathogenesis. RPI Prediction (RPIP) predicts the interactions between RNAs and proteins, which includes looking for the existence of interactions and the binding sites of interactions, and adding RNA-protein functional annotations (such as immunity regulation, neuroprotection, etc). Due to the huge amounts of complex biological data, Deep Learning-based RPIP (DL-based RPIP) has been widely investigated, as it can extract high-dimensional features from data and make accurate predictions. Over the last decade, there have been many achievements and contributions in DL-based RPIP. Although some previous studies review DL-based RPIP, to the best of our knowledge, there is still a lack of a comprehensive survey. In this paper, we extensively survey DL-based RPIP in terms of its entire process, including: feature encoding, deep learning modeling, results evaluation, RPIP application domains, and available websites and software. We also identify some open research challenges, and discuss the potential future work for DL-based RPIP.

Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system. Various external factors, such as weather changes and the emergence of new electricity consumption scenarios, can impact electricity demand, causing load data to fluctuate and become non-linear, which increases the complexity and difficulty of STELF. In the past decade, deep learning has been applied to STELF, modeling and predicting electricity demand with high accuracy, and contributing significantly to the development of STELF. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation. This paper also identifies some research challenges and potential research directions to be further investigated in future work.

We investigate the impact of the input dimension on the generalization error in generative adversarial networks (GANs). In particular, we first provide both theoretical and practical evidence to validate the existence of an optimal input dimension (OID) that minimizes the generalization error. Then, to identify the OID, we introduce a novel framework called generalized GANs (G-GANs), which includes existing GANs as a special case. By incorporating the group penalty and the architecture penalty developed in the paper, G-GANs have several intriguing features. First, our framework offers adaptive dimensionality reduction from the initial dimension to a dimension necessary for generating the target distribution. Second, this reduction in dimensionality also shrinks the required size of the generator network architecture, which is automatically identified by the proposed architecture penalty. Both reductions in dimensionality and the generator network significantly improve the stability and the accuracy of the estimation and prediction. Theoretical support for the consistent selection of the input dimension and the generator network is provided. Third, the proposed algorithm involves an end-to-end training process, and the algorithm allows for dynamic adjustments between the input dimension and the generator network during training, further enhancing the overall performance of G-GANs. Extensive experiments conducted with simulated and benchmark data demonstrate the superior performance of G-GANs. In particular, compared to that of off-the-shelf methods, G-GANs achieves an average improvement of 45.68% in the CT slice dataset, 43.22% in the MNIST dataset and 46.94% in the FashionMNIST dataset in terms of the maximum mean discrepancy or Frechet inception distance. Moreover, the features generated based on the input dimensions identified by G-GANs align with visually significant features.

Interlayer exchange coupling (IEC) between two magnetic layers sandwiched by a nonmagnetic spacer layer plays a critical role in shaping the magnetic properties of such heterostructures. The quantum anomalous Hall (QAH) effect has been realized in a structure composed of two magnetically doped topological insulator (TI) layers separated by an undoped TI layer. The quantized Hall conductance observed in this sandwich heterostructure originates from the combined contribution of the top and bottom surface states. In this work, we employ molecular beam epitaxy to synthesize a series of magnetic TI sandwiches with varying thicknesses of the middle undoped TI layer. The well-quantized QAH effect is observed in all these samples and its critical behavior is modulated by the IEC between the top and bottom magnetic TI layers. Near the plateau phase transition (PPT), we find that thinner QAH samples exhibit a two-dimensional critical metal behavior with nearly temperature-independent longitudinal resistance, whereas thicker QAH samples behave as a three-dimensional insulator with reduced longitudinal resistance at higher temperatures. The IEC-induced critical-metal-to-insulator transition in the QAH PPT regime can be understood through a two-channel Chalker-Coddington network model by tuning inter-channel tunneling. The agreement between experiment and theory strongly supports the QAH PPT within the Kosterlitz-Thouless framework, where the critical metal and disordered insulator phases exist in bound and unbound states of vortex-antivortex pairs, respectively., Comment: 19 pages, 4 figures, comments are very much welcome

The nitrogen vacancy (NV) center in diamond is an increasingly popular quantum sensor for microscopy of electrical current, magnetization, and spins. However, efficient NV-sample integration with a robust, high-quality interface remains an outstanding challenge to realize scalable, high-throughput microscopy. In this work, we characterize a diamond micro-chip (DMC) containing a (111)-oriented NV ensemble; and demonstrate its utility for high-resolution quantum microscopy. We perform strain imaging of the DMC and find minimal detrimental strain variation across a field-of-view of tens of micrometer. We find good ensemble NV spin coherence and optical properties in the DMC, suitable for sensitive magnetometry. We then use the DMC to demonstrate wide-field microscopy of electrical current, and show that diffraction-limited quantum microscopy can be achieved. We also demonstrate the deterministic transfer of DMCs with multiple materials of interest for next-generation electronics and spintronics. Lastly, we develop a polymer-based technique for DMC placement. This work establishes the DMC's potential to expand the application of NV quantum microscopy in materials, device, geological, biomedical, and chemical sciences., Comment: Includes supplementary materials section

The interface of two materials can harbor unexpected emergent phenomena. One example is interface-induced superconductivity. In this work, we employ molecular beam epitaxy to grow a series of heterostructures formed by stacking together two non-superconducting antiferromagnetic materials, an intrinsic antiferromagnetic topological insulator MnBi2Te4 and an antiferromagnetic iron chalcogenide FeTe. Our electrical transport measurements reveal interface-induced superconductivity in these heterostructures. By performing scanning tunneling microscopy and spectroscopy measurements, we observe a proximity-induced superconducting gap on the top surface of the MnBi2Te4 layer, confirming the interaction between superconductivity and antiferromagnetism in the MnBi2Te4 layer. Our findings will advance the fundamental inquiries into the topological superconducting phase in hybrid devices and provide a promising platform for the exploration of chiral Majorana physics in MnBi2Te4-based heterostructures., Comment: 24 pages, 4 figures, comments are welcome

The plateau phase transition in quantum anomalous Hall (QAH) insulators corresponds to a quantum state wherein a single magnetic domain gives way to multiple magnetic domains and then re-converges back to a single magnetic domain. The layer structure of the sample provides an external knob for adjusting the Chern number C of the QAH insulators. Here, we employ molecular beam epitaxy (MBE) to grow magnetic topological insulator (TI) multilayers with an asymmetric layer structure and realize the magnetic field-driven plateau phase transition between two QAH states with odd Chern number change {\Delta}C. In multilayer structures with C=+-1 and C=+-2 QAH states, we find two characteristic power-law behaviors between temperature and the scaling variables on the magnetic field at transition points. The critical exponents extracted for the plateau phase transitions with {\Delta}C=1 and {\Delta}C=3 in QAH insulators are found to be nearly identical, specifically, k1~0.390+-0.021 and k2~0.388+-0.015, respectively. We construct a four-layer Chalker-Coddington network model to understand the consistent critical exponents for the plateau phase transitions with {\Delta}C=1 and {\Delta}C=3. This work will motivate further investigations into the critical behaviors of plateau phase transitions with different {\Delta}C in QAH insulators and provide new opportunities for the development of QAH chiral edge current-based electronic and spintronic devices., Comment: 18 pages, 4 figures. Comments are welcome

When two different electronic materials are brought together, the resultant interface often shows unexpected quantum phenomena, including interfacial superconductivity and Fu-Kane topological superconductivity (TSC). Here, we use molecular beam epitaxy (MBE) to synthesize heterostructures formed by stacking together two magnetic materials, a ferromagnetic topological insulator (TI) and an antiferromagnetic iron chalcogenide (FeTe). We discover emergent interface-induced superconductivity in these heterostructures and demonstrate the trifecta occurrence of superconductivity, ferromagnetism, and topological band structure in the magnetic TI layer, the three essential ingredients of chiral TSC. The unusual coexistence of ferromagnetism and superconductivity can be attributed to the high upper critical magnetic field that exceeds the Pauli paramagnetic limit for conventional superconductors at low temperatures. The magnetic TI/FeTe heterostructures with robust superconductivity and atomically sharp interfaces provide an ideal wafer-scale platform for the exploration of chiral TSC and Majorana physics, constituting an important step toward scalable topological quantum computation., Comment: 14 pages, 4 figures. Accepted by Science. Comments are welcome

Magnetic topological states refer to a class of exotic phases in magnetic materials with their non-trivial topological property determined by magnetic spin configurations. An example of such states is the quantum anomalous Hall (QAH) state, which is a zero magnetic field manifestation of the quantum Hall effect. Current research in this direction focuses on QAH insulators with a thickness of less than 10nm. The thick QAH insulators in the three-dimensional(3D) regime are limited, largely due to inevitable bulk carriers being introduced in thick magnetic TI samples. Here, we employ molecular beam epitaxy (MBE) to synthesize magnetic TI trilayers with a thickness of up to ~106 nm. We find these samples exhibit well-quantized Hall resistance and vanishing longitudinal resistance at zero magnetic field. By varying magnetic dopants, gate voltages, temperature, and external magnetic fields, we examine the properties of these thick QAH insulators and demonstrate the robustness of the 3D QAH effect. The realization of the well-quantized 3D QAH effect indicates that the nonchiral side surface states of our thick magnetic TI trilayers are gapped and thus do not affect the QAH quantization. The 3D QAH insulators of hundred-nanometer thickness provide a promising platform for the exploration of fundamental physics, including axion physics and image magnetic monopole, and the advancement of electronic and spintronic devices to circumvent Moore's law., Comment: 24 pages, 5 figures. Comments are welcome

In this paper, we study the interaction between atom and the finite-size Su-Schrieffer-Heeger (SSH) model. We find that when the finite SSH model in the trivial phase, it can be viewed as the atom coupling with the waveguide with the finite bandwidths and non-linear dispersion relation. However, for the SSH model in the topological phase, when we consider the frequency of the atom is resonant with the edge mode of the SSH model, we find that the atom state couples to the two edge states. In this case, we find that there exists a special channel that can be utilized to transfer the atomic excitation to the ends of the SSH model using adiabatic processes. When the atom couples to the different sub-lattice, the excitation of the atom can be transferred to the leftmost or rightmost end of the chain, which provides the potential application toward quantum information processing. Furthermore, The excitation transfer of excited states of atoms to the ends of the chain can also be realized without the adiabatic process. Our work provides a pathway for realizing controllable quantum information transfer based on the atom couples topological matter.

Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution techniques for enhancing the quality of LR images and videos, few study has focused on investigate super-resolution for improving LR MER. The scarcity of investigation can be attributed to the inherent difficulty in capturing the subtle motions of micro-expressions, even in original-resolution MER samples, which becomes even more challenging in LR samples due to the loss of distinctive features. Furthermore, a lack of systematic benchmarking and thorough analysis of super-resolution-assisted MER methods has been noted. This paper tackles these issues by conducting a series of benchmark experiments that integrate both super-resolution (SR) and MER methods, guided by an in-depth literature survey. Specifically, we employ seven cutting-edge state-of-the-art (SOTA) MER techniques and evaluate their performance on samples generated from 13 SOTA SR techniques, thereby addressing the problem of super-resolution in MER. Through our empirical study, we uncover the primary challenges associated with SR-assisted MER and identify avenues to tackle these challenges by leveraging recent advancements in both SR and MER methodologies. Our analysis provides insights for progressing toward more efficient SR-assisted MER.

Over the last decade, the possibility of realizing topological superconductivity (TSC) has generated much excitement, mainly due to the potential use of its excitations (Majorana zero modes) in a fault-tolerant topological quantum computer 1,2. TSC can be created in electronic systems where the topological and superconducting orders coexist3, motivating the continued exploration of candidate material platforms to this end. Here, we use molecular beam epitaxy (MBE) to synthesize heterostructures that host emergent interfacial superconductivity when a non-superconducting antiferromagnet (FeTe) is interfaced with a topological insulator (TI) (Bi, Sb)2Te3 wherein the chemical potential can be tuned through varying the Bi/Sb ratio. By performing in-vacuo angle-resolved photoemission spectroscopy (ARPES) and ex-situ electrical transport measurements, we find that the superconducting transition temperature and the upper critical magnetic field are suppressed when the chemical potential approaches the Dirac point. This observation implies a direct correlation between the interfacial superconductivity and Dirac electrons of the TI layer. We provide evidence to show that the observed interfacial superconductivity and its chemical potential dependence is the result of the competition between the Ruderman-Kittel-Kasuya-Yosida-type ferromagnetic coupling mediated by Dirac surface states and antiferromagnetic exchange couplings that generate the bicollinear antiferromagnetic order in the FeTe layer. The Dirac-fermion-assisted interfacial superconductivity in (Bi,Sb)2Te3/FeTe heterostructures provides a new approach to probe TSC and Majorana physics in hybrid devices and potentially constitutes an alternative platform for topological quantum computation., Comment: 32 pages and 4 figures. Accepted by Nature Communications

This is a note of the author's ongoing work on persistent Sullivan minimal models. Sullivan minimal models have been important tools in rational homotopy theory for representing topological spaces using simplified algebraic structures. In this note, we study persistent Sullivan minimal models for persistent spaces and establish their stability results. For the case of Vietoris-Rips filtrations arising from metric spaces, such stability results can provide a stronger lower bound estimate of the Gromov-Hausdorff distance between metric spaces than the standard persistent homology.

In this paper, we develop a novel efficient and robust nonparametric regression estimator under a framework of feedforward neural network. There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, who integrates the information from observed data, as well as the information from data structure. Consequently, the resulting estimator has desirable optimal properties, such as efficiency. Second, different from the traditional maximum likelihood estimation (MLE), the proposed method avoid the specification of the distribution, hence is flexible to any kind of distribution, such as heavy tails, multimodal or heterogeneous distribution. Third, the proposed loss function relies on probabilities rather than direct observations as in least squares, contributing the robustness in the proposed estimator. Finally, the proposed loss function involves nonparametric regression function only. This enables a direct application of existing packages, simplifying the computation and programming. We establish the large sample property of the proposed estimator in terms of its excess risk and minimax near-optimal rate. The theoretical results demonstrate that the proposed estimator is equivalent to the true MLE in which the density function is known. Our simulation studies show that the proposed estimator outperforms the existing methods in terms of prediction accuracy, efficiency and robustness. Particularly, it is comparable to the true MLE, and even gets better as the sample size increases. This implies that the adaptive and data-driven loss function from the estimated density may offer an additional avenue for capturing valuable information. We further apply the proposed method to four real data examples, resulting in significantly reduced out-of-sample prediction errors compared to existing methods.

An axion insulator is a three-dimensional (3D) topological insulator (TI), in which the bulk maintains the time-reversal symmetry or inversion symmetry but the surface states are gapped by surface magnetization. The axion insulator state has been observed in molecular beam epitaxy (MBE)-grown magnetically doped TI sandwiches and exfoliated intrinsic magnetic TI MnBi2Te4 flakes with an even number layer. All these samples have a thickness of ~10 nm, near the 2D-to-3D boundary. The coupling between the top and bottom surface states in thin samples may hinder the observation of quantized topological magnetoelectric response. Here, we employ MBE to synthesize magnetic TI sandwich heterostructures and find that the axion insulator state persists in a 3D sample with a thickness of ~106 nm. Our transport results show that the axion insulator state starts to emerge when the thickness of the middle undoped TI layer is greater than ~3 nm. The 3D hundred-nanometer-thick axion insulator provides a promising platform for the exploration of the topological magnetoelectric effect and other emergent magnetic topological states, such as the high-order TI phase., Comment: 25 pages, 5 figures. Comments are very much welcome