Thermally assisted skyrmions creation in Pt/Co/Ta multilayer films

N\'eel-type magnetic skyrmions in multilayer films have attracted significant amount of attention recently for their stability at room temperature and capability of motion driven by a low-density electrical current, which can be potentially applied to spintronic devices. However, the thermal effect on the formation of the skyrmions and their behavior has rarely been studied. Here, we report a study on the creation of skyrmions in [Pt/Co/Ta]10 multilayer samples at different temperatures using an in-situ Lorentz transmission electron microscopy. By imaging the magnetization reversal process from positive (negative) saturation to negative (positive) saturation, we found that the skyrmions can be created by nucleation from ferromagnetic saturation state and by breaking the labyrinth domains under certain external fields. By tuning the external fields, a maximum density of skyrmions was reached at different temperatures. The key finding is that the creation of the skyrmions in the multilayers depends critically on the temperature and thermal history

Symmetry Enforced Self-Learning Monte Carlo Method Applied to the Holstein Model

Self-learning Monte Carlo method (SLMC), using a trained effective model to guide Monte Carlo sampling processes, is a powerful general-purpose numerical method recently introduced to speed up simulations in (quantum) many-body systems. In this work, we further improve the efficiency of SLMC by enforcing physical symmetries on the effective model. We demonstrate its effectiveness in the Holstein Hamiltonian, one of the most fundamental many-body descriptions of electron-phonon coupling. Simulations of the Holstein model are notoriously difficult due to the combination of the typical cubic scaling of fermionic Monte Carlo and the presence of extremely long autocorrelation times. Our method addresses both bottlenecks. This enables simulations on large lattices in the most difficult parameter regions, and evaluation of the critical point for the charge density wave transition at half-filling with high precision. We argue that our work opens a new research area of quantum Monte Carlo (QMC), providing a general procedure to deal with ergodicity in situations involving Hamiltonians with multiple, distinct low energy states.Comment: 4 pages, 3 figures with 2 pages supplemental materia

Self-Learning Determinantal Quantum Monte Carlo Method

Self-learning Monte Carlo method [arXiv:1610.03137, 1611.09364] is a powerful general-purpose numerical method recently introduced to simulate many-body systems. In this work, we implement this method in the framework of determinantal quantum Monte Carlo simulation of interacting fermion systems. Guided by a self-learned bosonic effective action, our method uses a cumulative update [arXiv:1611.09364] algorithm to sample auxiliary field configurations quickly and efficiently. We demonstrate that self-learning determinantal Monte Carlo method can reduce the auto-correlation time to as short as one near a critical point, leading to $\mathcal{O}(N)$-fold speedup. This enables to simulate interacting fermion system on a $100\times 100$ lattice for the first time, and obtain critical exponents with high accuracy.Comment: 5 pages, 4 figure

Table-to-Text: Describing Table Region with Natural Language

In this paper, we present a generative model to generate a natural language sentence describing a table region, e.g., a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the current state-of-the-art BLEU-4 score from 34.70 to 40.26 and from 33.32 to 39.12, respectively. Furthermore, we introduce an open-domain dataset WIKITABLETEXT including 13,318 explanatory sentences for 4,962 tables. Our model achieves a BLEU-4 score of 38.23, which outperforms template based and language model based approaches.Comment: 9 pages, 4 figures. This paper has been published by AAAI201

Experimental observation of Dirac-like surface states and topological phase transition in Pb1−x_{1-x}Snx_xTe(111) films

The surface of a topological crystalline insulator (TCI) carries an even number of Dirac cones protected by crystalline symmetry. We epitaxially grew high quality Pb$_{1-x}$Sn$_x$Te(111) films and investigated the TCI phase by in-situ angle-resolved photoemission spectroscopy. Pb$_{1-x}$Sn$_x$Te(111) films undergo a topological phase transition from trivial insulator to TCI via increasing the Sn/Pb ratio, accompanied by a crossover from n-type to p-type doping. In addition, a hybridization gap is opened in the surface states when the thickness of film is reduced to the two-dimensional limit. The work demonstrates an approach to manipulating the topological properties of TCI, which is of importance for future fundamental research and applications based on TCI

What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media

Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. Depression detection is key for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability. Although a few depression detection studies attempt to explain the decision based on the importance score or attention weights, these explanations misalign with the clinical depression diagnosis criterion that is based on depressive symptoms. To fill this gap, we follow the computational design science paradigm to develop a novel Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and interprets depressive symptoms as well as how long they last. Extensive empirical analyses using a large-scale dataset show that MSTPNet outperforms state-of-the-art depression detection methods with an F1-score of 0.851. This result also reveals new symptoms that are unnoted in the survey approach, such as sharing admiration for a different life. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media. In practice, our proposed method can be implemented in social media platforms to provide personalized online resources for detected depressed patients.Comment: 56 pages, 10 figures, 21 table