Emerging criticality in the disordered three-color Ashkin-Teller model

We study the effects of quenched disorder on the first-order phase transition in the two-dimensional three-color Ashkin-Teller model by means of large-scale Monte Carlo simulations. We demonstrate that the first-order phase transition is rounded by the disorder and turns into a continuous one. Using a careful finite-size-scaling analysis, we provide strong evidence for the emerging critical behavior of the disordered Ashkin-Teller model to be in the clean two-dimensional Ising universality class, accompanied by universal logarithmic corrections. This agrees with perturbative renormalization-group predictions by Cardy. As a byproduct, we also provide support for the strong-universality scenario for the critical behavior of the two-dimensional disordered Ising model. We discuss consequences of our results for the classification of disordered phase transitions as well as generalizations to other systems.Comment: 18 pages, 18 eps figures included, final version as publishe

Public Higher Education Governing Boards Composition and Regional Difference in U.S

[EN] Using The Public Higher Education Boards Database designed by Association of Governing Boards of Universities and Colleges (AGB) in 2008, this paper reviewed prior studies of governing boards and investigated regional differences of boards' characteristics including board type, selection method, board composition, provision condition, term length, supervision, and meeting frequency. The results show tha: (1) highly centralized state university governance with more political control exist in West and Middle West; (2) governing boards in Northeast are more autonomous with high percentage of alumni and self-perpetuating members and less political affiliations; (3) more faculty participations appear in South and West, and most Middle West boards do not have removal process and longer length of term.Park, HJ.; Zhu, Q. (2017). Public Higher Education Governing Boards Composition and Regional Difference in U.S. En Proceedings of the 3rd International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 1085-1094. https://doi.org/10.4995/HEAD17.2017.5519OCS1085109

Automatic Recognition of Knowledge Characteristics of Scientific and Technological Literature from the Perspective of Text Structure

This paper independently explores the chapter structure of scientific and technological literature in the field of shipbuilding in the natural sciences and the field of library and information in the social sciences. The chapter structure model of previous studies, namely \u27background, purpose, method, result, conclusion, demonstration,\u27 is quoted as the verification object of the document chapter structure in the field of exploration. In order to verify the rationality of the structure, this paper uses the deep learning models TextCNN, DPCNN, TextRCNN, and BiLSTM-Attention as experimental tools, and designs 5-fold cross-validation experiment and normal experiment, and finally verifies the rationality of the model structure, and It is concluded that the BiLSTM-Attention model can better identify the chapter structure in this field

Electric field-tunable layer polarization in graphene/boron nitride twisted quadrilayer superlattices

The recently observed unconventional ferroelectricity in AB bilayer graphene sandwiched by hexagonal Boron Nitride (hBN) presents a new platform to manipulate correlated phases in multilayered van der Waals heterostructures. We present a low-energy continuum model for AB bilayer graphene encapsulated by the top and bottom layers of either hBN or graphene, with two independent twist angles. For the graphene/hBN heterostructures, we show that twist angle asymmetry leads to a layer polarization of the valence and conduction bands. We also show that an out-of-plane displacement field not only tunes the layer polarization but also flattens the low-energy bands. We extend the model to show that the electronic structures of quadrilayer graphene heterostructure consisting of AB bilayer graphene encapsulated by the top and bottom graphene layers can similarly be tuned by an external electric field

A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition

Real-world data often exhibits a long-tailed distribution, in which head classes occupy most of the data, while tail classes only have very few samples. Models trained on long-tailed datasets have poor adaptability to tail classes and the decision boundaries are ambiguous. Therefore, in this paper, we propose a simple yet effective model, named Dual-Branch Long-Tailed Recognition (DB-LTR), which includes an imbalanced learning branch and a Contrastive Learning Branch (CoLB). The imbalanced learning branch, which consists of a shared backbone and a linear classifier, leverages common imbalanced learning approaches to tackle the data imbalance issue. In CoLB, we learn a prototype for each tail class, and calculate an inter-branch contrastive loss, an intra-branch contrastive loss and a metric loss. CoLB can improve the capability of the model in adapting to tail classes and assist the imbalanced learning branch to learn a well-represented feature space and discriminative decision boundary. Extensive experiments on three long-tailed benchmark datasets, i.e., CIFAR100-LT, ImageNet-LT and Places-LT, show that our DB-LTR is competitive and superior to the comparative methods.Comment: Published at Neural Network

Localization length exponent in two models of quantum Hall plateau transitions

Motivated by the recent numerical studies on the Chalker-Coddington network model that found a larger-than-expected critical exponent of the localization length characterizing the integer quantum Hall plateau transitions, we revisited the exponent calculation in the continuum model and in the lattice model, both projected to the lowest Landau level or subband. Combining scaling results with or without the corrections of an irrelevant length scale, we obtain $\nu = 2.48 \pm 0.02$, which is larger but still consistent with the earlier results in the two models, unlike what was found recently in the network model. The scaling of the total number of conducting states, as determined by the Chern number calculation, is accompanied by an effective irrelevant length scale exponent $y = 4.3$ in the lattice model, indicating that the irrelevant perturbations are insignificant in the topology number calculation.Comment: 10 pages, 8 figure