Complex Landau levels and related transport properties in the strained zigzag graphene nanoribbons

The real magnetic fields (MFs) acting on the graphene can induce flat real Landau levels (LLs). As an analogy, strains in graphene can produce significant pseudo MFs, triggering the appearance of dispersive pseudo LLs. By analysing the low energy effective Hamiltonian, we introduce the concept of the effective orbital MFs to integrate the real MFs and pseudo MFs. Accordingly, we obtain the complex LLs which incorporate the real LLs and pseudo LLs, and calculate the related transport properties. With the aid of these ideas, we reveal the mechanism underlying the fragility of the pseudo LLs against disorders, and predict that the $K$ and $K'$ valleys have different robust performances against the Anderson disorders and dephasing effects. Furthermore, the tunability of the polarized valley currents is also studied, opening up new possibilities for the design of valleytronics devices

Omnia Juncta in Uno*: foreign powers and trademark protection in Shanghai's concession era

We investigate how firms and markets adapt to trademark protection, an extensively used but under-examined form of IP protection to address asymmetric information, by exploring a historical precedent: China's trademark law of 1923. Exploiting unique, newly digitized firm-employee and firm-agent datasets from Shanghai in 1872-1941, we show that the trademark law, established as an unanticipated and Western-disapproved response to end foreign privileges in China, shaped firm dynamics and relationships on all sides of trade-mark conflicts. Western firms with greater dependence on trademark protection grew and raised brand investment, while Japanese businesses, most frequently accused of counterfeiting, contracted despite attempts to build their own brands. The trademark law also fostered relationships with domestic intermediaries, both within and outside the boundaries of Western firms, and the growth of the Chinese intermediary sector. At the market level, the trademark law did not reduce competition or raise brand prices, leading to a coexistence of trademarks and competitive markets and ultimately gains in consumer welfare. A comparison with previous attempts by foreign powers-such as extraterritorial rights and bilateral treaties-shows that the alternative institutions were broadly unsuccessful. *Omnia Juncta in Uno ("All Joined in One") was the Latin motto on the municipal seal of the Shanghai International Settlement (1843-1941) and signified the joint governance of foreign powers in the settlement

MiLMo:Minority Multilingual Pre-trained Language Model

Pre-trained language models are trained on large-scale unsupervised data, and they can fine-turn the model only on small-scale labeled datasets, and achieve good results. Multilingual pre-trained language models can be trained on multiple languages, and the model can understand multiple languages at the same time. At present, the search on pre-trained models mainly focuses on rich resources, while there is relatively little research on low-resource languages such as minority languages, and the public multilingual pre-trained language model can not work well for minority languages. Therefore, this paper constructs a multilingual pre-trained model named MiLMo that performs better on minority language tasks, including Mongolian, Tibetan, Uyghur, Kazakh and Korean. To solve the problem of scarcity of datasets on minority languages and verify the effectiveness of the MiLMo model, this paper constructs a minority multilingual text classification dataset named MiTC, and trains a word2vec model for each language. By comparing the word2vec model and the pre-trained model in the text classification task, this paper provides an optimal scheme for the downstream task research of minority languages. The final experimental results show that the performance of the pre-trained model is better than that of the word2vec model, and it has achieved the best results in minority multilingual text classification. The multilingual pre-trained model MiLMo, multilingual word2vec model and multilingual text classification dataset MiTC are published on http://milmo.cmli-nlp.com/

Reverse strain-induced snake states in graphene nanoribbons

Strain can tailor the band structures and properties of graphene nanoribbons (GNRs) with the well-known emergent pseudo-magnetic fields and the corresponding pseudo-Landau levels (pLLs). We design one type of the zigzag GNR (ZGNR) with reverse strains, producing pseudo-magnetic fields with opposite signs in the lower and upper half planes. Therefore, electrons propagate along the interface as "snake states", experiencing opposite Lorentz forces as they cross the zero field border line. By using the Landauer-Buttiker formalism combined with the nonequilibrium Green's function method, the existence and robustness of the reverse strain-induced snake states are further studied. Furthermore, the realization of long-thought pure valley currents in monolayer graphene systems is also proposed in our device.Comment: 6 figure

Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks

Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the input, concatenating multi-modality features inside models, or applying attention to each data modality. These fusion strategies are straightforward yet insufficient. In this paper, we propose a novel fusion method named Explicit Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of data. Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features. EAEF uses one branch to enhance feature extraction for i) and iii) and the other branch to remedy insufficient representations for ii). The outputs of two branches are fused to form complementary features. As a result, the proposed fusion method outperforms state-of-the-art by 1.6\% in mIoU on semantic segmentation, 3.1\% in MAE on salient object detection, 2.3\% in mAP on object detection, and 8.1\% in MAE on crowd counting. The code is available at https://github.com/FreeformRobotics/EAEFNet

PATS: Patch Area Transportation with Subdivision for Local Feature Matching

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detectorfree methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code will be released to benefit the community.Comment: Project page: https://zju3dv.github.io/pat

Inflammation mobilizes copper metabolism to promote colon tumorigenesis via an IL-17-STEAP4-XIAP axis.

Copper levels are known to be elevated in inflamed and malignant tissues. But the mechanism underlying this selective enrichment has been elusive. In this study, we report a axis by which inflammatory cytokines, such as IL-17, drive cellular copper uptake via the induction of a metalloreductase, STEAP4. IL-17-induced elevated intracellular copper level leads to the activation of an E3-ligase, XIAP, which potentiates IL-17-induced NFκB activation and suppresses the caspase 3 activity. Importantly, this IL-17-induced STEAP4-dependent cellular copper uptake is critical for colon tumor formation in a murine model of colitis-associated tumorigenesis and STEAP4 expression correlates with IL-17 level and XIAP activation in human colon cancer. In summary, this study reveals a IL-17-STEAP4-XIAP axis through which the inflammatory response induces copper uptake, promoting colon tumorigenesis