A semiclassical approach to surface Fermi arcs in Weyl semimetals

We present a semiclassical explanation for the morphology of the surface Fermi arcs of Weyl semimetals. Viewing the surface states as a two-dimensional Fermi gas subject to band bending and Berry curvatures, we show that it is the non-parallelism between the velocity and the momentum that gives rise to the spiral structure of Fermi arcs. We map out the Fermi arcs from the velocity field for a single Weyl point and a lattice with two Weyl points. We also investigate the surface magnetoplasma of Dirac semimetals in a magnetic field, and find that the drift motion, the chiral magnetic effect and the Imbert-Fedorov shift are all involved in the formation of surface Fermi arcs. Our work not only provides an insightful perspective on the surface Fermi arcs and a practical way to find the surface dispersion, but also paves the way for the study of other physical properties of the surface states of topological semimetals, such as transport properties and orbital magnetization, using semiclassical methods.Comment: 6 pages, 4 figures + Supplemental Material

Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation

Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. Previous MMT systems mainly focus on better access and use of visual information and tend to validate their methods on image-related datasets. These studies face two challenges. First, they can only utilize triple data (bilingual texts with images), which is scarce; second, current benchmarks are relatively restricted and do not correspond to realistic scenarios. Therefore, this paper correspondingly establishes new methods and new datasets for MMT. First, we propose a framework 2/3-Triplet with two new approaches to enhance MMT by utilizing large-scale non-triple data: monolingual image-text data and parallel text-only data. Second, we construct an English-Chinese {e}-commercial {m}ulti{m}odal {t}ranslation dataset (including training and testing), named EMMT, where its test set is carefully selected as some words are ambiguous and shall be translated mistakenly without the help of images. Experiments show that our method is more suitable for real-world scenarios and can significantly improve translation performance by using more non-triple data. In addition, our model also rivals various SOTA models in conventional multimodal translation benchmarks.Comment: 8 pages, ACL 2023 Findin

BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation

We present a large-scale video subtitle translation dataset, BigVideo, to facilitate the study of multi-modality machine translation. Compared with the widely used How2 and VaTeX datasets, BigVideo is more than 10 times larger, consisting of 4.5 million sentence pairs and 9,981 hours of videos. We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation. To better model the common semantics shared across texts and videos, we introduce a contrastive learning method in the cross-modal encoder. Extensive experiments on the BigVideo show that: a) Visual information consistently improves the NMT model in terms of BLEU, BLEURT, and COMET on both Ambiguous and Unambiguous test sets. b) Visual information helps disambiguation, compared to the strong text baseline on terminology-targeted scores and human evaluation. Dataset and our implementations are available at https://github.com/DeepLearnXMU/BigVideo-VMT.Comment: Accepted to ACL 2023 Finding

Numerical Simulation Study on the Flow and Heat Transfer Characteristics of Subcooled N-Heptane Flow Boiling in a Vertical Pipe under External Radiation

In the top submerged lance (TSL) smelting process, flow boiling may occur in the lance’s inner pipe due to the heat coming from the furnace when liquid fuel is adopted. In the current study, a numerical simulation was carried out by coupling the Eulerian two-fluid model with the improved RPI wall boiling model to investigate the subcooled n-heptane flow boiling in the inner pipe. The effects of inlet velocity and pipe wall emissivity on two-phase flow and heat transfer are elucidated. The results show that, for pipes with inlet velocity ranging from 0.3 m·s−1 to 1.0 m·s−1, an increase in inlet velocity leads to a lower void fraction near the outlet, as well as a lower average velocity and a lower average temperature of each phase. Meanwhile, the Onset of Nucleate Boiling (ONB) position approaches to the outlet, and the total pressure drop of the entire pipe reduces when the inlet velocity increases. However, the opposite trends appear when increasing the pipe wall emissivity. The maximum wall temperature corresponding to the critical heat flux (CHF) point is slightly affected by inlet velocity but significantly affected by pipe wall emissivity. The non-equilibrium effect and the specific components of pressure drop are also further investigated

Numerical Simulation Study on the Flow and Heat Transfer Characteristics of Subcooled N-Heptane Flow Boiling in a Vertical Pipe under External Radiation

In the top submerged lance (TSL) smelting process, flow boiling may occur in the lance’s inner pipe due to the heat coming from the furnace when liquid fuel is adopted. In the current study, a numerical simulation was carried out by coupling the Eulerian two-fluid model with the improved RPI wall boiling model to investigate the subcooled n-heptane flow boiling in the inner pipe. The effects of inlet velocity and pipe wall emissivity on two-phase flow and heat transfer are elucidated. The results show that, for pipes with inlet velocity ranging from 0.3 m·s−1 to 1.0 m·s−1, an increase in inlet velocity leads to a lower void fraction near the outlet, as well as a lower average velocity and a lower average temperature of each phase. Meanwhile, the Onset of Nucleate Boiling (ONB) position approaches to the outlet, and the total pressure drop of the entire pipe reduces when the inlet velocity increases. However, the opposite trends appear when increasing the pipe wall emissivity. The maximum wall temperature corresponding to the critical heat flux (CHF) point is slightly affected by inlet velocity but significantly affected by pipe wall emissivity. The non-equilibrium effect and the specific components of pressure drop are also further investigated

High Fischer ratio oligopeptides in food: sources, functions and application prospects

ABSTRACT: High Fischer ratio oligopeptides (HFROs) are a group of oligopeptides containing high levels of branched-chain amino acids (BCAA) and low levels of aromatic amino acids (AAA). HFROs have received a lot of attention as they are believed to have significant physiological activities, including antioxidant, liver damage repair, anti-fatigue, anti-tumor and energy supply to the body. HFROs are available from a wide range of sources and both plant and animal proteins can be used to prepare HFROs but the physiological tolerability and rejection of special populations needs to be considered. Enzymatic hydrolysis is the most common method for the preparation of HFROs, but optimization of the separation and purification process is still needed in the future. Diseases caused by disruptions in the balance of BCAA and AAA in the blood, such as hepatic encephalopathy, can be treated by supplementing HFROs with drugs or food. In addition, HFROs are able to reduce fatigue feedback and assist in the treatment of phenylketonuria at the molecular nutrient level. The aim of this review is to review recent research on HFROs and provide new perspectives on the high value use of crops and the development of novel functional and special medical purpose foods

Effects of Copper Pollution on the Phenolic Compound Content, Color, and Antioxidant Activity of Wine

The effects of copper pollution on the polyphenol content, color, and antioxidant activity of wine, as well as correlations among these factors, were investigated. Copper had clear influences on wine polyphenol content. At low copper concentrations, the concentrations of nearly all polyphenols increased, and the antioxidant activity values of the wine also increased. When the copper concentration reached the lowest level of the medium copper range (9.6~16 mg/L), most of the indices also improved. When the copper concentrations reached the latter part of the medium copper range (19.2 and 22.4 mg/L), many of the tested indices began to decrease. Furthermore, when the copper concentration reached the high ranges (32, 64, and 96 mg/L), the polyphenol content, CIELAB color parameters, and antioxidant activity of wine were substantially decreased, indicating the need to control increasing copper content in grape must

Rootstock–scion interaction affects Malus transcriptome profiles in response to cadmium

Abstract Apple production is threatened by cadmium contamination in orchards. Cd accumulation and tolerance in grafted Malus plants is affected by rootstock, scion, and their interaction. This dataset is part of an experiment investigating the molecular mechanism of Cd bioaccumulation and tolerance in different apple rootstock-scion combinations. We exposed four rootstock–scion combinations to Cd treatment consisting of Hanfu and Fuji apple (Malus domestica) scions grafted onto apple rootstocks of M. baccata or M. micromalus “qingzhoulinqin”. RNA sequencing was conducted in roots and leaves of grafting combinations under 0 or 50 μM CdCl2 conditions. A comprehensive transcriptional dataset of affected rootstock, scion, and their interaction among different graft combinations was obtained. This dataset provides new insights in the transcriptional control of Cd bioaccumulation and tolerance in grafting plants regulated by rootstock and scion. Herein, we discuss the molecular mechanism underlying Cd absorption and bioaccumulation

Jointly learning topics in sentence embedding for document summarization

Summarization systems for various applications, such as opinion mining, online news services, and answering questions, have attracted increasing attention in recent years. These tasks are complicated, and a classic representation using bag-of-words does not adequately meet the comprehensive needs of applications that rely on sentence extraction. In this paper, we focus on representing sentences as continuous vectors as a basis for measuring relevance between user needs and candidate sentences in source documents. Embedding models based on distributed vector representations are often used in the summarization community because, through cosine similarity, they simplify sentence relevance when comparing two sentences or a sentence/query and a document. However, the vector-based embedding models do not typically account for the salience of a sentence, and this is a very necessary part of document summarization. To incorporate sentence salience, we developed a model, called CCTSenEmb, that learns latent discriminative Gaussian topics in the embedding space and extended the new framework by seamlessly incorporating both topic and sentence embedding into one summarization system. To facilitate the semantic coherence between sentences in the framework of prediction-based tasks for sentence embedding, the CCTSenEmb further considers the associations between neighboring sentences. As a result, this novel sentence embedding framework combines sentence representations, word-based content, and topic assignments to predict the representation of the next sentence. A series of experiments with the DUC datasets validate CCTSenEmb's efficacy in document summarization in a query-focused extraction-based setting and an unsupervised ILP-based setting