Interlayer-spin-interaction-driven Sliding Ferroelectricity in a van der Waals Magnetic Heterobilayer

Sliding ferroelectricity is widely existed in van der Waals (vdW) two-dimensional (2D) multilayers, exhibiting great potential on low-dissipation non-volatile memories. However, in a vdW heterostructure, interlayer sliding usually fails to reverse or distinctly change the electric polarization, which makes the electrical control difficult in practice. Here we propose that in a vdW magnetic system, the interlayer spin interaction could provide an extra degree-of-freedom to remarkably tune the electric polarization. Combining tight-binding model analysis and first-principles calculations, we show that in the CrI3/MnSe2 and other vdW magnetic heterobilayers, the switching of the interlayer magnetic order can greatly change, even reverse the off-plane electronic polarization. Furthermore, interlayer sliding causes a non-volatile switching of the magnetic order and, thus, reverses the electric polarization, suggesting a non-volatile magnetoelectric coupling effect. These findings will significantly advances the development of 2D ferroelectrics and multiferroics for spintronic applications

Bibliometric analysis of social commerce research

Recently, social commerce has attracted the attention from both academics and practitioners and became a significant emerging research area. In this paper, bibliometric analysis has been applied to identify the characteristics and the developments of social commerce research. Based on the definition, we conduct a systematic review of social commerce research by synthesizing 1900 publications published between 2003 and 2018 in Web of Science. The 1900 publications cover 4033 authors, 724 journals, 79 countries or territories, and 1648 institutions. Furthermore,‘Computers in Human Behavior’ is the key journal publishing on social commerce research, and the USA, China and England are the countries that dominate the publication production. It can be concluded that there is much collaborative research in the social commerce domain as multi-authored publications make up the majority of all publications. In addition, three main research areas can be distinguished based on LLR (log-likelihood ratio): (1) the development trend of social commerce, (2) the relationship between customers and vendors, and (3) consumer trust in the context of social shopping. We believe that this review can provide some guidelines for future research

Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

With the rapid development of Web3D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks (CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3D furniture, and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches

Offsetting disagreement and security prices

We propose that investor beliefs frequently “cross” in the sense that an investor may like company A but dislike company B, whereas another investor may like company B but dislike company A. Such belief-crossing makes it almost impossible to construct a portfolio that is composed solely of every investor’s most favored companies. This causes the level of excitement for portfolios to be generally lower than the levels of excitement that individual companies generate among their most fervent supporters. Coupled with short-sale constraints, wherein prices are set by the most optimistic investors, this causes portfolios to trade at discounts. Utilizing several settings whereby the value of a portfolio and the values of the underlying components can be evaluated separately (e.g., closed-end funds), we present evidence supporting our proposition that, in financial markets, the “whole” is often less than the “sum of its parts.

Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyrics translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of augmented data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluation.Comment: 13 page

Unveiling the Siren's Song: Towards Reliable Fact-Conflicting Hallucination Detection

Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread attention owing to their myriad of practical applications, yet their adoption has been constrained by issues of fact-conflicting hallucinations across web platforms. The assessment of factuality in text, produced by LLMs, remains inadequately explored, extending not only to the judgment of vanilla facts but also encompassing the evaluation of factual errors emerging in complex inferential tasks like multi-hop, and etc. In response, we introduce FactCHD, a fact-conflicting hallucination detection benchmark meticulously designed for LLMs. Functioning as a pivotal tool in evaluating factuality within "Query-Respons" contexts, our benchmark assimilates a large-scale dataset, encapsulating a broad spectrum of factuality patterns, such as vanilla, multi-hops, comparison, and set-operation patterns. A distinctive feature of our benchmark is its incorporation of fact-based chains of evidence, thereby facilitating comprehensive and conducive factual reasoning throughout the assessment process. We evaluate multiple LLMs, demonstrating the effectiveness of the benchmark and current methods fall short of faithfully detecting factual errors. Furthermore, we present TRUTH-TRIANGULATOR that synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming to yield more credible detection through the amalgamation of predictive results and evidence. The benchmark dataset and source code will be made available in https://github.com/zjunlp/FactCHD.Comment: Work in progres

Disorder and diffuse scattering in single-chirality (TaSe4_4)2_2I crystals

The quasi-one-dimensional chiral compound (TaSe$_4$)$_2$I has been extensively studied as a prime example of a topological Weyl semimetal. Upon crossing its phase transition temperature $T_\textrm{CDW}$ $\approx$ 263 K, (TaSe$_4$)$_2$I exhibits incommensurate charge density wave (CDW) modulations described by the well-defined propagation vector $\sim$(0.05, 0.05, 0.11), oblique to the TaSe$_4$ chains. Although optical and transport properties greatly depend on chirality, there is no systematic report about chiral domain size for (TaSe$_4$)$_2$I. In this study, our single-crystal scattering refinements reveal a bulk iodine deficiency, and Flack parameter measurements on multiple crystals demonstrate that separate (TaSe$_4$)$_2$I crystals have uniform handedness, supported by direct imaging and helicity dependent THz emission spectroscopy. Our single-crystal X-ray scattering and calculated diffraction patterns identify multiple diffuse features and create a real-space picture of the temperature-dependent (TaSe$_4$)$_2$I crystal structure. The short-range diffuse features are present at room temperature and decrease in intensity as the CDW modulation develops. These transverse displacements, along with electron pinning from the iodine deficiency, help explain why (TaSe$_4$)$_2$I behaves as an electronic semiconductor at temperatures above and below $T_\textrm{CDW}$, despite a metallic band structure calculated from density functional theory of the ideal structure.Comment: 24 pages, 20 figures, 3 table