The combined effect of foreign direct investment on firm productivity

This paper attempts to answer the economic implications of combining inward foreign direct investment (IFDI) and outward foreign direct investment (OFDI) by constructing a panel fixed effects model using Chinese industrial firm-level data for the period 1998–2013. Specifically, we focus on the impact of combining IFDI and OFDI on firm productivity in China. We also introduce interactive terms into the model to explore the direct and indirect mechanisms through which IFDI and OFDI affect productivity growth. The results show that IFDI and OFDI work together to contribute to productivity growth by acting directly on the level of technology, thereby increasing productivity. IFDI intensifies market concentration, which in turn positively moderates the relationship between OFDI and productivity. Furthermore, IFDI moderates the financing constraints of firms, but has a weaker effect; the easing of financing constraints facilitates the positive impact of OFDI on productivity. Absorptive capacity favours IFDI spillover, but OFDI inhibits absorptive capacity improvements. Our in-depth analysis of the mechanism of the combined impact of IFDI and OFDI on productivity reveals the objectives of using this combination, thereby providing theoretical support and policy recommendations for the implementation of this strategy

Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering

Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question. Existing Retrieval-based approaches first generate instructions from the question and then use them to guide the multi-hop reasoning on the knowledge graph. As the instructions are fixed during the whole reasoning procedure and the knowledge graph is not considered in instruction generation, the model cannot revise its mistake once it predicts an intermediate entity incorrectly. To handle this, we propose KBIGER(Knowledge Base Iterative Instruction GEnerating and Reasoning), a novel and efficient approach to generate the instructions dynamically with the help of reasoning graph. Instead of generating all the instructions before reasoning, we take the (k-1)-th reasoning graph into consideration to build the k-th instruction. In this way, the model could check the prediction from the graph and generate new instructions to revise the incorrect prediction of intermediate entities. We do experiments on two multi-hop KBQA benchmarks and outperform the existing approaches, becoming the new-state-of-the-art. Further experiments show our method does detect the incorrect prediction of intermediate entities and has the ability to revise such errors.Comment: Accepted by NLPCC 2022(oral

Chinese Teachers’ Perceptions on Implementation of CLT in College Business English Class

This qualitative study investigates teachers’ perceptions and challenges of the implementation of Communicative Language Teaching. The participants were nine Business English teachers at a private college in Chengdu, China. The data was collected through semi-structured interviews. The findings revealed that the majority of participants have favourable perceptions of CLT. However, participants mentioned teacher-related challenges, student-related challenges, and policy-related challenges that hinder their implementation of CLT in Business English classes. The findings of this study are beneficial to the field of CLT in China, especially in the English for Specific Purpose context. The recommendations for future studies are discussed

Constructing Carrollian Field Theories from Null Reduction

In this paper, we propose a novel way to construct off-shell actions of $d$-dimensional Carrollian field theories by considering the null-reduction of the Bargmann invariant actions in $d+1$ dimensions. This is based on the fact that $d$-dimensional Carrollian symmetry is the restriction of the $(d+1)$-dimensional Bargmann symmetry to a null hyper-surface. We focus on free scalar field theory and electromagnetic field theory, and show that the electric and magnetic sectors of these theories originate from different Bargmann invariant actions in one higher dimension. In the cases of the massless free scalar field and $d=4$ electromagnetic field, we verify Carrollian conformal invariance of the resulting theories, and find that there appear naturally chain representations and staggered modules of Carrollian conformal algebra.Comment: 59 pages, major revisions, results unchange

CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement

Visual geolocalization is a cost-effective and scalable task that involves matching one or more query images, taken at some unknown location, to a set of geo-tagged reference images. Existing methods, devoted to semantic features representation, evolving towards robustness to a wide variety between query and reference, including illumination and viewpoint changes, as well as scale and seasonal variations. However, practical visual geolocalization approaches need to be robust in appearance changing and extreme viewpoint variation conditions, while providing accurate global location estimates. Therefore, inspired by curriculum design, human learn general knowledge first and then delve into professional expertise. We first recognize semantic scene and then measure geometric structure. Our approach, termed CurriculumLoc, involves a delicate design of multi-stage refinement pipeline and a novel keypoint detection and description with global semantic awareness and local geometric verification. We rerank candidates and solve a particular cross-domain perspective-n-point (PnP) problem based on these keypoints and corresponding descriptors, position refinement occurs incrementally. The extensive experimental results on our collected dataset, TerraTrack and a benchmark dataset, ALTO, demonstrate that our approach results in the aforementioned desirable characteristics of a practical visual geolocalization solution. Additionally, we achieve new high recall@1 scores of 62.6% and 94.5% on ALTO, with two different distances metrics, respectively. Dataset, code and trained models are publicly available on https://github.com/npupilab/CurriculumLoc.Comment: 14 pages, 15 figure