Foreign Investment Companies Limited by Shares: The Latest Chinese Organization for Major International Ventures

Foreign Investment Companies Limited By Shares ( FICLBS ) is one of the most important recent innovations in the People\u27s Republic of China\u27s ( China ) foreign-invested enterprises law. Since January 10, 1995, China has authorized use of the FICLBS and, for the first time, it more closely resembles major corporate organizations used by international foreign investors.! So far over eighteen FICLBS have been approved for operation through 1999, with combined actual foreign investment of USD 1.2 billion.2 FICLBS has the potential to be the ideal organization for major international investment in China after it joins the World Trade Organization. The FICLBS can issue freely transferred or traded public stock both inside and outside of China. FICLBS regulations encourape establishment of technologically advanced production-type companies. Although the statutes that regulate the FICLBS are entirely new to China\u27s body of civil law, they have similar characteristics to company laws of industrialized countries. That is not to say such statutes would result in similar outcomes in regulating the FICLBS compared to company laws of industrialized countries that regulate their respective public stock companies. Our analyses of the FICLBS reveal significant varying interpretations of particular regulations by different Chinese agencies, due to its legal characterization as a special kind of Foreign-Invested Enterprise with various novel categories of stock shares. FICLBS has genuine progressive features but they need to be improved and refined to attract more foreign investors to use this relatively new legal enterprise form

Third-Party Aligner for Neural Word Alignments

Word alignment is to find translationally equivalent words between source and target sentences. Previous work has demonstrated that self-training can achieve competitive word alignment results. In this paper, we propose to use word alignments generated by a third-party word aligner to supervise the neural word alignment training. Specifically, source word and target word of each word pair aligned by the third-party aligner are trained to be close neighbors to each other in the contextualized embedding space when fine-tuning a pre-trained cross-lingual language model. Experiments on the benchmarks of various language pairs show that our approach can surprisingly do self-correction over the third-party supervision by finding more accurate word alignments and deleting wrong word alignments, leading to better performance than various third-party word aligners, including the currently best one. When we integrate all supervisions from various third-party aligners, we achieve state-of-the-art word alignment performances, with averagely more than two points lower alignment error rates than the best third-party aligner. We released our code at https://github.com/sdongchuanqi/Third-Party-Supervised-Aligner.Comment: 12 pages, 4 figures, findings of emnlp 202

2-Amino-4-(2-chloro­phen­yl)-5,10-dioxo-5,10-dihydro-4H-benzo[g]chromene-3-carbonitrile

In the mol­ecule of the title compound, C20H11ClN2O3, the pyran ring adopts a flattened-boat conformation. In the crystal structure, inter­molecular N—H⋯N and N—H⋯O hydrogen bonds generate edge-fused R 2 2(12) and R 2 2(14) ring motifs; the hydrogen-bonded motifs are linked to each other, forming a three-dimensional network. A π–π contact [centroid-to-centroid distance = 3.879 (3) Å] between the chloro­phenyl rings may further stabilize the structure

U-shaped fusion convolutional transformer based workflow for fast optical coherence tomography angiography generation in lips

Oral disorders, including oral cancer, pose substantial diagnostic challenges due to late-stage diagnosis, invasive biopsy procedures, and the limitations of existing non-invasive imaging techniques. Optical coherence tomography angiography (OCTA) shows potential in delivering non-invasive, real-time, high-resolution vasculature images. However, the quality of OCTA images are often compromised due to motion artifacts and noise, necessitating more robust and reliable image reconstruction approaches. To address these issues, we propose a novel model, a U-shaped fusion convolutional transformer (UFCT), for the reconstruction of high-quality, low-noise OCTA images from two-repeated OCT scans. UFCT integrates the strengths of convolutional neural networks (CNNs) and transformers, proficiently capturing both local and global image features. According to the qualitative and quantitative analysis in normal and pathological conditions, the performance of the proposed pipeline outperforms that of the traditional OCTA generation methods when only two repeated B-scans are performed. We further provide a comparative study with various CNN and transformer models and conduct ablation studies to validate the effectiveness of our proposed strategies. Based on the results, the UFCT model holds the potential to significantly enhance clinical workflow in oral medicine by facilitating early detection, reducing the need for invasive procedures, and improving overall patient outcomes.</p

U-shaped fusion convolutional transformer based workflow for fast optical coherence tomography angiography generation in lips

Oral disorders, including oral cancer, pose substantial diagnostic challenges due to late-stage diagnosis, invasive biopsy procedures, and the limitations of existing non-invasive imaging techniques. Optical coherence tomography angiography (OCTA) shows potential in delivering non-invasive, real-time, high-resolution vasculature images. However, the quality of OCTA images are often compromised due to motion artifacts and noise, necessitating more robust and reliable image reconstruction approaches. To address these issues, we propose a novel model, a U-shaped fusion convolutional transformer (UFCT), for the reconstruction of high-quality, low-noise OCTA images from two-repeated OCT scans. UFCT integrates the strengths of convolutional neural networks (CNNs) and transformers, proficiently capturing both local and global image features. According to the qualitative and quantitative analysis in normal and pathological conditions, the performance of the proposed pipeline outperforms that of the traditional OCTA generation methods when only two repeated B-scans are performed. We further provide a comparative study with various CNN and transformer models and conduct ablation studies to validate the effectiveness of our proposed strategies. Based on the results, the UFCT model holds the potential to significantly enhance clinical workflow in oral medicine by facilitating early detection, reducing the need for invasive procedures, and improving overall patient outcomes.</p