Do foreign institutional investors drive corporate social responsibility? Evidence from listed firms in China

This paper investigates the effect of qualified foreign institutional investors (QFIIs) on corporate social responsibility (CSR) within the context of listed firms in China. We find that QFIIs offer an incisive channel for improving socially responsible practices. In addition, we find that firms with QFIIs are more likely to comply with the Global Reporting Initiative (GRI) guidelines, and that their sustainability reports tend to be longer. We also find that this positive effect is more pronounced in firms with low initial CSR scores than those with high CSR scores at the time when QFIIs enter the sample. Our empirical evidence further confirms that this positive impact is driven by QFIIs from countries with high social awareness, or QFIIs from geographically distant countries, consistent with their motives, and is linked to the ownership of QFIIs, especially when the QFII is among the top ten of the largest shareholders. Finally, our extended analysis reveals that the increase in CSR performance associated with the presence of QFIIs results in greater firm performance and easier access to finance

Fractional strong matching preclusion for two variants of hypercubes

Let F be a subset of edges and vertices of a graph G. If G-F has no fractional perfect matching, then F is a fractional strong matching preclusion set of G. The fractional strong matching preclusion number is the cardinality of a minimum fractional strong matching preclusion set. In this paper, we mainly study the fractional strong matching preclusion problem for two variants of hypercubes, the multiply twisted cube and the locally twisted cube, which are two of the most popular interconnection networks. In addition, we classify all the optimal fractional strong matching preclusion set of each

Integrating non-planar metamaterials with magnetic absorbing materials to yield ultra-broadband microwave hybrid absorbers

Broadening the bandwidth of electromagnetic wave absorbers has greatly challenged material scientists. Here, we propose a two-layer hybrid absorber consisting of a non-planar metamaterial (MM) and a magnetic microwave absorbing material (MAM). The non-planar MM using magnetic MAMs instead of dielectric substrates shows good low frequency absorption and low reflection across a broad spectrum. Benefiting from this and the high frequency strong absorption of the MAM layer, the lightweight hybrid absorber exhibits 90% absorptivity over the whole 2-18 GHz range. Our result reveals a promising and flexible method to greatly extend or control the absorption bandwidth of absorbers. (C) 2014 AIP Publishing LLC

Case report: Traumatic lumbosacral spondyloptosis with locked L5 inferior articular process

BackgroundTraumatic lumbosacral spondyloptosis is a very rare spinal disease caused by high-energy trauma. We report a case of traumatic lumbosacral spondyloptosis with locked L5 inferior articular process.Case presentationA 33-year-old man presented with multisite pain for 6 h following waist trauma and was admitted to the hospital. He suffered multiple injuries from severe impact on the waist after driving an out of control forklift truck. Preoperative imaging examinations revealed that the patient was diagnosed with traumatic lumbosacral spondyloptosis and the L5 inferior articular process was locked into the anterior margin of the S1 vertebra. A posterior instrumentation, decompression of the cauda equina, and interbody fusion procedure was performed. The patient received hyperbaric oxygen and rehabilitation treatment 10 days after the surgery. At the 6-month postoperative follow-up, the muscle strength of the lower limbs was improved, the patient had no numbness of both lower limbs, and the urinary retention symptom was significantly improved. The American Spinal Injury Association grade improved from grade C preoperatively to grade D postoperatively. As far as we know, there have been no relevant reports on traumatic lumbosacral spondyloptosis with locked L5 inferior articular process yet.ConclusionWe believe that the hyperflexion and shear forces were the potential causes of this injury. In addition, the preoperative imaging examinations should be evaluated carefully. If the inferior articular process of L5 were locked, we suggest removing the bilateral inferior articular processes first and then perform reduction

Progressive Scene Text Erasing with Self-Supervision

Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data. Although data synthetic engines can provide vast amounts of annotated training samples, there are differences between synthetic and real-world data. In this paper, we employ self-supervision for feature representation on unlabeled real-world scene text images. A novel pretext task is designed to keep consistent among text stroke masks of image variants. We design the Progressive Erasing Network in order to remove residual texts. The scene text is erased progressively by leveraging the intermediate generated results which provide the foundation for subsequent higher quality results. Experiments show that our method significantly improves the generalization of the text erasing task and achieves state-of-the-art performance on public benchmarks

Correlation between vitamin D levels and blood pressure in elderly hypertensive patients with osteoporosis

ObjectivesThe association between vitamin D and blood pressure in elderly patients with hypertension complicated by osteoporosis remains unclear. The objective of this study is to explore whether vitamin D deficiency contributes to elevated blood pressure in elderly individuals with both hypertension and osteoporosis.MethodsThis study represents a single-center retrospective observational investigation carried out at the Zhongshan Hospital Affiliated to Xiamen University. Ambulatory blood pressure, bone density, vitamin D levels, and additional laboratory parameters were collected upon admission. The association between vitamin D and ambulatory blood pressure outcomes was assessed using Spearman correlation tests and partial correlation analyses. The relationship between vitamin D and changes in blood pressure was analyzed through Generalized Additive Models, and threshold analysis was conducted to explore potential thresholds.Results139 patients with newly diagnosed osteoporosis were consecutively included (mean age 73 years, 84.9% female). There is a negative correlation between 25-(OH) D3 and 24 h mean systolic blood pressure (mSBP), diurnal mSBP, nocturnal mSBP, maximum SBP, respectively. The results of the generalized additive model analysis show that there is a nonlinear relationship between 25-(OH) D3 and 24 h mSBP, diurnal mSBP, nocturnal mSBP, respectively. After determining the critical point of 25-(OH) D3 as 42 nmol/L, a segmented linear regression model was used to calculate the effect size and 95% confidence interval on both sides of the critical point. When 25-(OH) D3 is ≤42 nmol/L, it significantly negatively correlates with 24 h, diurnal, and nocturnal mean SBP. Conversely, when 25-(OH) D3 exceeds 42 nmol/L, there is no statistically significant association with 24 h, diurnal, or nocturnal mSBP.ConclusionThere was a significant negative correlation between vitamin D levels and blood pressure levels in elderly patients with hypertension and osteoporosis

Tailoring Personality Traits in Large Language Models via Unsupervisedly-Built Personalized Lexicons

Personality plays a pivotal role in shaping human expression patterns, thus regulating the personality of large language models (LLMs) holds significant potential in enhancing the user experience of LLMs. Previous methods either relied on fine-tuning LLMs on specific corpora or necessitated manually crafted prompts to elicit specific personalities from LLMs. However, the former approach is inefficient and costly, while the latter cannot precisely manipulate personality traits at a fine-grained level. To address the above challenges, we have employed a novel Unsupervisedly-Built Personalized Lexicons (UBPL) in a pluggable manner during the decoding phase of LLMs to manipulate their personality traits. UBPL is a lexicon built through an unsupervised approach from a situational judgment test dataset (SJTs4LLM). Users can utilize UBPL to adjust the probability vectors of predicted words in the decoding phase of LLMs, thus influencing the personality expression of LLMs. Extensive experimentation demonstrates the remarkable effectiveness and pluggability of our method for fine-grained manipulation of LLM's personality.Comment: Work in progres

More ConvNets in the 2020s:Scaling up Kernels Beyond 51x51 using Sparsity

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local-window attention mechanism, showing appealing performance and efficiency. While one of them, i.e. RepLKNet, impressively manages to scale the kernel size to 31x31 with improved performance, the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of advanced ViTs such as Swin Transformer. In this paper, we explore the possibility of training extreme convolutions larger than 31x31 and test whether the performance gap can be eliminated by strategically enlarging convolutions. This study ends up with a recipe for applying extremely large kernels from the perspective of sparsity, which can smoothly scale up kernels to 61x61 with better performance. Built on this recipe, we propose Sparse Large Kernel Network (SLaK), a pure CNN architecture equipped with sparse factorized 51x51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as a wide range of downstream tasks including semantic segmentation on ADE20K, object detection on PASCAL VOC 2007, and object detection/segmentation on MS COCO