Determinants of capital structure: Evidence from UK listed firm panel data

This paper follows many previous empirical researches, identifying and finding the determinants of capital structure of UK-listed firms. It chooses indicators for each explanatory variable and apply regression model to obtain the qualitative results, and then analyse them. Each research always gets its own results. This paper tends to find its results in the recent years and compare it with previous paper. 200 UK-listed companies in the period of 2001 to 2011 are investigated. Capital structure of a firm is computed in two ways, long-term debt ratio and short-term debt ratio. An OLS regression is used. The results observed show that growth opportunities variable is the most significant determinant of capital structure, followed be volatility and uniqueness. Profitability, size, non-debt tax shields and tangibility are not significant in all the models. Key words: capital structure; leverage; firm characteristics

On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions

As Federated Learning (FL) has gained increasing attention, it has become widely acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the overall framework when learning over a sequence of tasks results in the phenomenon known as ``catastrophic forgetting''. Consequently, much FL research has centered on devising federated increasing learning methods to alleviate forgetting while augmenting knowledge. On the other hand, forgetting is not always detrimental. The selective amnesia, also known as federated unlearning, which entails the elimination of specific knowledge, can address privacy concerns and create additional ``space'' for acquiring new knowledge. However, there is a scarcity of extensive surveys that encompass recent advancements and provide a thorough examination of this issue. In this manuscript, we present an extensive survey on the topic of knowledge editing (augmentation/removal) in Federated Learning, with the goal of summarizing the state-of-the-art research and expanding the perspective for various domains. Initially, we introduce an integrated paradigm, referred to as Federated Editable Learning (FEL), by reevaluating the entire lifecycle of FL. Secondly, we provide a comprehensive overview of existing methods, evaluate their position within the proposed paradigm, and emphasize the current challenges they face. Lastly, we explore potential avenues for future research and identify unresolved issues.Comment: 7 pages, 1 figure, 2 tabel

Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models

The demand for psychological counseling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which has heightened the need for timely and professional mental health support. Online psychological counseling has emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based system leveraging Large Language Models (LLMs) for question-answering in online psychological consultation. Our framework combines pre-trained LLMs with real-world professional Q&A from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, with human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article concludes by discussing the potential of large language models to enhance mental health support through AI technologies in online psychological consultation

AST:Adaptive Self-supervised Transformer for optical remote sensing representation

Due to the variation in spatial resolution and the diversity of object scales, the interpretation of optical remote sensing images is extremely challenging. Deep learning has become the mainstream solution to interpret such complex scenes. However, the explosion of deep learning model architectures has resulted in the need for hundreds of millions of remote sensing images for which labels are very costly or often unavailable publicly. This paper provides an in-depth analysis of the main reasons for this data thirst, i.e., (i) limited representational power for model learning, and (ii) underutilization of unlabeled remote sensing data. To overcome the above difficulties, we present a scalable and adaptive self-supervised Transformer (AST) for optical remote sensing image interpretation. By performing masked image modeling in pre-training, the proposed AST releases the rich supervision signals in massive unlabeled remote sensing data and learns useful multi-scale semantics. Specifically, a cross-scale Transformer architecture is designed to collaboratively learn global dependencies and local details by introducing a pyramid structure, to facilitate multi-granular feature interactions and generate scale-invariant representations. Furthermore, a masking token strategy relying on correlation mapping is proposed to achieve adaptive masking of partial patches without affecting key structures, which enhances the understanding of visually important regions. Extensive experiments on various optical remote sensing interpretation tasks show that AST has good generalization capability and competitiveness.</p

Universal Actuation Module and Kinematic Model for Heart Valve Interventional Catheter Robotization

Catheters have been widely used to deal with heart valve diseases. However, the diversity in handle structures and bending curvatures imposes significant complexities in safe delivery and positioning. In this letter, we designed a module for single knob actuation assembled coaxially on the catheter handle, composed of a chuck for universal clamping of diameters from 15 to 45 mm and a position-adjustable shaft to accommodate various spacing between knobs. In addition, we proposed a two-curvature with pseudo joints (TC-PJ) model for bending control of bendable sections (BSs) in catheters. The verification was decoupled into two steps based on the other three deformation patterns. Firstly, comparing the two-curvature (TC) model with pseudo-rigid-body (PRB), constant curvature (CC), and Euler spiral (ES) models to simulate planar bending and elongation, the results showed a more accurate shape representation. Then, five distinct catheters were employed to test the clamping universality of the module and tip positioning precision of the TC-PJ model which took torsion and shear strain into consideration. The root-mean-square error (RMSE) and the standard deviation (SD) of tip position and direction were analysed. Results indicated the module's suitability for clamping these catheters, with the large guide sheath exhibiting minimal position RMSE (SD) of around 0.10 (0.051) mm and 0.049 (2.15) degrees, while the puncture catheter demonstrated the highest position and direction RMSE (SD) extending to about 1.16 (0.53) mm and 0.70 (31.33) degrees, primarily attributed to the coupling of two sequential bendable components. Overall, the proposed actuation module and kinematic model showed the ability of universal manipulation and an average tip position and direction RMSE of 0.65 mm and 0.23 degrees in free space.</p

Training and Serving System of Foundation Models: A Comprehensive Survey

Foundation models (e.g., ChatGPT, DALL-E, PengCheng Mind, PanGu-$\Sigma$) have demonstrated extraordinary performance in key technological areas, such as natural language processing and visual recognition, and have become the mainstream trend of artificial general intelligence. This has led more and more major technology giants to dedicate significant human and financial resources to actively develop their foundation model systems, which drives continuous growth of these models' parameters. As a result, the training and serving of these models have posed significant challenges, including substantial computing power, memory consumption, bandwidth demands, etc. Therefore, employing efficient training and serving strategies becomes particularly crucial. Many researchers have actively explored and proposed effective methods. So, a comprehensive survey of them is essential for system developers and researchers. This paper extensively explores the methods employed in training and serving foundation models from various perspectives. It provides a detailed categorization of these state-of-the-art methods, including finer aspects such as network, computing, and storage. Additionally, the paper summarizes the challenges and presents a perspective on the future development direction of foundation model systems. Through comprehensive discussion and analysis, it hopes to provide a solid theoretical basis and practical guidance for future research and applications, promoting continuous innovation and development in foundation model systems

Compassion, Discrimination, and Prosocial Behaviors: Young Diasporic Chinese During the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic has fueled anti-Asian, especially anti-Chinese sentiments worldwide, which may negatively impact diasporic Chinese youths\u27 adjustment and prosocial development. This study examined the association between compassion, discrimination and prosocial behaviors in diasporic Chinese youths during the COVID-19 pandemic. 360 participants participated and completed the multi-country, cross-sectional, web-based survey between April 22 and May 9, 2020, the escalating stage of the pandemic. This study found compassion as prosocial behaviors\u27 proximal predictor, while discrimination independently predicted participation in volunteering, and could potentially enhance the association between compassion and charitable giving. These findings suggest that prosociality among young people is sensitive to social context, and that racial discrimination should be considered in future prosocial studies involving young members of ethnic and racial minorities

Experimental and numerical investigation of a novel photovoltaic/thermal system using micro-channel flat loop heat pipe (PV/T-MCFLHP)

In this paper, a novel photovoltaic/thermal system using micro-channel flat loop heat pipe (PV/T-MCFLHP) is proposed, and the thermal and electrical performance of the system is investigated theoretically and experimentally. The variations of temperatures were analysed, and the efficiency of the system was calculated under different conditions, i.e. simulated solar radiation, water flow rate and refrigerant filling ratio. The maximum overall efficiency of the system was found to be 51.3%, the thermal efficiency 43.8% and the electrical efficiency 7.5% with the refrigerant filling ratio of 25%, simulated solar radiation of 800 W/m2 and water flow rate of 400 L/h. Test results were compared with simulation results, and the recorded average error was 10.2%

Mechanism, structural and functional insights into nidovirus-induced double-membrane vesicles

During infection, positive-stranded RNA causes a rearrangement of the host cell membrane, resulting in specialized membrane structure formation aiding viral genome replication. Double-membrane vesicles (DMVs), typical structures produced by virus-induced membrane rearrangements, are platforms for viral replication. Nidoviruses, one of the most complex positive-strand RNA viruses, have the ability to infect not only mammals and a few birds but also invertebrates. Nidoviruses possess a distinctive replication mechanism, wherein their nonstructural proteins (nsps) play a crucial role in DMV biogenesis. With the participation of host factors related to autophagy and lipid synthesis pathways, several viral nsps hijack the membrane rearrangement process of host endoplasmic reticulum (ER), Golgi apparatus, and other organelles to induce DMV formation. An understanding of the mechanisms of DMV formation and its structure and function in the infectious cycle of nidovirus may be essential for the development of new and effective antiviral strategies in the future