Research on the Impact of Returnee Executives' Strategic CSR Orientation on Corporate Value

An increasing number of studies have focused on the impact of the increase in the proportion of overseas returnees in enterprise management. How do executives with an overseas background affect corporate value? Based on Upper Echelons Theory, this paper studies the influence of overseas returnees on enterprise value from the perspective of strategic corporate social responsibility. Based on the sample composed of companies listed in Shanghai and Shenzhen stock exchange, the research shows that executives from overseas through strengthening strategic corporate social responsibility, make the enterprise social responsibility incorporated into the development of business strategy, finally promoting the ascension of the enterprise value. Namely, the strategic corporate social responsibility orientation plays an intermediary role between executives from overseas and enterprise value. This study further correlates the characteristics of overseas returnees with the strategic decisions of enterprises, deepens the understanding of the realization mechanism of overseas executives' promotion of corporate value, enriches the research results of factors influencing corporate social responsibility, to improve the active construction of local enterprises' social responsibility by changing the structure of the executive

Continuous Layout Editing of Single Images with Diffusion Models

Recent advancements in large-scale text-to-image diffusion models have enabled many applications in image editing. However, none of these methods have been able to edit the layout of single existing images. To address this gap, we propose the first framework for layout editing of a single image while preserving its visual properties, thus allowing for continuous editing on a single image. Our approach is achieved through two key modules. First, to preserve the characteristics of multiple objects within an image, we disentangle the concepts of different objects and embed them into separate textual tokens using a novel method called masked textual inversion. Next, we propose a training-free optimization method to perform layout control for a pre-trained diffusion model, which allows us to regenerate images with learned concepts and align them with user-specified layouts. As the first framework to edit the layout of existing images, we demonstrate that our method is effective and outperforms other baselines that were modified to support this task. Our code will be freely available for public use upon acceptance

Efficient Semi-Supervised Federated Learning for Heterogeneous Participants

Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data. However, training and deploying large-scale models on resource-constrained clients is challenging. Fortunately, Split Federated Learning (SFL) offers a feasible solution by alleviating the computation and/or communication burden on clients. However, existing SFL works often assume sufficient labeled data on clients, which is usually impractical. Besides, data non-IIDness across clients poses another challenge to ensure efficient model training. To our best knowledge, the above two issues have not been simultaneously addressed in SFL. Herein, we propose a novel Semi-SFL system, which incorporates clustering regularization to perform SFL under the more practical scenario with unlabeled and non-IID client data. Moreover, our theoretical and experimental investigations into model convergence reveal that the inconsistent training processes on labeled and unlabeled data have an influence on the effectiveness of clustering regularization. To this end, we develop a control algorithm for dynamically adjusting the global updating frequency, so as to mitigate the training inconsistency and improve training performance. Extensive experiments on benchmark models and datasets show that our system provides a 3.0x speed-up in training time and reduces the communication cost by about 70.3% while reaching the target accuracy, and achieves up to 5.1% improvement in accuracy under non-IID scenarios compared to the state-of-the-art baselines.Comment: 16 pages, 12 figures, conferenc

Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

The emerging Graph Convolutional Network (GCN) has now been widely used in many domains, and it is challenging to improve the efficiencies of applications by accelerating the GCN trainings. For the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computation on all vertices of an input graph. In this paper, we first point out that in a GCN training problem with a given training set, the aggregation stages of its backward propagation phase (called as backward aggregations in this paper) can be converted to partially-active graph processing procedures, which conduct computation on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the data used during backward propagations of GCN training conducted on GPUs. The experimental results show that compared with GNNAdvisor, our approach improves the performance of the backward aggregation of GCN trainings on typical real-world graphs by 1.48x~5.65x. Moreover, the execution path preparing can be conducted either before the training (during preprocessing) or on-the-fly with the training. When used during preprocessing, our approach improves the overall GCN training by 1.05x~1.37x. And when used on-the-fly, our approach improves the overall GCN training by 1.03x~1.35x

Experimental investigation of combined transpiration and jet cooling of sintered metal porous struts

The scramjet combustion chamber provides the propulsion for hypersonic vehicle. The fuel is combusted in the chamber to generate thrust. The strut is used to inject fuel into the core of combustion chamber to enhance the mixing and the combustion. The mainstream in a scramjet combustion chamber is at supersonic velocity and high temperature. The tremendous aerodynamic heating will then cause ablation of the strut without adequate cooling. Therefore, effective thermal protection methods must be provided for the strut, especially for the leading edge. Transpiration cooling is one of the most effective cooling methods to protect surfaces at high heat flux conditions, and can effectively protect most part of the strut, but some ablation was found at the strut leading edge. Please download the full abstract below

Il-verbi f'sekwenza fil-Malti

F’dan l-artiklu l-awtriċi tistħarreġ in-natura tal-katina verbali fil-Malti, li għalissa nistgħu niddeskrivuha bħala sekwenza ta’ verbi li jokkorru wara xulxin mingħajr l-ebda ndħil ta’ xi element ieħor.peer-reviewe

Ultrafast consolidation of bulk nanocrystalline titanium alloy through ultrasonic vibration

Nanocrystalline (NC) materials have fascinating physical and chemical properties, thereby they exhibit great prospects in academic and industrial fields. Highly efficient approaches for fabricating bulk NC materials have been pursued extensively over past decades. However, the instability of nanograin, which is sensitive to processing parameters (such as temperature and time), is always a challenging issue to be solved and remains to date. Herein, we report an ultrafast nanostructuring strategy, namely ultrasonic vibration consolidation (UVC). The strategy utilizes internal friction heat, generated from mutually rubbing between Ti-based metallic glass powders, to heat the glassy alloy rapidly through its supercooled liquid regime, and accelerated viscous flow bonds the powders together. Consequently, bulk NC-Ti alloy with grain size ranging from 10 to 70 nm and nearly full density is consolidated in 2 seconds. The novel consolidation approach proposed here offers a general and highly efficient pathway for manufacturing bulk nanomaterials