Matting Anything

In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance. MAM offers several significant advantages over previous specialized image matting networks: (i) MAM is capable of dealing with various types of image matting, including semantic, instance, and referring image matting with only a single model; (ii) MAM leverages the feature maps from the Segment Anything Model (SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha matte through iterative refinement, which has only 2.7 million trainable parameters. (iii) By incorporating SAM, MAM simplifies the user intervention required for the interactive use of image matting from the trimap to the box, point, or text prompt. We evaluate the performance of MAM on various image matting benchmarks, and the experimental results demonstrate that MAM achieves comparable performance to the state-of-the-art specialized image matting models under different metrics on each benchmark. Overall, MAM shows superior generalization ability and can effectively handle various image matting tasks with fewer parameters, making it a practical solution for unified image matting. Our code and models are open-sourced at https://github.com/SHI-Labs/Matting-Anything.Comment: Project web-page: https://chrisjuniorli.github.io/project/Matting-Anything

The Development of Strategic Human Resource Management in the Chinese Financial Services Sector: Understanding the Roles of External Economic Factors and the State

The Chinese economy has experienced reform, rapid growth and a significant slowing down period over the last thirty years. During this time, the Chinese approach to people management has also shifted, with some observers suggesting a shift from personnel management into strategic Human Resource Management (SHRM). In many studies of SHRM, economic factors have been recognised to be essential external environmental forces which contribute to HRM strategy formulation (Schuler, 1992; Truss and Gratton, 1994, Boxall and Purcell, 2011). Under this argument, financial markets and the economic development of many countries have changed, the social and political environment has also been forced to adapt, and as a result, work, employment and HRM system of firms have been required to adjust to these changes. This thesis examines the specific effects of Chinese economic development on employers’ HR decisions, something which has been neglected in the SHRM literature to date. Employers in China, as elsewhere, may adopt different approaches to HRM development. For some, HRM systems in recent years have been built up from a low base, whilst others have adjusted well-established existing HRM approaches. Other employers have explored or implement radically different or ‘new’ approaches. These approaches can result in contradictions, tensions and resistance, due to differences between the rhetoric of seamless adjustment to ‘strategic’ HRM and the reality of what happens. All of these elements of HRM adjustment are underexplored in debates to date. The Chinese case is a particularly interesting lens through which to explore these under-researched issues due to its unique management contexts and the richness on social and economic transformation. The development of SHRM in China is ripe for exploration regarding how organisational HRM strategy does or does not supports operations. The key contribution of the thesis is in its examination of ‘fit’ and ‘non-fit’ of SHRM in Chinese firms. The thesis argues that changes in the external environment in China have led to different rather than uniform adjustments in HRM strategy and practices in individual firms. Adjustments that are often presented as necessary, being influenced or required by the state, or seen to be part of a ‘best practice’ approach to SHRM may or may not actually be implemented effectively in practice. However, even where effective implementation does not occur, resultant HR systems may still contribute to the growth and development of organisations. In this thesis, this examination of fit and non-fit is explored from the viewpoint of employers in the context of economic development in a transitional economy. The thesis also provides insights into how and why contradictions in business strategy and development are significant, and in doing so, examines the efficiency of HR systems in China in terms of reacting to change. The empirical strategy for the research in this thesis involves qualitative research methods. A single case study of the Chinese financial service sector with multiple firms is used. Fifty-nine semi-structured interviews are conducted. The interviews were designed to explore employers’ responses to the external economic environment. Interviews were carried out with policy makers of government institutions, executive and general managers and HR directors from financial firms including state-owned and joint-stock banks, insurance companies and other financial institutions. The data generated from this research explores the drivers and effects of changes to HRM systems in two ways. First, the research identifies factors and events that cause concerns for firms, or which demand changes. Secondly, the thesis explores how these concerns or imperatives for change have, or have not been addressed and implemented in organisations. The findings reveal that specific economic development policies and changing economic cycles are recognized by employers, causing them concerns or compelling them to alter their skills mix or the number of workers they require. Differences in perceptions and approaches between state-owned firms and joint-stock firms are found to be significant. Some external factors which impact upon HRM strategy, such as political environmental factors lead firms to adjust their organisational governance systems and business strategy. The adjustment of specific HRM practices to external changes can be seen mostly in changes to recruitment and training strategy. The fit between business strategy and other HR practices, notably payroll systems and performance appraisals during different economic cycle stages, is less clear. However, even where ‘non-fit’ occurs, HR systems are still considered as working supportively to the growth of organisations. In conclusion, this thesis contributes to the theory of SHRM by developing a deeper understanding of fit and by illuminating the idea that actively choosing ‘non-fit’ at both strategic and implementation level can enhance the effectiveness of organisational operation under certain circumstances. It also contributes to the theoretical framework of SHRM through introducing the taxonomy of the role of economic factors and the state in China

Two Case Histories of Alkali Liquid Method to Reinforce Collapsible Loess Deposit

Presented in this paper is the summary of two case histories using alkali liquid method to reinforce collapsible Loess ground. One is the ground treatment of administration building which was not in a position of normal service because of the unequal settlement of the ground caused by collapsibility; the other is the ground improvement of the office building of a hospital before construction. The test to examine reinforcing effects is held one month after ground stabilization. It is learned from the test results that the soil compressibility characteristics within the treated aera has been changed from high grade to medium grade or tow grade, and the collapsibility of loess within the treated area has been eliminated. The method of alkali liquid to improve ground has many advantages, namely, simple in construction, with obvious effects, and no vibration or contamination to be caused

Consensus-Based Distributed Filtering with Fusion Step Analysis

For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering. However, due to the limited communication resources in physical systems, the number of fusion steps cannot be infinite. To deal with this issue, the present paper analyzes the performance of CMDF with finite consensus fusion operations. First, by introducing a modified discrete-time algebraic Riccati equation and several novel techniques, the convergence of the estimation error covariance matrix of each sensor is guaranteed under a collective observability condition. In particular, the steady-state covariance matrix can be simplified as the solution to a discrete-time Lyapunov equation. Moreover, the performance degradation induced by reduced fusion frequency is obtained in closed form, which establishes an analytical relation between the performance of the CMDF with finite fusion steps and that of centralized filtering. Meanwhile, it provides a trade-off between the filtering performance and the communication cost. Furthermore, it is shown that the steady-state estimation error covariance matrix exponentially converges to the centralized optimal steady-state matrix with fusion operations tending to infinity during each sampling interval. Finally, the theoretical results are verified with illustrative numerical experiments

A nanogapped hysteresis-free field-effect transistor

We propose a semi-suspended device structure and construct nanogapped, hysteresis-free field-effect transistors (FETs), based on the van der Waals stacking technique. The structure, which features a semi-suspended channel above a submicron-long wedge-like nanogap, is fulfilled by transferring ultraclean BN-supported MoS$_2$ channels directly onto dielectric-spaced vertical source/drain stacks. Electronic characterization and analyses reveal a high overall device quality, including ultraclean channel interfaces, negligible electrical scanning hysteresis, and Ohmic contacts in the structures. The unique hollow FET structure holds the potential for exploiting reliable electronics, as well as nanofluid and pressure sensors.Comment: 22 pages, 4 figures, with S

Escaping the Big Data Paradigm with Compact Transformers

With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amounts of training data. Many have come to believe that because of this, transformers are not suitable for small sets of data. This trend leads to concerns such as: limited availability of data in certain scientific domains and the exclusion of those with limited resource from research in the field. In this paper, we aim to present an approach for small-scale learning by introducing Compact Transformers. We show for the first time that with the right size, convolutional tokenization, transformers can avoid overfitting and outperform state-of-the-art CNNs on small datasets. Our models are flexible in terms of model size, and can have as little as 0.28M parameters while achieving competitive results. Our best model can reach 98% accuracy when training from scratch on CIFAR-10 with only 3.7M parameters, which is a significant improvement in data-efficiency over previous Transformer based models being over 10x smaller than other transformers and is 15% the size of ResNet50 while achieving similar performance. CCT also outperforms many modern CNN based approaches, and even some recent NAS-based approaches. Additionally, we obtain a new SOTA result on Flowers-102 with 99.76% top-1 accuracy, and improve upon the existing baseline on ImageNet (82.71% accuracy with 29% as many parameters as ViT), as well as NLP tasks. Our simple and compact design for transformers makes them more feasible to study for those with limited computing resources and/or dealing with small datasets, while extending existing research efforts in data efficient transformers. Our code and pre-trained models are publicly available at https://github.com/SHI-Labs/Compact-Transformers.Comment: Added new results on Flowers-102, distillatio

Mitochondria-Targeted Nanomedicine for Enhanced Efficacy of Cancer Therapy

Nanomedicines have been designed and developed to deliver anticancer drugs or exert anticancer therapy more selectively to tumor sites. Recent investigations have gone beyond delivering drugs to tumor tissues or cells, but to intracellular compartments for amplifying therapy efficacy. Mitochondria are attractive targets for cancer treatment due to their important functions for cells and close relationships to tumor occurrence and metastasis. Accordingly, multifunctional nanoplatforms have been constructed for cancer therapy with the modification of a variety of mitochondriotropic ligands, to trigger the mitochondria-mediated apoptosis of tumor cells. On this basis, various cancer therapeutic modalities based on mitochondria-targeted nanomedicines are developed by strategies of damaging mitochondria DNA (mtDNA), increasing reactive oxygen species (ROS), disturbing respiratory chain and redox balance. Herein, in this review, we highlight mitochondria-targeted cancer therapies enabled by nanoplatforms including chemotherapy, photothermal therapy (PTT), photodynamic therapy (PDT), chemodynamic therapy (CDT), sonodynamic therapy (SDT), radiodynamic therapy (RDT) and combined immunotherapy, and discussed the ongoing challenges.Peer reviewe