Creativity, innovation and the ‘New’ MBA : China and the 21st century knowledge economy

This paper discusses the development of new models of business education in contemporary China. It describes the rise of the Masters of Business Administration (MBA) degree in the context of the growth of a new professional-managerial class in China, as a corollary of modernisation and economic reform. While the Masters of Business Administration (MBA) has its origins in the United States, it has grown into a globally recognized qualification for business status, particularly when acquired from ‘elite’ institutions in a highly competitive and extensively ranked global system. Its growth in Asia is reflective of the significant shortages of managerial expertise as economic success throws traditional family-based or state capitalist models of business organization into question. In China, the rise of the MBA has been more recent, although the original idea was introduced in the late 1970s, not long after the directive of Deng Xiaoping to modernise the economy. We consider the role played by new MBA programs, such as the Executive MBA (EMBA) and the International MBA (IMBA) as new educational products designed, not so much for the re-engineering of management practices in SOEs along more effective commercial lines, but rather upon developing an internationally networked business elite better able to engage with the new challenges of the global knowledge economy

Diffusion Model as Representation Learner

Diffusion Probabilistic Models (DPMs) have recently demonstrated impressive results on various generative tasks.Despite its promises, the learned representations of pre-trained DPMs, however, have not been fully understood. In this paper, we conduct an in-depth investigation of the representation power of DPMs, and propose a novel knowledge transfer method that leverages the knowledge acquired by generative DPMs for recognition tasks. Our study begins by examining the feature space of DPMs, revealing that DPMs are inherently denoising autoencoders that balance the representation learning with regularizing model capacity. To this end, we introduce a novel knowledge transfer paradigm named RepFusion. Our paradigm extracts representations at different time steps from off-the-shelf DPMs and dynamically employs them as supervision for student networks, in which the optimal time is determined through reinforcement learning. We evaluate our approach on several image classification, semantic segmentation, and landmark detection benchmarks, and demonstrate that it outperforms state-of-the-art methods. Our results uncover the potential of DPMs as a powerful tool for representation learning and provide insights into the usefulness of generative models beyond sample generation. The code is available at \url{https://github.com/Adamdad/Repfusion}.Comment: Accepted by ICCV 202

Globally Optimal Cell Tracking using Integer Programming

We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor

C-Procgen: Empowering Procgen with Controllable Contexts

We present C-Procgen, an enhanced suite of environments on top of the Procgen benchmark. C-Procgen provides access to over 200 unique game contexts across 16 games. It allows for detailed configuration of environments, ranging from game mechanics to agent attributes. This makes the procedural generation process, previously a black-box in Procgen, more transparent and adaptable for various research needs.The upgrade enhances dynamic context management and individualized assignments, while maintaining computational efficiency. C-Procgen's controllable contexts make it applicable in diverse reinforcement learning research areas, such as learning dynamics analysis, curriculum learning, and transfer learning. We believe that C-Procgen will fill a gap in the current literature and offer a valuable toolkit for future works

Streptamer technology allows to isolate leukemia antigen-specific CD8+ T cells

In this work we investigated whether streptamer technology could purify WT1-specific CD8+ T cells, what is important for the development of adoptive immunotherapy. Sample from HLA/A2+ HDs were identified and selected by streptamer. The function of selected CD8+ T cells was identified by the staining of phenotypic markers. The results showed that streptamer permits the detection and selection of WT1-specific CD8+ T cells in the PBMCs from HDs. The naïve function of selected CD8+ T cells was preserved and most selected CD8+ T cells demonstrated an effector T cell immunophenotype