Hut Annandale: Humblest Dwelling

Lots of us have a dream deep down in the heart: to get away from the congested cities and live in a hut in nature. French port Jean Wahl once wrote: The frothing of the hedges I keep deep inside me. In my project, he explored this dream and constructed a group of architectural structures by hand for those potential hermits. Studying at Bard College, I have found this region is a place with a great hermit culture. With the picturesque scene of nature and the location near the New York Metropolitan area, here the mid-Hudson Valley has attracted lots of artists and scholars to live. It reminds me of his hometown, a small Chinese city named Zhenjiang locates next to one of China’s ancient capitals – Nanking. In history, lots of literati have chosen to escape to Zhenjiang from life in the capital and created great masterpieces during their life as hermits. I believe currently in this era of congestion; people should also give themselves a separate space from the ocean of the noise and have a think of the life of a conceptual recluse. In response to the culture of hermit, I have imagined a hut, which is the place the hermits can get closest to the world and the dream of the poem. This hut became the main structure of the project. A roof, a beam together with several pillars and eaves have made a humblest architecture. The simplicity of the hut makes it into an archetype of the architecture as well as a minimal universe. In Eastern languages’ vocabulary, the etymology of the word “universe” (YuZhou) is coming from architecture. Yu means eaves and Zhou means beams. A space under the roof is our first recognized individual space separated from the world and it represents our most primitive understanding of a universe. The French philosopher Gaston Bachelard has made a conceptual connection between house and universe in his book The Poetics of Space as well. He said “At whatever dialectical pole the dreamer stands, whether in the house or the universe, the dialectics become dynamic. House and space are not merely two juxtaposed elements of space. In the reign of the imagination, they awaken daydreams in each other, that are opposed.” Besides the inherited cosmic quality of the simplest hut, I also want this space to have a poetic quality which could make it transcend the geometrical space and be the space for spirits, like a wooden hut in a faded print or a small pavilion in a huge Chinese landscape painting. Without giving any physical walls to the structure, I hope this space is open to the world and the time. The openness together with the big roof and flying eaves have given the structure the imagery of airy and weightless. For the form of the structure, I have combined the refined elements from the traditional Eastern architecture and art and the expressive big curves to make the structure both have Baroque visual tension and a gentle and ethereal quality like an Eastern freehand brushwork. In the material selection. I have chosen bamboos as the building material. As a plant, bamboo has the beauty of lightness and rectitude and its imagery has been widely used in Eastern art. In addition, the individual construction of the bamboo architecture explores the potentiality of the nontectonic architectures. At the time when high-tech and expensive architectures become the absolute mainstream, this kind of low-tech, cheap but sustainable architecture should be valued as well

Learning Generative ConvNets via Multi-grid Modeling and Sampling

This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized examples from the model. Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image. The synthesized image at each subsequent grid is obtained by a finite-step MCMC initialized from the synthesized image generated at the previous coarser grid. After obtaining the synthesized examples, the parameters of the models at multiple grids are updated separately and simultaneously based on the differences between synthesized and observed examples. We show that this multi-grid method can learn realistic energy-based generative ConvNet models, and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201

Community building and building the community: a case study of the bottom-up community development in Shanghai, China

This paper examines the concept and dynamics of community building (Shequ Yingzao), an emerging trend in urban development in China that emphasizes community-based design and bottom-up processes. The case study of Xinhua Community in Shanghai serves as the focal point of this investigation. By analyzing Shanghai's recently developed policy framework supporting community building city-wide and the practices implemented by the local non-profit organization Big Fish Community Building Center (Dayu Yingzao) in Xinhua, this study explores the crucial role of community-based organizations in promoting participatory design, complementing top-down urban planning, facilitating urban regeneration, and fostering grassroots governance, community autonomy, and local democracy in the context of Shanghai. Through qualitative research, this study evaluates the case of Xinhua Community’s community building, aiming to identify both the potential and challenges of autonomy-based community development and design in China. The findings indicate that the presence of the local community building organization and the policy support from Shanghai’s municipality played decisive roles in Xinhua’s community building. Xinhua's community building has made significant strides in recent years but it still grapples with issues related to ambiguous property rights, social disconnection, government dominance, and incomplete community autonomy. The dependence on administrative power and fluctuating political interests underscores the importance of cultivating a more self-sufficient and resilient community building process. Nevertheless, community building activities in Xinhua significantly accumulate social capital in the community, which suggests prospects for further improvement. This study contributes to a more profound understanding of community building processes and their implications for urban development, governance, community autonomy, and local democracy, both in China and beyond

Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood

Training energy-based models (EBMs) with maximum likelihood estimation on high-dimensional data can be both challenging and time-consuming. As a result, there a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models. To close this gap, inspired by the recent efforts of learning EBMs by maximimizing diffusion recovery likelihood (DRL), we propose cooperative diffusion recovery likelihood (CDRL), an effective approach to tractably learn and sample from a series of EBMs defined on increasingly noisy versons of a dataset, paired with an initializer model for each EBM. At each noise level, the initializer model learns to amortize the sampling process of the EBM, and the two models are jointly estimated within a cooperative training framework. Samples from the initializer serve as starting points that are refined by a few sampling steps from the EBM. With the refined samples, the EBM is optimized by maximizing recovery likelihood, while the initializer is optimized by learning from the difference between the refined samples and the initial samples. We develop a new noise schedule and a variance reduction technique to further improve the sample quality. Combining these advances, we significantly boost the FID scores compared to existing EBM methods on CIFAR-10 and ImageNet 32x32, with a 2x speedup over DRL. In addition, we extend our method to compositional generation and image inpainting tasks, and showcase the compatibility of CDRL with classifier-free guidance for conditional generation, achieving similar trade-offs between sample quality and sample diversity as in diffusion models

The Effect of Product Recommendations on Online Investor Behaviors

Despite the popularity of product recommendations on online investment platforms, few studies have explored their impact on investor behaviors. Using data from a global e-commerce platform, we apply regression discontinuity design to causally examine the effects of product recommendations on online investors' mutual fund investments. Our findings indicate that recommended funds experience a significant rise in purchases, especially among low socioeconomic status investors who are most influenced by these recommendations. However, investors tend to suffer significantly worse investment returns after purchasing recommended funds, and this negative impact is also most significant for investors with low socioeconomic status. To explain this disparity, we find investors tend to gather less information and expend reduced effort in fund research when buying recommended funds. Furthermore, investors' redemption timing of recommended funds is less optimal than non-recommended funds. We also find that recommended funds experience a larger return reversal than non-recommended funds. In conclusion, product recommendations make investors behave more irrationally and these negative consequences are most significant for investors with low socioeconomic status, which can amplify wealth inequality among investors in financial markets