Parallel Narrative: Short-Video Social Media Platforms’ Influences on Contemporary Narrative

Through the study of Kwai, a popular short-video social media platform in China, this thesis investigates the social issues, media class divides and aesthetics specific to Kwai culture. It further proposes a strategy of artistic practice - parallel narrative - an experiment in video art production and editing techniques that explores new possibilities of narrative in video art. Integrating theoretical research on Post-Internet art and object-oriented ontology, this thesis reveals people’s ability to digest multisource information and shows how mobile technologies and open-source materials contribute to the formation of parallel narratives

Translated architecture

“Translated Architecture” is an unique psychological implication space that is translated from the spatiality camera language. Its significance lies in the ability to better approach and understand the psychological condition brought by the living environment. To better understand what the camera language could lead us to, this thesis chooses to take phobias as an entry point to analyze the subtle changes in human psychology under different camera views.phobias like social phobia, Claustrophobia, Agoraphobia on a certain level is a form of mental illness that is related to architectural space. Take Claustrophobia as an example, Claustrophobia is a situational phobia triggered by an irrational and intense fear of tight or crowded spaces. Claustrophobia can be triggered by things like being locked in a windowless room, being stuck in a crowded elevator or driving on a congested highway. It is one of the most common phobias. The question is why and how does space and view impact our mental conditions. Phobia is subjective. Space is not. Different spaces and different angles to perceive the space will have a decisive impact on our subjective feeling. Movies and Its camera language can facilitate relationships between space, human behavior, and psychology. When we are watching a movie, the use of camera language is an action that is most likely to change our subjective perception. In this book. I’ll be first showing 5 different camera techniques from different classic movie scenes. Then recreate these scenes using the same enactment in a familiar space. Mostly on RISD campus. And finally there will be diagrams showing how the camera techniques impact on the preexisting space and how the camera techniques could translate into architectural space

Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data

In some applications of reinforcement learning, a dataset of pre-collected experience is already available but it is also possible to acquire some additional online data to help improve the quality of the policy. However, it may be preferable to gather additional data with a single, non-reactive exploration policy and avoid the engineering costs associated with switching policies. In this paper we propose an algorithm with provable guarantees that can leverage an offline dataset to design a single non-reactive policy for exploration. We theoretically analyze the algorithm and measure the quality of the final policy as a function of the local coverage of the original dataset and the amount of additional data collected.Comment: 43 page

NDF: Neural Deformable Fields for Dynamic Human Modelling

We propose Neural Deformable Fields (NDF), a new representation for dynamic human digitization from a multi-view video. Recent works proposed to represent a dynamic human body with shared canonical neural radiance fields which links to the observation space with deformation fields estimations. However, the learned canonical representation is static and the current design of the deformation fields is not able to represent large movements or detailed geometry changes. In this paper, we propose to learn a neural deformable field wrapped around a fitted parametric body model to represent the dynamic human. The NDF is spatially aligned by the underlying reference surface. A neural network is then learned to map pose to the dynamics of NDF. The proposed NDF representation can synthesize the digitized performer with novel views and novel poses with a detailed and reasonable dynamic appearance. Experiments show that our method significantly outperforms recent human synthesis methods.Comment: 16 pages, 7 figures. Accepted by ECCV 202

Combining Cloud and Mobile Computing for Machine Learning

Although the computing power of mobile devices is increasing, machine learning models are also growing in size. This trend creates problems for mobile devices due to limitations like their memory capacity and battery life. While many services, like ChatGPT and Midjourney, run all the inferences in the cloud, we believe a flexible and fine-grained task distribution is more desirable. In this work, we consider model segmentation as a solution to improving the user experience, dividing the computation between mobile devices and the cloud in a way that offloads the compute-heavy portion of the model while minimizing the data transfer required. We show that the division not only reduces the wait time for users but can also be fine-tuned to optimize the workloads of the cloud. To achieve that, we design a scheduler that collects information about network quality, client device capability, and job requirements, making decisions to achieve consistent performance across a range of devices while reducing the work the cloud needs to perform.Comment: Ruiqi Xu and Tianchi Zhang contributed equally to this wor

Learning Graph Embedding with Adversarial Training Methods

Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction errors for graph data. They have mostly overlooked the embedding distribution of the latent codes, which unfortunately may lead to inferior representation in many cases. In this paper, we present a novel adversarially regularized framework for graph embedding. By employing the graph convolutional network as an encoder, our framework embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph. The adversarial training principle is applied to enforce our latent codes to match a prior Gaussian or Uniform distribution. Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit other potential variations of ARGA and ARVGA to get a deeper understanding on our designs. Experimental results compared among twelve algorithms for link prediction and twenty algorithms for graph clustering validate our solutions.Comment: To appear in IEEE Transactions on Cybernetics. arXiv admin note: substantial text overlap with arXiv:1802.0440

Responses of suspended sediment yield to topography and land use in the Yellow River Basin, China

University of Tokyo(東京大学