Next generation antidepressants with novel mechanisms for treatment resistant depression

Chen, Mu‐Hong; Tu, Pei-Chi; Su, Tung‐Ping

doi:10.1016/bs.pbr.2023.02.005

Cited by 3 publications

(2 citation statements)

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“…Despite their impressive performance, current 3D GANs face several challenges. Multi-view images remain indispensable for synthesizing 3D assets with plausible details from all viewing angles [10,21,7]. While it is possible to train a 3D GAN using only single-view images, the quality declines substantially.…”

Section: Introductionmentioning

confidence: 99%

Does university participation facilitate young people’s citizenship behaviour in the UK?

Yang

Hoskins

2022

Cambridge Journal of Education

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Despite the growing success of 3D-aware GANs, which can be trained on 2D images to generate high-quality 3D assets, they still rely on multi-view images with camera annotations to synthesize sufficient details from all viewing directions. However, the scarce availability of calibrated multi-view image datasets, especially in comparison to single-view images, has limited the potential of 3D GANs. Moreover, while bypassing camera pose annotations with a camera distribution constraint reduces dependence on exact camera parameters, it still struggles to generate a consistent orientation of 3D assets. To this end, we propose SYM3D, a novel 3D-aware GAN designed to leverage the prevalent reflectional symmetry structure found in natural and man-made objects, alongside a proposed view-aware spatial attention mechanism in learning the 3D representation. We evaluate SYM3D on both synthetic (ShapeNet Chairs, Cars, and Airplanes) and real-world datasets (ABO-Chair), demonstrating its superior performance in capturing detailed geometry and texture, even when trained on only single-view images. Finally, we demonstrate the effectiveness of incorporating symmetry regularization in helping reduce artifacts in the modeling of 3D assets in the text-to-3D task.Preprint. Under review.

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Section: Introductionmentioning

confidence: 99%

Does university participation facilitate young people’s citizenship behaviour in the UK?

Yang

Hoskins

2022

Cambridge Journal of Education

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“…MultiDiffusion [3] and SyncDiffusion [35] correlate multiple diffusion models for different regions of a 2D image. Many recent works [1,7,10,17,23,27,30,31,32,34,42,45,46,48,75,87,88] try to repeat the success of diffusion models on the 3D generation task. However, the scarcity of 3D data makes it difficult to directly train diffusion models on 3D and the resulting generation quality is still much worse and less generalizable than the counterpart image generation models, though some works [1,7,32] are trying to only use 2D images for training 3D diffusion models.…”

Section: Related Work 21 Diffusion Modelsmentioning

confidence: 99%

Exploring metadiscourse in university lectures in Hong Kong

Liu¹

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Federated Graph Neural Network (FedGNN) has recently emerged as a rapidly growing research topic, as it integrates the strengths of graph neural networks and federated learning to enable advanced machine learning applications without direct access to sensitive data. Despite its advantages, the distributed nature of FedGNN introduces additional vulnerabilities, particularly backdoor attacks stemming from malicious participants. Although graph backdoor attacks have been explored, the compounded complexity introduced by the combination of GNNs and federated learning has hindered a comprehensive understanding of these attacks, as existing research lacks extensive benchmark coverage and in-depth analysis of critical factors. To address these limitations, we propose Bkd-FedGNN, a benchmark for backdoor attacks on FedGNN. Specifically, Bkd-FedGNN decomposes the graph backdoor attack into trigger generation and injection steps, and extending the attack to the node-level federated setting, resulting in a unified framework that covers both node-level and graph-level classification tasks. Moreover, we thoroughly investigate the impact of multiple critical factors in backdoor attacks on FedGNN. These factors are categorized into global-level and local-level factors, including data distribution, the number of malicious attackers, attack time, overlapping rate, trigger size, trigger type, trigger position, and poisoning rate. Finally, we conduct comprehensive evaluations on 13 benchmark datasets and 13 critical factors, comprising 1,725 experimental configurations for node-level and graphlevel tasks from six domains. These experiments encompass over 8,000 individual tests, allowing us to provide a thorough evaluation and insightful observations that advance our understanding of backdoor attacks on FedGNN. The Bkd-FedGNN benchmark is publicly available at https://github.com/usail-hkust/ BkdFedGCN.Preprint. Under review.

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