Supreme People\u27s Court Annual Report on Intellectual Property Cases (2015) (China)

The Supreme People’s Court of China began publishing its Annual Report on Intellectual Property Cases in 2008. The annual reports, published in April each year, summarize and review new intellectual property cases. This translation includes all 32 cases and 38 legal issues of the 2015 Annual Report. It addresses various areas of law related to intellectual property, including patent law, trademark law, copyright law, unfair competition law, antitrust law, new plant product patent law, and laws related to procedural and evidentiary issues in intellectual property cases. While China is not a common law country, these cases serve as guidelines for lower courts in adjudicating intellectual property disputes

Supreme People\u27s Court Annual Report on Intellectual Property Cases (2015) (China)

The Supreme People’s Court of China began publishing its Annual Report on Intellectual Property Cases in 2008. The annual reports, published in April each year, summarize and review new intellectual property cases. This translation includes all 32 cases and 38 legal issues of the 2015 Annual Report. It addresses various areas of law related to intellectual property, including patent law, trademark law, copyright law, unfair competition law, antitrust law, new plant product patent law, and laws related to procedural and evidentiary issues in intellectual property cases. While China is not a common law country, these cases serve as guidelines for lower courts in adjudicating intellectual property disputes

Scalable Diffusion Models with State Space Backbone

This paper presents a new exploration into a category of diffusion models built upon state space architecture. We endeavor to train diffusion models for image data, wherein the traditional U-Net backbone is supplanted by a state space backbone, functioning on raw patches or latent space. Given its notable efficacy in accommodating long-range dependencies, Diffusion State Space Models (DiS) are distinguished by treating all inputs including time, condition, and noisy image patches as tokens. Our assessment of DiS encompasses both unconditional and class-conditional image generation scenarios, revealing that DiS exhibits comparable, if not superior, performance to CNN-based or Transformer-based U-Net architectures of commensurate size. Furthermore, we analyze the scalability of DiS, gauged by the forward pass complexity quantified in Gflops. DiS models with higher Gflops, achieved through augmentation of depth/width or augmentation of input tokens, consistently demonstrate lower FID. In addition to demonstrating commendable scalability characteristics, DiS-H/2 models in latent space achieve performance levels akin to prior diffusion models on class-conditional ImageNet benchmarks at the resolution of 256$\times$256 and 512$\times$512, while significantly reducing the computational burden. The code and models are available at: https://github.com/feizc/DiS

Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks

Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attention due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train an unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to a shallow network with only one learnable layer and cannot achieve satisfactory results when compared with multi-layer SNNs. In this paper, we eased this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature. This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness. 2)We proposed a Pooling-Reshape-Activate (PRA) layer to make the Sp-Inception module stackable. 3)We stacked multiple Sp-Inception modules to construct multi-layer SNNs. Our algorithm outperforms the baseline algorithms on the hand-written digit classification task, and reaches state-of-the-art results on the MNIST dataset among the existing unsupervised SNNs.Comment: Published at the 2020 International Joint Conference on Neural Networks (IJCNN); Extended from arXiv:2001.0168

Measuring optical vortices by means of dual shearing-type Sagnac interferometers

Measuring the positions of optical vortices is an essential part in the researches of speckles and adaptive optics. The measurement accuracy is restricted by the performance of optical devices and the properties of optical vortices, such as density and size. In order to achieve high accuracy and wide range of application, the dual shearing-type Sagnac interferometers is proposed using two shearing plates to adjust the precision of optical vortices measurement. The shearing displacements are able to balance the measuring precision and the value of the intensity ratio point to provide optimum measurement performance. This method is useful for the observation of optical vortices with different sizes and densities, especially for the high density condition