Performance Evaluation of Channel Decoding With Deep Neural Networks

With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.Comment: 6 pages, 11 figures, Latex; typos corrected; IEEE ICC 2018 to appea

MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR

Based on powerful text-to-image diffusion models, text-to-3D generation has made significant progress in generating compelling geometry and appearance. However, existing methods still struggle to recover high-fidelity object materials, either only considering Lambertian reflectance, or failing to disentangle BRDF materials from the environment lights. In this work, we propose Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR (\textbf{MATLABER}) that leverages a novel latent BRDF auto-encoder for material generation. We train this auto-encoder with large-scale real-world BRDF collections and ensure the smoothness of its latent space, which implicitly acts as a natural distribution of materials. During appearance modeling in text-to-3D generation, the latent BRDF embeddings, rather than BRDF parameters, are predicted via a material network. Through exhaustive experiments, our approach demonstrates the superiority over existing ones in generating realistic and coherent object materials. Moreover, high-quality materials naturally enable multiple downstream tasks such as relighting and material editing. Code and model will be publicly available at \url{https://sheldontsui.github.io/projects/Matlaber}

Fastened CROWN: Tightened Neural Network Robustness Certificates

The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reliable evaluations of the fragility level in different deep neural networks. Apart from devising adversarial attacks, quantifiers that certify safeguarded regions have also been designed in the past five years. The summarizing work of Salman et al. unifies a family of existing verifiers under a convex relaxation framework. We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints. Given this theoretical result, the computationally expensive linear programming based method is shown to be unnecessary. We then propose an optimization-based approach \textit{FROWN} (\textbf{F}astened C\textbf{ROWN}): a general algorithm to tighten robustness certificates for neural networks. Extensive experiments on various networks trained individually verify the effectiveness of FROWN in safeguarding larger robust regions.Comment: Zhaoyang Lyu and Ching-Yun Ko contributed equally, accepted to AAAI 202

The Annual Rhythmic Differentiation of Populus davidiana Growth–Climate Response Under a Warming Climate in The Greater Hinggan Mountains

The stability and balance of forest ecosystems have been seriously affected by climate change. Herein, we use dendrochronological methods to investigate the radial growth and climate response of pioneer tree species in the southern margin of cold temperate coniferous forest based on Populus davidiana growing on the Greater Hinggan Mountains in northeastern China. Correlations of P. davidiana growth with temperature and precipitation in a year (October–September) were rhythmically opposed: while temperatures in previous October–June (winter and spring) and in May–September (growing season) respectively inhibited and promoted radial growth on P. davidiana (p \u3c 0.01), precipitation in the same periods respectively promoted and inhibited of growth (p \u3c 0.01). High temperature or less rain/snow in winter and early spring, and low temperature or excess rainfall in summer, are inconducive to P. davidiana growth and vice versa (p \u3c 0.01). In addition, in March–April, when air temperature was above 0 °C and ground temperature below 0 °C, physiological drought caused significant growth inhibition in P. davidiana (p \u3c 0.05). In general, temperatures play a driving and controlling role in the synergistic effect of temperature and precipitation on P. davidiana growth. Under current conditions of available water supply, changes of temperature, especially warming, are beneficial to the growth of P. davidiana in the study area. The current climate conditions promote the growth of P. davidiana, the pioneer species, compared with the growth inhibition of Larix gmelinii, the dominant species. Thus, the structure and function of boreal forest might be changed under global warming by irreversible alterations in the growth and composition of coniferous and broadleaf tree species in the forest

DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

We present DiffBIR, which leverages pretrained text-to-image diffusion models for blind image restoration problem. Our framework adopts a two-stage pipeline. In the first stage, we pretrain a restoration module across diversified degradations to improve generalization capability in real-world scenarios. The second stage leverages the generative ability of latent diffusion models, to achieve realistic image restoration. Specifically, we introduce an injective modulation sub-network -- LAControlNet for finetuning, while the pre-trained Stable Diffusion is to maintain its generative ability. Finally, we introduce a controllable module that allows users to balance quality and fidelity by introducing the latent image guidance in the denoising process during inference. Extensive experiments have demonstrated its superiority over state-of-the-art approaches for both blind image super-resolution and blind face restoration tasks on synthetic and real-world datasets. The code is available at https://github.com/XPixelGroup/DiffBIR

Accelerating Diffusion Models via Early Stop of the Diffusion Process

Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks. By modeling the reverse process of gradually diffusing the data distribution into a Gaussian distribution, generating a sample in DDPMs can be regarded as iteratively denoising a randomly sampled Gaussian noise. However, in practice DDPMs often need hundreds even thousands of denoising steps to obtain a high-quality sample from the Gaussian noise, leading to extremely low inference efficiency. In this work, we propose a principled acceleration strategy, referred to as Early-Stopped DDPM (ES-DDPM), for DDPMs. The key idea is to stop the diffusion process early where only the few initial diffusing steps are considered and the reverse denoising process starts from a non-Gaussian distribution. By further adopting a powerful pre-trained generative model, such as GAN and VAE, in ES-DDPM, sampling from the target non-Gaussian distribution can be efficiently achieved by diffusing samples obtained from the pre-trained generative model. In this way, the number of required denoising steps is significantly reduced. In the meantime, the sample quality of ES-DDPM also improves substantially, outperforming both the vanilla DDPM and the adopted pre-trained generative model. On extensive experiments across CIFAR-10, CelebA, ImageNet, LSUN-Bedroom and LSUN-Cat, ES-DDPM obtains promising acceleration effect and performance improvement over representative baseline methods. Moreover, ES-DDPM also demonstrates several attractive properties, including being orthogonal to existing acceleration methods, as well as simultaneously enabling both global semantic and local pixel-level control in image generation.Comment: Code is released at https://github.com/ZhaoyangLyu/Early_Stopped_DDP

Fastened CROWN: Tightened Neural Network Robustness Certificates

The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reliable evaluations of the fragility level in different deep neural networks. Apart from devising adversarial attacks, quantifiers that certify safeguarded regions have also been designed in the past five years. The summarizing work in (Salman et al. 2019) unifies a family of existing verifiers under a convex relaxation framework. We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints. Given this theoretical result, the computationally expensive linear programming based method is shown to be unnecessary. We then propose an optimization-based approach FROWN (Fastened CROWN): a general algorithm to tighten robustness certificates for neural networks. Extensive experiments on various networks trained individually verify the effectiveness of FROWN in safeguarding larger robust regions.</jats:p

Fastened CROWN: Tightened Neural Network Robustness Certificates

The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reliable evaluations of the fragility level in different deep neural networks. Apart from devising adversarial attacks, quantifiers that certify safeguarded regions have also been designed in the past five years. The summarizing work in (Salman et al. 2019) unifies a family of existing verifiers under a convex relaxation framework. We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints. Given this theoretical result, the computationally expensive linear programming based method is shown to be unnecessary. We then propose an optimization-based approach FROWN (Fastened CROWN): a general algorithm to tighten robustness certificates for neural networks. Extensive experiments on various networks trained individually verify the effectiveness of FROWN in safeguarding larger robust regions

A GIS-Based Method for Identification of Blindness in Former Site Selection of Sewage Treatment Plants and Exploration of Optimal Siting Areas: A Case Study in Liao River Basin

With regard to environmental facilities, blindness and the subjectivity of site selection lead to serious economic, engineering and social problems. A proper siting proposal often poses a challenge to local governments, as multiple factors should be considered, such as costs, construction conditions and social impact. How to make the optimal siting decision has become a topical issue in academic circles. In order to enrich the framework of site selection models, this study combined GIS, AHP and Remote Sensing (RS) technologies to conduct siting suitability analysis of sewage treatment plants, and it was first applied in the Liao River basin in Jilin Province in China. The enriched model is able to reveal blindness in the former site selection of sewage treatment plants and explore optimal siting areas, involving an effective quantification method for summer dominant wind direction and urban stream direction. In a case study, it was found that local governments need to be cautious of the distance of sites from rivers and residential areas and the impact of these sites on downwind and downstream residents. Additionally, siting suitability has obvious regional characteristics, and its distribution varies significantly between towns. Huaide Town shows the largest optimal siting areas and can be given priority for the construction of new sewage treatment plants. This paper developed a more scientific approach to site selection, and the outcome can provide a robust reference for local governments