Optimizing Image Compression via Joint Learning with Denoising

High levels of noise usually exist in today's captured images due to the relatively small sensors equipped in the smartphone cameras, where the noise brings extra challenges to lossy image compression algorithms. Without the capacity to tell the difference between image details and noise, general image compression methods allocate additional bits to explicitly store the undesired image noise during compression and restore the unpleasant noisy image during decompression. Based on the observations, we optimize the image compression algorithm to be noise-aware as joint denoising and compression to resolve the bits misallocation problem. The key is to transform the original noisy images to noise-free bits by eliminating the undesired noise during compression, where the bits are later decompressed as clean images. Specifically, we propose a novel two-branch, weight-sharing architecture with plug-in feature denoisers to allow a simple and effective realization of the goal with little computational cost. Experimental results show that our method gains a significant improvement over the existing baseline methods on both the synthetic and real-world datasets. Our source code is available at https://github.com/felixcheng97/DenoiseCompression.Comment: Accepted to ECCV 202

DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics

The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, which causes the client models drift further away from each other and degrades the aggregated global model's performance. A natural solution is to gather all client data onto the server, such that the server has a global view of the entire data distribution. Unfortunately, this reduces to regular training, which compromises clients' privacy and conflicts with the purpose of FL. In this paper, we put forth an idea to collect and leverage global knowledge on the server without hindering data privacy. We unearth such knowledge from the dynamics of the global model's trajectory. Specifically, we first reserve a short trajectory of global model snapshots on the server. Then, we synthesize a small pseudo dataset such that the model trained on it mimics the dynamics of the reserved global model trajectory. Afterward, the synthesized data is used to help aggregate the deflected clients into the global model. We name our method Dynafed, which enjoys the following advantages: 1) we do not rely on any external on-server dataset, which requires no additional cost for data collection; 2) the pseudo data can be synthesized in early communication rounds, which enables Dynafed to take effect early for boosting the convergence and stabilizing training; 3) the pseudo data only needs to be synthesized once and can be directly utilized on the server to help aggregation in subsequent rounds. Experiments across extensive benchmarks are conducted to showcase the effectiveness of Dynafed. We also provide insights and understanding of the underlying mechanism of our method

Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems

Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code will be released.Comment: RecSys 202

Equivariant Contrastive Learning for Sequential Recommendation

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., featurelevel dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.Comment: Accepted by RecSys 202

PLANNING AND IMPLEMENTATION OF RENEWABLE ENERGY IN COASTAL CITIES - A CASE STUDY OF HUANGPU DISTRICT, GUANGZHOU

To help local governments to scientifically formulate renewable energy (RE) development goals and implement them effectively, the study in this paper developed the renewable energy planning and implementation methodology and applied this method in Huangpu district of Guangzhou for demonstration and application in practical settings. To evaluate the potential of Huangpu from the perspective of renewable energy, quantitative analysis has been carried out by using the GIS method, followed by a multi-criteria assessment and the Delphi Method to screen out applicable technologies in this region. The solar photovoltaic (PV) technology installed on the industrial building rooftop is identified as the suitable solar PV technology development in the target region. The potential of solar resource is also higher than the government's PV development target of 177MW. In addition, the cost-benefit investment analysis based on three typical sizes of solar PV projects has also been carried out using the financial analysis method along with social and environmental benefits. It has been estimated that by 2025, a total of 1001 GWh energy can be generated while carbon emissions can be reduced by 851000 tons of CO2 equivalent (tCO(2)e) in the case of the implementation of all 320MW PV projects. The program will contribute to the addition of 163 PV systems in the local PV development market

PLANNING AND IMPLEMENTATION OF RENEWABLE ENERGY IN COASTAL CITIES - A CASE STUDY OF HUANGPU DISTRICT, GUANGZHOU

To help local governments to scientifically formulate renewable energy (RE) development goals and implement them effectively, the study in this paper developed the renewable energy planning and implementation methodology and applied this method in Huangpu district of Guangzhou for demonstration and application in practical settings. To evaluate the potential of Huangpu from the perspective of renewable energy, quantitative analysis has been carried out by using the GIS method, followed by a multi-criteria assessment and the Delphi Method to screen out applicable technologies in this region. The solar photovoltaic (PV) technology installed on the industrial building rooftop is identified as the suitable solar PV technology development in the target region. The potential of solar resource is also higher than the government's PV development target of 177MW. In addition, the cost-benefit investment analysis based on three typical sizes of solar PV projects has also been carried out using the financial analysis method along with social and environmental benefits. It has been estimated that by 2025, a total of 1001 GWh energy can be generated while carbon emissions can be reduced by 851000 tons of CO2 equivalent (tCO(2)e) in the case of the implementation of all 320MW PV projects. The program will contribute to the addition of 163 PV systems in the local PV development market

Evolution of Solar Photovoltaic Policies and Industry in China

Abstract China has experienced a rapid growth of both SPV manufacturing capacity and installed capacity in the last twenty-five years. However, this growth has followed a very erratic path. This study identifies policies issued through this period for a closer look on the impact of these policies to the solar photovoltaic (SPV) industry development in China. This paper examines five stages in China’s SPV policy from mid-1990s to 2019. Each stage has implemented different combinations of policy program. These changes in government policy and the effects to the SPV sector are attributed to three main sets of variables. First and foremost, the events that influence the policy and strategy priorities of Chinese government. Secondary factors include the government’s poor management of the policy impacts to the SPV manufacturing industry and the domestic SPV market at early days, as well as policymaking and problems coping within government. The subsidy, FIT policies and other programs had stimulated the deployment of SPV in a large scale but brings several problems such as subsidy reliance. The fundamental measure to improve SPV development into a grid parity era is the technological advancement of photovoltaic in efficiency and manufacturing