Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization

Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains. Previous methods focus on generating domain invariant features from various source domains. However, we argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks, which has been largely ignored. Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise. The proposed domain expert features lie in a learned latent space where the images in each domain can be classified independently, enabling the implicit use of classification-aware domain variations. Based on the analysis, we proposed a novel paradigm called Domain Disentanglement Network (DDN) to disentangle the domain expert features from the source domain images and aggregate the source domain expert features for representing the target test domain. We also propound a new contrastive learning method to guide the domain expert features to form a more balanced and separable feature space. Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet, and TerraIncognita demonstrate the competitive performance of our method compared to the recently proposed alternatives

A Dynamic Points Removal Benchmark in Point Cloud Maps

In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing dynamic points in point clouds often lack clarity in comparative evaluations and comprehensive analysis. Therefore, we propose an easy-to-extend unified benchmarking framework for evaluating techniques for removing dynamic points in maps. It includes refactored state-of-art methods and novel metrics to analyze the limitations of these approaches. This enables researchers to dive deep into the underlying reasons behind these limitations. The benchmark makes use of several datasets with different sensor types. All the code and datasets related to our study are publicly available for further development and utilization.Comment: Code check https://github.com/KTH-RPL/DynamicMap_Benchmark.git , 7 pages, accepted by ITSC 202

Quantitative measurement of mechanical properties in wound healing processes in a corneal stroma model by using vibrational optical coherence elastography (OCE)

Corneal wound healing, caused by frequent traumatic injury to the cornea and increasing numbers of refractive surgeries, has become a vital clinical problem. In the cornea, wound healing is an extremely complicated process. However, little is known about how the biomechanical changes in wound healing response of the cornea. Collagen-based hydrogels incorporating corneal cells are suitable for replicating a three-dimensional (3D) equivalent of the cornea in-vitro. In this study, the mechanical properties of corneal stroma models were quantitatively monitored by a vibrational optical coherence elastography (OCE) system during continuous culture periods. Specifically, human corneal keratocytes were seeded at 5 × 105 cells/mL in the hydrogels with a collagen concentration of 3.0 mg/mL. The elastic modulus of the unwounded constructs increased from 2.950 ± 0.2 kPa to 11.0 ± 1.4 kPa, and the maximum thickness decreased from 1.034 ± 0.1 mm to 0.464 ± 0.09 mm during a 15-day culture period. Furthermore, a traumatic wound in the construct was introduced with a size of 500 µm. The elastic modulus of the neo-tissue in the wound area increased from 1.488 ± 0.4 kPa to 6.639 ± 0.3 kPa over 13 days. This study demonstrates that the vibrational OCE system is capable of quantitative monitoring the changes in mechanical properties of a corneal stroma wound model during continuous culture periods and improves our understanding on corneal wound healing processes

Relational Self-Supervised Learning

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduce a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as \textit{relation} metric, which is thus utilized to match the feature embeddings of different augmentations. To boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. The designed asymmetric predictor head and an InfoNCE warm-up strategy enhance the robustness to hyper-parameters and benefit the resulting performance. Experimental results show that our proposed ReSSL substantially outperforms the state-of-the-art methods across different network architectures, including various lightweight networks (\eg, EfficientNet and MobileNet).Comment: Extended version of NeurIPS 2021 paper. arXiv admin note: substantial text overlap with arXiv:2107.0928

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.Comment: 14 pages, 13 figures, journal extension of FaceScape(CVPR 2020). arXiv admin note: substantial text overlap with arXiv:2003.1398

AGC regulation capability prediction and optimization of coal-fired thermal power plants

The improvement of the AGC regulation capability of thermal power plants is very important for the secure and stable operation of the power grid, especially in the situation of large-scale renewable energy access to the power grid. In this study, the prediction and optimization for the AGC regulation capability of thermal power plants is proposed. Firstly, considering parameters related to the AGC regulation of the thermal power plant, the max-relevance and min-redundancy (mRMR) is used to extract features from historical sequences of the parameters. Next, a model with multi-long short-term neural networks (mLSTM) is constructed to predict the AGC regulation capability; that is, the obtained feature set is considered as the inputs of the first LSTM sub-model to predict future values of the main steam pressure and main steam temperature, which are then utilized as the inputs of the second LSTM sub-model to predict the actual power generation during AGC regulation operation. Then, the AGC regulation index is calculated according to the “management rules of grid-connected operation of power plant in Northern China” and “management rules of auxiliary service of the grid-connected power plant in Northern China” (i.e., “two rules”), and it is then considered as the objective function to be maximized by optimizing the coal feed rate, air supply rate, and feedwater flow rate. Finally, the actual AGC regulation process of a 300 MW coal-fired power plant is used as an application, and the results show that the proposed method can effectively predict and improve the regulation capability when the AGC instruction is received from the power grid

Laser in Glaucoma and Ocular Hypertension Trial (LIGHT) in China - A Randomized Controlled Trial: Design and Baseline Characteristics

PURPOSE: To describe the baseline characteristics of a trial to evaluate whether selective laser trabeculoplasty (SLT), as a first-line treatment, provides superior economic and health-related quality of life outcomes to medical treatment in China. DESIGN: The LiGHT China trial is an unmasked, single-center, pragmatic, randomized controlled trial. METHODS: A total of 771 previously undiagnosed patients with primary open angle glaucoma (POAG, 622 patients) or ocular hypertension (OHT, 149 patients) at Zhongshan Ophthalmic Center were recruited from March 2015 to January 2019. Subjects were randomized to SLT-1st (followed by medication then surgery when required) or Medicine-1st (medication followed by surgery when required). The primary outcome was health-related quality of life (HRQL). The secondary outcomes were clinical outcomes, cost, cost-effectiveness, Glaucoma Utility Index, Glaucoma Symptom Scale, visual function, and safety. RESULTS: The mean age of POAG patients was 49.8 years and 38.8 years for OHT. The median intraocular pressure was 20 mm Hg for the 1,105 POAG eyes and 24 mm Hg for the 271 OHT eyes. POAG eyes had thinner central cornea thickness (CCT, 536 µm) than OHT eyes (545 µm). Median mean deviation of the visual field in POAG eyes was -4.2 dB. Median refractive error was -1.5 D for OHT eyes and -1.25 D for POAG eyes. There was no difference between POAG and OHT patients on baseline scores of GUI, GSS and VF-14. The difference between OHT and POAG on the EQ-5D-5L was 0.024. CONCLUSIONS: Compared with participants in the LiGHT UK trial, participants in this trial were younger, more myopic and had more severe visual field defects

Quantifying rainfall-derived inflow and infiltration in sanitary sewer systems based on conductivity monitoring

Quantifying rainfall-derived inflow and infiltration (RDII) in a sanitary sewer is difficult when RDII and overflow occur simultaneously. This study proposes a novel conductivity-based method for estimating RDII. The method separately decomposes rainfall-derived inflow (RDI) and rainfall-induced infiltration (RII) on the basis of conductivity data. Fast Fourier transform was adopted to analyze variations in the flow and water quality during dry weather. Nonlinear curve fitting based on the least squares algorithm was used to optimize parameters in the proposed RDII model. The method was successfully applied to real-life case studies, in which inflow and infiltration were successfully estimated for three typical rainfall events with total rainfall volumes of 6.25 mm (light), 28.15 mm (medium), and 178 mm (heavy). Uncertainties of model parameters were estimated using the generalized likelihood uncertainty estimation (GLUE) method and were found to be acceptable. Compared with traditional flow-based methods, the proposed approach exhibits distinct advantages in estimating RDII and overflow, particularly when the two processes happen simultaneously

Electron-Enriched Pd Nanoparticles for Selective Hydrogenation of Halonitrobenzenes to Haloanilines

Selective hydrogenation of halonitrobenzenes into haloanilines represents a green process to replace the environmentally unfriendly non-catalytic chemical reduction process in industry. However, this transformation often suffers from serious dehalogenation due to the easy break of carbon-halogen bonds on metal surfaces. Modulations of the electronic structure of the supported Pd nanoparticles on Lewis-basic layered double hydroxides have been demonstrated to promote catalytic activity and selectivity for hydrogenation of halonitrobenzenes into haloanilines. Mechanism studies suggest that Pd with the enhanced electron density not only improves the capability for hydrogen activation, but also generates the partially negative-charged hydrogen species to suppress the electrophilic attack on the carbon-halogen bond and avoid the dehalogenation