ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation

Automatic tissue segmentation of fetal brain images is essential for the quantitative analysis of prenatal neurodevelopment. However, producing voxel-level annotations of fetal brain imaging is time-consuming and expensive. To reduce labeling costs, we propose a practical unsupervised domain adaptation (UDA) setting that adapts the segmentation labels of high-quality fetal brain atlases to unlabeled fetal brain MRI data from another domain. To address the task, we propose a new UDA framework based on Appearance and Structure Consistency, named ASC. We adapt the segmentation model to the appearances of different domains by constraining the consistency before and after a frequency-based image transformation, which is to swap the appearance between brain MRI data and atlases. Consider that even in the same domain, the fetal brain images of different gestational ages could have significant variations in the anatomical structures. To make the model adapt to the structural variations in the target domain, we further encourage prediction consistency under different structural perturbations. Extensive experiments on FeTA 2021 benchmark demonstrate the effectiveness of our ASC in comparison to registration-based, semi-supervised learning-based, and existing UDA-based methods.Comment: MICCAI 2023, released code: https://github.com/lhaof/AS

A large LNG tank technology system “CGTank®” of CNOOC and its engineering application

AbstractLNG tanks are complex in design and building process and high in costs, so LNG tank technology is one of the most advanced ones in the field of energy, which has been monopolized by foreign companies for a long time. In order to work out LNG tank technology domestically, China National Offshore Oil Corporation (CNOOC for short), the largest LNG importer in China, develops a LNG tank technology system “CGTank®” successfully in reference to the design and construction experience of domestic and foreign companies, after years of scientific research in tackling difficult problems. This system presents four traits as follows. First, a set of calculation software is developed independently by CNOOC, and the tanks in all operating conditions are calculated after 3D hologram and multi-point contact model of fluid-solid coupling effect is built up. Second, earthquake effect research and inner tank check research are improved innovatively by means of response spectrum analysis after European standards are introduced. Third, it is put forward for the first time that the stress strength discrimination standard is based on the principal stress which is obtained by means of the maximum shearing failure theory. And fourth, a large LNG full-capacity tank technology package with completely independent intellectual property right is established. The “CGTank®” system was first applied in the Tianjin LNG demonstration project, which has passed all indicator tests and is now in operation smoothly. The project is provided with the core tank design technology by CNOOC Gas and Power Group and with the EPC by CNOOC Engineering Co., Ltd. The independent LNG tank technology can be applied in a wide scope and it is favorable for impelling domestic production of LNG industry completely

Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding

We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task. In the first stage, we propose an Augmented 2D Temporal Adjacent Network (Augmented 2D-TAN) to temporally ground the target moment corresponding to the given description. Primarily, we improve the original 2D-TAN from two aspects: First, a temporal context-aware Bi-LSTM Aggregation Module is developed to aggregate clip-level representations, replacing the original max-pooling. Second, we propose to employ Random Concatenation Augmentation (RCA) mechanism during the training phase. In the second stage, we use pretrained MDETR model to generate per-frame bounding boxes via language query, and design a set of hand-crafted rules to select the best matching bounding box outputted by MDETR for each frame within the grounded moment.Comment: Best Paper Award at the 3rd Person in Context (PIC) Challenge CVPR Workshop 202

Experimental Study of Granular Clogging in Two-Dimensional Hopper

We experimentally investigate the clogging process of granular materials in a two-dimensional hopper, and present a self-consistent physical mechanism of clogging based on preformed dynamic chain structures in the flow. We found that these chain structures follow a specific modified restricted random walk, and clogging occurs when they are mechanically stable enough to withstand the flow fluctuations, resulting in the formation of an arch at the outlet. We introduce a simple model which can explain the clogging probability by incorporating an analytical expression for chain formation and its transition into an arch. Our results provide insight into the microscopic mechanism of clogging in hopper flow.Comment: 22 pages, 8 figure

Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach

The district heating network (DHN) is essential in enhancing the operational flexibility of integrated energy systems (IES). Yet, it is hard to obtain an accurate and concise DHN model for the operation owing to complicated network features and imperfect measurement. Considering this, this paper proposes a physically informed data-driven aggregate model (AGM) for DHN, providing a concise description of the source-load relationship of DHN without exposing network details. First, we derive the analytical relationship between the state variables of the source and load nodes of DHN, offering a physical fundament for the AGM. Second, we propose a physics-informed estimator for AGM that is robust to low-quality measurement, in which the physical constraints associated with the parameter normalization and sparsity are embedded to improve the accuracy and robustness. Finally, we propose a physics-enhanced algorithm to solve the nonlinear estimator with non-closed constraints efficiently. Simulation results verify the effectiveness of the proposed method