22,857 results on '"WANG Shuo"'
Search Results
202. Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy.
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Huynh, Linda, Bonebrake, Benjamin, Tran, Joshua, Marasco, Jacob, Ahlering, Thomas, Wang, Shuo, and Baine, Michael
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machine learning ,prostate cancer ,radiomics ,recurrence - Abstract
The use of multiparametric magnetic resonance imaging (mpMRI)-derived radiomics has the potential to offer noninvasive, imaging-based biomarkers for the identification of subvisual characteristics indicative of a poor oncologic outcome. The present study, therefore, seeks to develop, validate, and assess the performance of an MRI-derived radiomic model for the prediction of prostate cancer (PC) recurrence following radical prostatectomy (RP) with curative intent. mpMRI imaging was obtained from 251 patients who had undergone an RP for the treatment of localized prostate cancer across two institutions and three surgeons. All patients had a minimum of 2 years follow-up via prostate-specific antigen serum testing. Each prostate mpMRI was individually reviewed, and the prostate was delineated as a single slice (ROI) on axial T2 high-resolution image sets. A total of 924 radiomic features were extracted and tested for stability via intraclass correlation coefficient (ICC) following image normalization via histogram matching. Fourteen important and nonredundant features were found to be predictors of PC recurrence at a mean ± SD of 3.2 ± 2.2 years post-RP. Five-fold, ten-run cross-validation of the model containing these fourteen features yielded an area under the curve (AUC) of 0.89 ± 0.04 in the training set (n = 225). In comparison, the University of California San Fransisco Cancer of the Prostate Risk Assessment score (UCSF-CAPRA) and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical prostatectomy nomograms yielded AUC of 0.66 ± 0.05 and 0.67 ± 0.05, respectively (p < 0.01). When the radiomic model was applied to the test set (n = 26), AUC was 0.78; sensitivity, specificity, positive predictive value, and negative predictive value were 60%, 86%, 52%, and 89%, respectively. Accuracy in predicting PC recurrence was 81%.
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- 2023
203. Functional connectivity between the amygdala and prefrontal cortex underlies processing of emotion ambiguity.
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Sun, Sai, Yu, Hongbo, Yu, Rongjun, and Wang, Shuo
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Humans ,Neural Pathways ,Magnetic Resonance Imaging ,Emotions ,Amygdala ,Prefrontal Cortex ,Brain Mapping ,Facial Expression - Abstract
Processing facial expressions of emotion draws on a distributed brain network. In particular, judging ambiguous facial emotions involves coordination between multiple brain areas. Here, we applied multimodal functional connectivity analysis to achieve network-level understanding of the neural mechanisms underlying perceptual ambiguity in facial expressions. We found directional effective connectivity between the amygdala, dorsomedial prefrontal cortex (dmPFC), and ventromedial PFC, supporting both bottom-up affective processes for ambiguity representation/perception and top-down cognitive processes for ambiguity resolution/decision. Direct recordings from the human neurosurgical patients showed that the responses of amygdala and dmPFC neurons were modulated by the level of emotion ambiguity, and amygdala neurons responded earlier than dmPFC neurons, reflecting the bottom-up process for ambiguity processing. We further found parietal-frontal coherence and delta-alpha cross-frequency coupling involved in encoding emotion ambiguity. We replicated the EEG coherence result using independent experiments and further showed modulation of the coherence. EEG source connectivity revealed that the dmPFC top-down regulated the activities in other brain regions. Lastly, we showed altered behavioral responses in neuropsychiatric patients who may have dysfunctions in amygdala-PFC functional connectivity. Together, using multimodal experimental and analytical approaches, we have delineated a neural network that underlies processing of emotion ambiguity.
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- 2023
204. Application of blockchain in integrated energy system transactions
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Liu Jianhua, Sun Shengbo, Chang Zheng, Zhou Bo, Wang Yongli, Wang Jingyan, and Wang Shuo
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Environmental sciences ,GE1-350 - Abstract
Blockchain technology is the underlying technology of Bitcoin, which is fair, transparent and decentralized. The integrated energy system has the characteristics of open interconnection, user-centered and distributed peer-to-peer sharing, and its energy trading model will also be developed centrally to distributed. The characteristics of blockchain technology make it naturally applicable to energy transactions in integrated energy systems. This article first analyzes the characteristics of the integrated energy system market and summarizes the participants in the market. Then, based on the existing research and analysis, a blockchain-based energy transaction architecture is designed, and a weakly centralized management method is introduced. finally, the problems and challenges faced by the application of blockchain in energy transactions are analyzed.
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- 2020
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205. Forecast of Power Grid Investment Scale Based on Support Vector Machine
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Wang Yongli, Lu Yanchao, Wang Jingyan, Wang Xiaohui, Wang Shuo, and Xue Lu
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Environmental sciences ,GE1-350 - Abstract
Economic transformation creates a new environment for grid investment. In the situation of high quality development, the traditional investment scale prediction model is no longer applicable. Aiming at the problems of single parameter of grid-driven investment scale prediction model and poor linear fitting accuracy, a provincial medium- and long-term investment scale prediction model based on support vector machine was proposed. Aiming at the single parameter and poor line fitting accuracy of the grid-driven investment scale prediction model under the new situation, the new environment, new directions and new requirements for grid investment are analyzed. Based on the support vector machine algorithm, a medium-and long-term investment scale prediction model for provincial grids based on support vector machines is proposed. The scale of provincial grid investment is expected from 2019 to 2022. The empirical results show that the prediction model constructed in this paper is effective and feasible. It is of great significance to explore the prediction model of medium and long-term investment scale of power grid enterprises in the new situation.
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- 2020
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206. Evaluation of Regional Power Grid Investment Capacity Based on Transmission and Distribution Price Reform
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Pan Dong, Wang Yongli, Wang Shuo, and Wang Jingyan
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Environmental sciences ,GE1-350 - Abstract
The transmission and distribution price reform has changed the profit model of the power grid, and a reasonable assessment of the investment capacity of the regional power grid is critical to the future investment planning of the power grid. To this end, first of all, sort out the key index system of power grid investment capacity for transmission and distribution price reform. Then comprehensively consider the available distribution profit, depreciation and external financing of the power grid, and establish a quantitative model of power grid investment capacity; Finally, the ability to invest in the establishment of quantitative models to measure the ability of a power grid investment companies, investment capacity is calculated after the reform of the electricity transmission and distribution, in order to verify the ability to quantify the investment rationality and effectiveness of the model. Finally, taking three power companies as examples, the feasibility of the model and the rationality of investment capacity evaluation are verified.
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- 2020
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207. Research on Operation Optimization Strategy of Integrated Energy System Considering Ground Source Heat Pump
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Wang Linfeng, Zhang Kai, Xu Nan, Wang Jingyan, Zhang Danyang, Wang Shuo, and Wang Yongli
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Environmental sciences ,GE1-350 - Abstract
With the depletion of fossil energy and the popularity of renewable energy, a comprehensive energy system with the goal of improving system energy efficiency and consuming renewable energy is booming. Based on the combined heat, power, and heat generation, this paper builds a comprehensive energy system operation optimization model in conjunction with ground source heat pumps. It aims to find the optimal operation strategy based on the actual situation of the park’s load, equipment capacity, and energy prices. Using the linear programming method, a mathematical model with the best economic efficiency of the integrated energy system is established, the optimal operation strategy for a typical day is analyzed, and the annual operation is simulated. Finally, it compares with conventional energy supply methods and analyzes the contribution to the consumption of renewable energy.
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- 2020
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208. Geospatial Analysis of Emerging Drought Risk in a Warming Climate
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Wang, Shuo, primary and Qing, Yamin, additional
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- 2024
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209. Hydrogels for Drug Delivery
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Wang, Shuo, primary, Chen, Xiaobao, additional, Meng, Meng, additional, and Wang, Wei, additional
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- 2024
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210. Mechanism design for the end-to-end deterministic transmissions with decoupled time domains
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Wu, Binwei, Wang, Shuo, and Tan, Weiqian
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Computer Science - Networking and Internet Architecture - Abstract
This paper proposes an innovative end-to-end deterministic network mechanism to achieve delay-bounded transmissions across multiple network domains. The proposed mechanism installs discrete shapers at the edge of the network domains, which serves to decouple the clock domains of different networks. Thereby, the challenges associated with cross-domain clock synchronization that are inherent in state-of-the-art deterministic mechanisms are mitigated, e.g., high complexity during the system implementation and the traffic scheduling. Moreover, the proposed mechanism enhances the availability of the deterministic networking, i.e., not only periodic deterministic traffic, but also aperiodic deterministic traffic and stochastic flows are enabled to be served. Furthermore, an auction-based online scheduling algorithm is developed to improve network efficiency and reduce cost. Simulation results show that the proposed mechanism can effectively realize the end-to-end delay-bounded transmission across multiple domains. Meanwhile, the cross-domain latency could also be reduced compared to the existing methods., Comment: in Chinese language
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- 2023
211. AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports
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Sun, Hongyu, Wang, Yongcai, Cai, Xudong, Wang, Peng, Huang, Zhe, Li, Deying, Shao, Yu, and Wang, Shuo
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Computer Science - Computer Vision and Pattern Recognition - Abstract
One fundamental limitation to the research of bird strike prevention is the lack of a large-scale dataset taken directly from real-world airports. Existing relevant datasets are either small in size or not dedicated for this purpose. To advance the research and practical solutions for bird strike prevention, in this paper, we present a large-scale challenging dataset AirBirds that consists of 118,312 time-series images, where a total of 409,967 bounding boxes of flying birds are manually, carefully annotated. The average size of all annotated instances is smaller than 10 pixels in 1920x1080 images. Images in the dataset are captured over 4 seasons of a whole year by a network of cameras deployed at a real-world airport, covering diverse bird species, lighting conditions and 13 meteorological scenarios. To the best of our knowledge, it is the first large-scale image dataset that directly collects flying birds in real-world airports for bird strike prevention. This dataset is publicly available at https://airbirdsdata.github.io/., Comment: 17 pages, 9 figures, 3 tables; accepted by ACCV 2022
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- 2023
212. IDLS: Inverse Depth Line based Visual-Inertial SLAM
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Li, Wanting, Wang, Shuo, Wang, Yongcai, Shao, Yu, Bai, Xuewei, and Li, Deying
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Robotics - Abstract
For robust visual-inertial SLAM in perceptually-challenging indoor environments,recent studies exploit line features to extract descriptive information about scene structure to deal with the degeneracy of point features. But existing point-line-based SLAM methods mainly use Pl\"ucker matrix or orthogonal representation to represent a line, which needs to calculate at least four variables to determine a line. Given the numerous line features to determine in each frame, the overly flexible line representation increases the computation burden and comprises the accuracy of the results. In this paper, we propose inverse depth representation for a line, which models each extracted line feature using only two variables, i.e., the inverse depths of the two ending points. It exploits the fact that the projected line's pixel coordinates on the image plane are rather accurate, which partially restrict the line. Using this compact line presentation, Inverse Depth Line SLAM (IDLS) is proposed to track the line features in SLAM in an accurate and efficient way. A robust line triangulation method and a novel line re-projection error model are introduced. And a two-step optimization method is proposed to firstly determine the lines and then to estimate the camera poses in each frame. IDLS is extensively evaluated in multiple perceptually-challenging datasets. The results show it is more accurate, robust, and needs lower computational overhead than the current state-of-the-art of point-line-based SLAM methods.
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- 2023
213. Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
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Zhang, Yizhe, Zhou, Tao, Wang, Shuo, Liang, Peixian, and Chen, Danny Z.
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. This paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method. The code is available at \url{https://github.com/yizhezhang2000/SAMAug}., Comment: GitHub: https://github.com/yizhezhang2000/SAMAug. Comments and questions are welcome
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- 2023
214. Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis
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Ruan, Mindi, Yu, Xiangxu, Zhang, Na, Hu, Chuanbo, Wang, Shuo, and Li, Xin
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Computer Science - Computer Vision and Pattern Recognition - Abstract
How can we teach a computer to recognize 10,000 different actions? Deep learning has evolved from supervised and unsupervised to self-supervised approaches. In this paper, we present a new contrastive learning-based framework for decision tree-based classification of actions, including human-human interactions (HHI) and human-object interactions (HOI). The key idea is to translate the original multi-class action recognition into a series of binary classification tasks on a pre-constructed decision tree. Under the new framework of contrastive learning, we present the design of an interaction adjacent matrix (IAM) with skeleton graphs as the backbone for modeling various action-related attributes such as periodicity and symmetry. Through the construction of various pretext tasks, we obtain a series of binary classification nodes on the decision tree that can be combined to support higher-level recognition tasks. Experimental justification for the potential of our approach in real-world applications ranges from interaction recognition to symmetry detection. In particular, we have demonstrated the promising performance of video-based autism spectrum disorder (ASD) diagnosis on the CalTech interview video database., Comment: Accepted by ACM Multimedia Systems Conference 2023
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- 2023
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215. The 7th AI City Challenge
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Naphade, Milind, Wang, Shuo, Anastasiu, David C., Tang, Zheng, Chang, Ming-Ching, Yao, Yue, Zheng, Liang, Rahman, Mohammed Shaiqur, Arya, Meenakshi S., Sharma, Anuj, Feng, Qi, Ablavsky, Vitaly, Sclaroff, Stan, Chakraborty, Pranamesh, Prajapati, Sanjita, Li, Alice, Li, Shangru, Kunadharaju, Krishna, Jiang, Shenxin, and Chellappa, Rama
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential. The 2023 challenge had five tracks, which drew a record-breaking number of participation requests from 508 teams across 46 countries. Track 1 was a brand new track that focused on multi-target multi-camera (MTMC) people tracking, where teams trained and evaluated using both real and highly realistic synthetic data. Track 2 centered around natural-language-based vehicle track retrieval. Track 3 required teams to classify driver actions in naturalistic driving analysis. Track 4 aimed to develop an automated checkout system for retail stores using a single view camera. Track 5, another new addition, tasked teams with detecting violations of the helmet rule for motorcyclists. Two leader boards were released for submissions based on different methods: a public leader board for the contest where external private data wasn't allowed and a general leader board for all results submitted. The participating teams' top performances established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks., Comment: Summary of the 7th AI City Challenge Workshop in conjunction with CVPR 2023
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- 2023
216. CamDiff: Camouflage Image Augmentation via Diffusion Model
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Luo, Xue-Jing, Wang, Shuo, Wu, Zongwei, Sakaridis, Christos, Cheng, Yun, Fan, Deng-Ping, and Van Gool, Luc
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The burgeoning field of camouflaged object detection (COD) seeks to identify objects that blend into their surroundings. Despite the impressive performance of recent models, we have identified a limitation in their robustness, where existing methods may misclassify salient objects as camouflaged ones, despite these two characteristics being contradictory. This limitation may stem from lacking multi-pattern training images, leading to less saliency robustness. To address this issue, we introduce CamDiff, a novel approach inspired by AI-Generated Content (AIGC) that overcomes the scarcity of multi-pattern training images. Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt. Consequently, the synthesized image retains its original camouflage label while incorporating salient objects, yielding camouflage samples with richer characteristics. The results of user studies show that the salient objects in the scenes synthesized by our framework attract the user's attention more; thus, such samples pose a greater challenge to the existing COD models. Our approach enables flexible editing and efficient large-scale dataset generation at a low cost. It significantly enhances COD baselines' training and testing phases, emphasizing robustness across diverse domains. Our newly-generated datasets and source code are available at https://github.com/drlxj/CamDiff.
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- 2023
217. Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers
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Huang, Zhou, Dai, Hang, Xiang, Tian-Zhu, Wang, Shuo, Chen, Huai-Xin, Qin, Jie, and Xiong, Huan
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggregation in decoders, which are not conducive to camouflaged object detection that explores subtle cues from indistinguishable backgrounds. To address these issues, in this paper, we propose a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrinking for camouflaged object detection. Specifically, we propose a nonlocal token enhancement module (NL-TEM) that employs the non-local mechanism to interact neighboring tokens and explore graph-based high-order relations within tokens to enhance local representations of transformers. Moreover, we design a feature shrinkage decoder (FSD) with adjacent interaction modules (AIM), which progressively aggregates adjacent transformer features through a layer-bylayer shrinkage pyramid to accumulate imperceptible but effective cues as much as possible for object information decoding. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing 24 competitors on three challenging COD benchmark datasets under six widely-used evaluation metrics. Our code is publicly available at https://github.com/ZhouHuang23/FSPNet., Comment: CVPR 2023. Project webpage at: https://tzxiang.github.io/project/COD-FSPNet/index.html
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- 2023
218. AI-Generated Network Design: A Diffusion Model-based Learning Approach
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Huang, Yudong, Xu, Minrui, Zhang, Xinyuan, Niyato, Dusit, Xiong, Zehui, Wang, Shuo, and Huang, Tao
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Computer Science - Networking and Internet Architecture - Abstract
The future networks pose intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirements, and complicated manual configuration. However, traditional rule-based solutions heavily rely on human efforts and expertise, while data-driven intelligent algorithms still lack interpretability and generalization. In this paper, we propose the AIGN (AI-Generated Network), a novel intention-driven paradigm for network design, which allows operators to quickly generate a variety of customized network solutions and achieve expert-free problem optimization. Driven by the diffusion model-based learning approach, AIGN has great potential to learn the reward-maximizing trajectories, automatically satisfy multiple constraints, adapt to different objectives and scenarios, or even intelligently create novel designs and mechanisms unseen in existing network environments. Finally, we conduct a use case to demonstrate that AIGN can effectively guide the design of transmit power allocation in digital twin-based access networks., Comment: 7 pages, 3 figures
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- 2023
219. Two-fold symmetric superconductivity in the kagome superconductor RbV3Sb5
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Wang, Shuo, Wu, Ze-Nan, Fang, Jing-Zhi, Wei, Zhongming, Wang, Zhiwei, Huang, Wen, Yao, Yugui, Yang, Jia-Jie, Lin, Ben-Chuan, and Yu, Dapeng
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Condensed Matter - Superconductivity ,Condensed Matter - Strongly Correlated Electrons - Abstract
The recently discovered kagome superconductors offer a promising platform for investigating intertwined orders and novel states, including topology, superconductivity, charge density waves, and more. The interplay among these orders can spontaneously break rotational symmetry, giving rise to exotic phenomena such as nematicity or even nematic superconductivity. Here we present our findings on the two-fold symmetric superconductivity in thin-flake RbV3Sb5 in response to direction-dependent in-plane magnetic fields, in contrast to the inherent six-fold structural symmetry of the crystal lattice. The two-fold symmetry was evidenced through a combination of magnetoresistance transport experiments, critical magnetic field measurements, and observations of anisotropic superconducting gaps. Additionally, by altering the experimental configuration, we also detected the presence of six-fold symmetric components superimposed on the two-fold symmetry at the boundary between normal and superconducting states. Our results underscore the correlation-driven symmetry-breaking phenomena and emphasize the potential of this correlated kagome family as a promising platform for investigating intertwined orders, including unconventional superconductivity.
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- 2023
220. Detect influential points of feature rankings
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Wang, Shuo and Lu, Junyan
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Statistics - Applications ,Statistics - Methodology - Abstract
Background Deriving feature rankings is essential in bioinformatics studies since the ordered features are important in guiding subsequent research. Feature rankings may be distorted by influential points (IP), but such effects are rarely mentioned in previous studies. This study aimed to investigate the impact of IPs on feature rankings and propose a new method to detect IPs. Method The present study utilized a case-deletion (i.e., leave-one-out) approach to assess the impact of cases. The influence of a case was measured by comparing the rank changes before and after the deletion of that case. We proposed a rank comparison method using adaptive top-prioritized weights that highlighted the rank changes of the top-ranked features. The weights were adjustable to the distribution of rank changes. Results Potential IPs could be observed in several datasets. The presence of IPs could significantly alter the results of the following analysis (e.g., enriched pathways), suggesting the necessity of IPs detection when deriving feature rankings. Compared with existing methods, the novel rank comparison method could identify rank changes of important (top-ranked) features because of employing the adaptive weights adjusted to the distribution of rank changes. Conclusions IPs detection should be routinely performed when deriving feature rankings. The new method for IPs detection exhibited favorable features compared with existing methods.
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- 2023
221. Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children's Apps
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Sun, Ruoxi, Xue, Minhui, Tyson, Gareth, Wang, Shuo, Camtepe, Seyit, and Nepal, Surya
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Computer Science - Cryptography and Security - Abstract
The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world's most marginalized children. However, while legislatures around the world have enacted regulations to protect children's online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern for parents and wider society. In this paper, we explore the potential privacy issues and threats that exist in these apps. We investigate 20,195 mobile apps from the Google Play store that are designed particularly for children (Family apps) or include children in their target user groups (Normal apps). Using both static and dynamic analysis, we find that 4.47% of Family apps request location permissions, even though collecting location information from children is forbidden by the Play store, and 81.25% of Family apps use trackers (which are not allowed in children's apps). Even major developers with 40+ kids apps on the Play store use ad trackers. Furthermore, we find that most permission request notifications are not well designed for children, and 19.25% apps have inconsistent content age ratings across the different protection authorities. Our findings suggest that, despite significant attention to children's privacy, a large gap between regulatory provisions, app store policies, and actual development practices exist. Our research sheds light for government policymakers, app stores, and developers., Comment: Accepted at the Web Conference 2023
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- 2023
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222. Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
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Wang, Zhicai, Hao, Yanbin, Mu, Tingting, Li, Ouxiang, Wang, Shuo, and He, Xiangnan
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match. Although transductive ZSL (TZSL) attempts to improve this by allowing the use of unlabelled examples from the unseen classes, there is still a high level of distribution shift. We propose a novel TZSL model (named as Bi-VAEGAN), which largely improves the shift by a strengthened distribution alignment between the visual and auxiliary spaces. The key proposal of the model design includes (1) a bi-directional distribution alignment, (2) a simple but effective L_2-norm based feature normalization approach, and (3) a more sophisticated unseen class prior estimation approach. In benchmark evaluation using four datasets, Bi-VAEGAN achieves the new state of the arts under both the standard and generalized TZSL settings. Code could be found at https://github.com/Zhicaiwww/Bi-VAEGAN, Comment: CVPR2023
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- 2023
223. Blockchain Based Spectrum Sharing
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Sun, Chen and Wang, Shuo
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Computer Science - Networking and Internet Architecture ,Computer Science - Databases - Abstract
Spectrum sharing has long been considered as method to improve spectrum resource utilization. Centralized geolocation database approach has been accepted globally for commercial applications. Recently blockchain has been considered as a platform to support spectrum sharing in a distributed manner. Like other commodities, spectrum or right of spectrum usage can be exchanged through blockchain. However, this leads to changes of the location where a particular frequency channel is utilized which brings potential interference. In this paper, we present the interference based consensus and smart contract for blockchain based spectrum sharing. We will also introduce the other aspects of blockchain such as cross train transaction, cross tier spectrum coordination and incentive mechanism that need to be studied for spectrum sharing applications.
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- 2023
224. Toward a Geometric Theory of Manifold Untangling
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Li, Xin and Wang, Shuo
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Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition - Abstract
It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling. A mathematical abstraction of object recognition by the visual cortex is how to untangle the manifolds associated with different object category. Such a manifold untangling problem is closely related to the celebrated kernel trick in metric space. In this paper, we conjecture that there is a more general solution to manifold untangling in the topological space without artificially defining any distance metric. Geometrically, we can either $embed$ a manifold in a higher dimensional space to promote selectivity or $flatten$ a manifold to promote tolerance. General strategies of both global manifold embedding and local manifold flattening are presented and connected with existing work on the untangling of image, audio, and language data. We also discuss the implications of untangling the manifold into motor control and internal representations.
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- 2023
225. Toward NeuroDM: Where Computational Neuroscience Meets Data Mining
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Li, Xin, Liu, Bin, and Wang, Shuo
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Quantitative Biology - Neurons and Cognition ,Computer Science - Neural and Evolutionary Computing - Abstract
At the intersection of computational neuroscience (CN) and data mining (DM), we advocate a holistic view toward their rich connections. On the one hand, fundamental concepts in neuroscience such as saliency, memory, and emotion can find novel applications in data mining. On the other hand, multimodal imaging has opened the door for data mining to facilitate the extraction of important cognitive and behavioral information from multimodal neural data. By NeuroDM, we advocate for more collaboration between CN and DM to expedite the advances in two well-established fields. The analogy between the over-parameterization of biological and artificial neural networks might suggest a unifying perspective of advancing both fields.
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- 2023
226. Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View
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Wang, Shuo, Zhao, Xinhai, Xu, Hai-Ming, Chen, Zehui, Yu, Dameng, Chang, Jiahao, Yang, Zhen, and Zhao, Feng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Multi-view 3D object detection (MV3D-Det) in Bird-Eye-View (BEV) has drawn extensive attention due to its low cost and high efficiency. Although new algorithms for camera-only 3D object detection have been continuously proposed, most of them may risk drastic performance degradation when the domain of input images differs from that of training. In this paper, we first analyze the causes of the domain gap for the MV3D-Det task. Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation. To acquire a robust depth prediction, we propose to decouple the depth estimation from the intrinsic parameters of the camera (i.e. the focal length) through converting the prediction of metric depth to that of scale-invariant depth and perform dynamic perspective augmentation to increase the diversity of the extrinsic parameters (i.e. the camera poses) by utilizing homography. Moreover, we modify the focal length values to create multiple pseudo-domains and construct an adversarial training loss to encourage the feature representation to be more domain-agnostic. Without bells and whistles, our approach, namely DG-BEV, successfully alleviates the performance drop on the unseen target domain without impairing the accuracy of the source domain. Extensive experiments on various public datasets, including Waymo, nuScenes, and Lyft, demonstrate the generalization and effectiveness of our approach. To the best of our knowledge, this is the first systematic study to explore a domain generalization method for MV3D-Det., Comment: Accepted to CVPR 2023
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- 2023
227. Fight fire with fire: why not be more tolerant of ChatGPT in academic writing?
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Wang, Shuo and Yokoyama, Hiromi M.
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- 2024
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228. Growing Threats From Swings Between Hot and Wet Extremes in a Warmer World
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You, Jiewen, Wang, Shuo, Zhang, Boen, Raymond, Colin, and Matthews, Tom
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Earth Sciences ,Atmospheric Sciences ,Climate Action ,heat ,pluvial ,compound events ,Meteorology & Atmospheric Sciences - Abstract
The abrupt alternation between hot and wet extremes can lead to more severe societal impacts than isolated extremes. However, despite an understanding of hot and wet extremes separately, their temporally compounding characteristics are not well examined yet. Our study presents a comprehensive assessment of successive heat-pluvial and pluvial-heat events globally. We find that these successive extremes within a week occur every 6–7 years on average within warm seasons during 1956–2015, about 15% more often than would be expected by chance, and that they have a significant increase in frequency of about 22% per decade due to warming. We further investigate the role of vapor pressure deficit (VPD) and find that heat-pluvial (pluvial-heat) events are linked to negative (positive) VPD anomalies. Our results are statistically significant based on moving-blocks bootstrap resampling and field significance tests, highlighting these methods' importance in robustly identifying compound events under autocorrelation and multiple-testing conditions.
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- 2023
229. Cognitive and neural bases of visual-context-guided decision-making.
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Sun, Sai, Wang, Shuo, Yu, Rongjun, and Yu, Hongbo
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Attention ,Behavioral adjustment ,Outcome evaluation ,Striatum ,Subjective valuation ,Visual salience ,Humans ,Decision Making ,Attention ,Ventral Striatum ,Neostriatum ,Cognition ,Reward - Abstract
Humans adjust their behavioral strategies based on feedback, a process that may depend on intrinsic preferences and contextual factors such as visual salience. In this study, we hypothesized that decision-making based on visual salience is influenced by habitual and goal-directed processes, which can be evidenced by changes in attention and subjective valuation systems. To test this hypothesis, we conducted a series of studies to investigate the behavioral and neural mechanisms underlying visual salience-driven decision-making. We first established the baseline behavioral strategy without salience in Experiment 1 (n = 21). We then highlighted the utility or performance dimension of the chosen outcome using colors in Experiment 2 (n = 30). We demonstrated that the difference in staying frequency increased along the salient dimension, confirming a salience effect. Furthermore, the salience effect was abolished when directional information was removed in Experiment 3 (n = 28), suggesting that the salience effect is feedback-specific. To generalize our findings, we replicated the feedback-specific salience effects using eye-tracking and text emphasis. The fixation differences between the chosen and unchosen values were enhanced along the feedback-specific salient dimension in Experiment 4 (n = 48) but unchanged after removing feedback-specific information in Experiment 5 (n = 32). Moreover, the staying frequency was correlated with fixation properties, confirming that salience guides attention deployment. Lastly, our neuroimaging study (Experiment 6, n = 25) showed that the striatum subregions encoded salience-based outcome evaluation, while the vmPFC encoded salience-based behavioral adjustments. The connectivity of the vmPFC-ventral striatum accounted for individual differences in utility-driven, whereas the vmPFC-dmPFC for performance-driven behavioral adjustments. Together, our results provide a neurocognitive account of how task-irrelevant visual salience drives decision-making by involving attention and the frontal-striatal valuation systems. PUBLIC SIGNIFICANCE STATEMENT: Humans may use the current outcome to make behavior adjustments. How this occurs may depend on stable individual preferences and contextual factors, such as visual salience. Under the hypothesis that visual salience determines attention and subsequently modulates subjective valuation, we investigated the underlying behavioral and neural bases of visual-context-guided outcome evaluation and behavioral adjustments. Our findings suggest that the reward system is orchestrated by visual context and highlight the critical role of attention and the frontal-striatal neural circuit in visual-context-guided decision-making that may involve habitual and goal-directed processes.
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- 2023
230. CTLA-4 blockade induces tumor pyroptosis via CD8+ T cells in head and neck squamous cell carcinoma.
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Wang, Shuo, Wu, Zhi-Zhong, Zhu, Su-Wen, Wan, Shu-Cheng, Zhang, Meng-Jie, Zhang, Bo-Xin, Yang, Qi-Chao, Xiao, Yao, Li, Hao, Mao, Liang, Wang, Zhi-Yong, Gutkind, Jorge, and Sun, Zhi-Jun
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CD8(+) T cells ,CTLA-4 blockade ,IFN-γ ,TNF-α ,gasdermins ,head and neck squamous cell carcinoma ,immune checkpoint blockade ,pyroptosis ,Mice ,Animals ,Squamous Cell Carcinoma of Head and Neck ,CD8-Positive T-Lymphocytes ,CTLA-4 Antigen ,Tumor Necrosis Factor-alpha ,Pyroptosis ,Gasdermins ,Cytokines ,Interferon-gamma ,Head and Neck Neoplasms ,Tumor Microenvironment - Abstract
Immune checkpoint blockade (ICB) treatment has demonstrated excellent medical effects in oncology, and it is one of the most sought after immunotherapies for tumors. However, there are several issues with ICB therapy, including low response rates and a lack of effective efficacy predictors. Gasdermin-mediated pyroptosis is a typical inflammatory death mode. We discovered that increased expression of gasdermin protein was linked to a favorable tumor immune microenvironment and prognosis in head and neck squamous cell carcinoma (HNSCC). We used the mouse HNSCC cell lines 4MOSC1 (responsive to CTLA-4 blockade) and 4MOSC2 (resistant to CTLA-4 blockade) orthotopic models and demonstrated that CTLA-4 blockade treatment induced gasdermin-mediated pyroptosis of tumor cells, and gasdermin expression positively correlated to the effectiveness of CTLA-4 blockade treatment. We found that CTLA-4 blockade activated CD8+ T cells and increased the levels of interferon γ (IFN-γ) and tumor necrosis factor α (TNF-α) cytokines in the tumor microenvironment. These cytokines synergistically activated the STAT1/IRF1 axis to trigger tumor cell pyroptosis and the release of large amounts of inflammatory substances and chemokines. Collectively, our findings revealed that CTLA-4 blockade triggered tumor cells pyroptosis via the release of IFN-γ and TNF-α from activated CD8+ T cells, providing a new perspective of ICB.
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- 2023
231. Deep Learning Synthetic Strain: Quantitative Assessment of Regional Myocardial Wall Motion at MRI.
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Masutani, Evan M, Chandrupatla, Rahul S, Wang, Shuo, Zocchi, Chiara, Hahn, Lewis D, Horowitz, Michael, Jacobs, Kathleen, Kligerman, Seth, Raimondi, Francesca, Patel, Amit, and Hsiao, Albert
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Cardiac ,Ischemia/Infarction ,MR Imaging ,Neural Networks ,Cardiovascular ,Heart Disease ,Heart Disease - Coronary Heart Disease ,Biomedical Imaging ,Clinical Research ,Detection ,screening and diagnosis ,4.2 Evaluation of markers and technologies - Abstract
PurposeTo assess the feasibility of a newly developed algorithm, called deep learning synthetic strain (DLSS), to infer myocardial velocity from cine steady-state free precession (SSFP) images and detect wall motion abnormalities in patients with ischemic heart disease.Materials and methodsIn this retrospective study, DLSS was developed by using a data set of 223 cardiac MRI examinations including cine SSFP images and four-dimensional flow velocity data (November 2017 to May 2021). To establish normal ranges, segmental strain was measured in 40 individuals (mean age, 41 years ± 17 [SD]; 30 men) without cardiac disease. Then, DLSS performance in the detection of wall motion abnormalities was assessed in a separate group of patients with coronary artery disease, and these findings were compared with consensus results of four independent cardiothoracic radiologists (ground truth). Algorithm performance was evaluated by using receiver operating characteristic curve analysis.ResultsMedian peak segmental radial strain in individuals with normal cardiac MRI findings was 38% (IQR: 30%-48%). Among patients with ischemic heart disease (846 segments in 53 patients; mean age, 61 years ± 12; 41 men), the Cohen κ among four cardiothoracic readers for detecting wall motion abnormalities was 0.60-0.78. DLSS achieved an area under the receiver operating characteristic curve of 0.90. Using a fixed 30% threshold for abnormal peak radial strain, the algorithm achieved a sensitivity, specificity, and accuracy of 86%, 85%, and 86%, respectively.ConclusionThe deep learning algorithm had comparable performance with subspecialty radiologists in inferring myocardial velocity from cine SSFP images and identifying myocardial wall motion abnormalities at rest in patients with ischemic heart disease.Keywords: Neural Networks, Cardiac, MR Imaging, Ischemia/Infarction Supplemental material is available for this article. © RSNA, 2023.
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- 2023
232. Shone’s Complex
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Wang, Shuo, Wong, Pierre C., Anderson, Robert H., editor, Backer, Carl L., editor, Berger, Stuart, editor, Blom, Nico A., editor, Holzer, Ralf J., editor, Robinson, Joshua D., editor, and Abdulla, Ra-id, Editor-in-Chief
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- 2024
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233. Radiomics and Multiomics Research
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Dong, Di, Liu, Shengyuan, Liu, Zhenyu, Mu, Wei, Wang, Shuo, Shao, Lizhi, Li, Bao, Wei, Jingwei, Jiang, Yuming, Wan, Xiangbo, Tian, Jie, and Liu, Shiyuan, editor
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- 2024
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234. OCP: An Online Contingent Planning Method for Robot Tasks with Incomplete Knowledge
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Xiao, Zhiqun, Yang, Shuo, Xue, Yuanzhou, Wang, Shuo, Mao, Xinjun, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Xiankun, editor, and Zhang, Chuanlei, editor
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- 2024
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235. A Dynamic Collaborative Recommendation Method Based on Multimodal Fusion
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Wang, Shuo, Yang, Yue, Yang, Jing, Liu, Jiaqi, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Xiankun, editor, and Zhang, Qinhu, editor
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- 2024
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236. ReVFed: Representation-Based Privacy-Preserving Vertical Federated Learning with Heterogeneous Models
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Wang, Shuo, Yu, Jing, Gai, Keke, Zhu, Liehuang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Cungeng, editor, Chen, Huajun, editor, Zhao, Liang, editor, Arshad, Junaid, editor, Asyhari, Taufiq, editor, and Wang, Yonghao, editor
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- 2024
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237. Analysis on the Rural Logistics Development Model and the Design of Evaluation Index System for Rural Logistics Development Level Under the Background of E-commerce in China
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Wang, Shuo, Lv, Zhengwei, Barbosa-Povoa, Ana Paula, Editorial Board Member, de Almeida, Adiel Teixeira, Editorial Board Member, Gans, Noah, Editorial Board Member, Gupta, Jatinder N. D., Editorial Board Member, Heim, Gregory R., Editorial Board Member, Hua, Guowei, Editorial Board Member, Kimms, Alf, Editorial Board Member, Li, Xiang, Editorial Board Member, Masri, Hatem, Editorial Board Member, Nickel, Stefan, Editorial Board Member, Qiu, Robin, Editorial Board Member, Shankar, Ravi, Editorial Board Member, Slowiński, Roman, Editorial Board Member, Tang, Christopher S., Editorial Board Member, Wu, Yuzhe, Editorial Board Member, Zhu, Joe, Editorial Board Member, Zopounidis, Constantin, Editorial Board Member, Gong, Daqing, editor, Ma, Yixuan, editor, Fu, Xiaowen, editor, Zhang, Juliang, editor, and Shang, Xiaopu, editor
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- 2024
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238. Combine Synergetic Approach with Multi-scale Feature Fusion for Boosting Abdominal Multi-organ and Pan-Cancer Segmentation
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Wang, Shuo, Peng, Yanjun, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Ma, Jun, editor, and Wang, Bo, editor
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- 2024
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239. Local Attention Augmentation for Chinese Spelling Correction
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Wang, Shuo, Tong, Chaodong, Peng, Kun, Jiang, Lei, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Franco, Leonardo, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M. A., editor
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- 2024
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240. Research on BO-CNN Based Tool Wear Status Monitoring Method
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Wang, Shuo, Yu, Zhenliang, Zhang, Jian, Zhou, Liyao, Zhang, Wenwu, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Carbone, Giuseppe, editor, and Laribi, Med Amine, editor
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- 2024
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241. Research on Fault Prediction of Electric Spindle in Five-Axis Machining Center Based on Bayes-SVM
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Wang, Shuo, Yu, Zhenliang, Zhang, Wenwu, Zhang, Jian, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Carbone, Giuseppe, editor, and Laribi, Med Amine, editor
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- 2024
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242. Dynamic Development Characteristics of Shale Oil Reservoir Pores in Dongying Depression
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Chen, Ting, Yu, Chun-lei, Sun, Zhi-gang, Wang, Shuo-zhen, Gao, Yun-long, Wu, Wei, Series Editor, and Lin, Jia'en, editor
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- 2024
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243. Prediction Techniques and Application Effects of Sweet Spots in Transitional Marine-Land Shale Gas Reservoirs at the Eastern Margin of the Ordos Basin
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Li, Xiang, Li, Xing-tao, Dang, Hu-qiang, Hu, Xiao-dan, Gao, Feng, Wang, Shuo, Wu, Wei, Series Editor, and Lin, Jia'en, editor
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- 2024
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244. An Efficient and Simple Battery Wireless Charging System with Re-configurable Rectifier
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Wang, Youzheng, Liu, Hongchen, Yu, Huiying, Wang, Shuyu, Wang, Shuo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Cai, Chunwei, editor, Qu, Xiaohui, editor, Mai, Ruikun, editor, Zhang, Pengcheng, editor, Chai, Wenping, editor, and Wu, Shuai, editor
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- 2024
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245. Exploration and Application of Seismic Geosteerable Drilling Technology for Coalbed Methane Horizontal Wells
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Jia, Xue-cheng, Wang, Yuan, Zhang, Xiao-min, Wang, Shuo, Zhang, Feng, Sun, Xue-dong, Zhang, Bao-quan, Wu, Wei, Series Editor, and Lin, Jia'en, editor
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- 2024
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246. A Comparative Study of Pre-Stack Fracture Prediction Methods for Coal Bed Methane
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Zhang, Feng, Zhang, Xiao-min, Jiang, Hong-yu, Jia, Xue-cheng, Wang, Shuo, Zhang, Bao-quan, Wu, Wei, Series Editor, and Lin, Jia'en, editor
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- 2024
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247. Hierarchical Supervised Contrastive Learning for Multimodal Sentiment Analysis
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Chen, Kezhou, Wang, Shuo, Hao, Yanbin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rudinac, Stevan, editor, Hanjalic, Alan, editor, Liem, Cynthia, editor, Worring, Marcel, editor, Jónsson, Björn Þór, editor, Liu, Bei, editor, and Yamakata, Yoko, editor
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- 2024
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248. Adaptive joint placement of edge intelligence services in mobile edge computing
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Du, Lei, Huo, Ru, Sun, Chuang, Wang, Shuo, and Huang, Tao
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- 2024
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249. Early diagnosis and prognostic potential of RAC3 in bladder tumor
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Wang, Shuo, Wei, Zhuo, Shu, Hui, Xu, Yandong, Fan, Zheqi, Shuang, Songtao, Li, Pei, Lu, Pan, and Ye, Chang
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- 2024
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250. Transforming detrimental intermetallics by accumulative thermal and strain energies in the Al–Fe–Si alloy
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Li, Quan, Wang, Junsheng, Xue, Chengpeng, Wang, Shuo, Yang, Xinghai, Tian, Guangyuan, Su, Hui, Li, Xingxing, and Miao, Yisheng
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- 2024
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