3,218 results on '"Huang, Sheng"'
Search Results
2. Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification
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Yan, Jiexuan, Huang, Sheng, Mu, Nankun, Huangfu, Luwen, and Liu, Bo
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP's embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over baselines. The code is available at https://github.com/jiexuanyan/CPRFL., Comment: Accepted by ACM MM 2024
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- 2024
3. CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs
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Lv, Weijie, Xia, Xuan, and Huang, Sheng-Jun
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Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Large language models (LLMs) have shown great potential in code-related tasks, yet open-source models lag behind their closed-source counterparts. To bridge this performance gap, existing methods generate vast amounts of synthetic data for fine-tuning, leading to inefficiencies in training. Motivated by the need for more effective and efficient training, we propose the Code Adaptive Compute-efficient Tuning (CodeACT) framework. CodeACT introduces the Complexity and Diversity Aware Sampling (CDAS) method to select high-quality training data based on complexity and diversity, and the Dynamic Pack padding strategy to reduce computational resource usage by minimizing padding tokens during training. Experimental results demonstrate that CodeACT-DeepSeek-Coder-6.7B, fine-tuned on only 40% of the EVOL-Instruct data, achieves an 8.6% performance increase on HumanEval, reduces training time by 78%, and decreases peak GPU memory usage by 27%. These findings underscore CodeACT's ability to enhance the performance and efficiency of open-source models. By optimizing both the data selection and training processes, CodeACT offers a comprehensive approach to improving the capabilities of open-source LLMs while significantly reducing computational requirements, addressing the dual challenges of data quality and training efficiency, and paving the way for more resource-efficient and performant models.
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- 2024
4. LightViz: Autonomous Light-field Surveying and Mapping for Distributed Light Pollution Monitoring
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Huang, Sheng-En, Suhi, Kazi Farha Farzana, and Islam, Md Jahidul
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Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Existing technologies for distributed light-field mapping and light pollution monitoring (LPM) rely on either remote satellite imagery or manual light surveying with single-point sensors such as SQMs (sky quality meters). These modalities offer low-resolution data that are not informative for dense light-field mapping, pollutant factor identification, or sustainable policy implementation. In this work, we propose LightViz -- an interactive software interface to survey, simulate, and visualize light pollution maps in real-time. As opposed to manual error-prone methods, LightViz (i) automates the light-field data collection and mapping processes; (ii) provides a platform to simulate various light sources and intensity attenuation models; and (iii) facilitates effective policy identification for conservation. To validate the end-to-end computational pipeline, we design a distributed light-field sensor suit, collect data on Florida coasts, and visualize the distributed light-field maps. In particular, we perform a case study at St. Johns County in Florida, which has a two-decade conservation program for lighting ordinances. The experimental results demonstrate that LightViz can offer high-resolution light-field mapping and provide interactive features to simulate and formulate community policies for light pollution mitigation. We also propose a mathematical formulation for light footprint evaluation, which we integrated into LightViz for targeted LPM in vulnerable communities., Comment: 10 pages, 11 figures
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- 2024
5. Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
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Xiao, Jia-Hao, Xie, Ming-Kun, Fan, Heng-Bo, Niu, Gang, Sugiyama, Masashi, and Huang, Sheng-Jun
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Computer Science - Machine Learning - Abstract
Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. To solve this problem, the mainstream method developed an effective thresholding strategy to generate accurate pseudo-labels. Unfortunately, the method neglected the quality of model predictions and its potential impact on pseudo-labeling performance. In this paper, we propose a dual-perspective method to generate high-quality pseudo-labels. To improve the quality of model predictions, we perform dual-decoupling to boost the learning of correlative and discriminative features, while refining the generation and utilization of pseudo-labels. To obtain proper class-wise thresholds, we propose the metric-adaptive thresholding strategy to estimate the thresholds, which maximize the pseudo-label performance for a given metric on labeled data. Experiments on multiple benchmark datasets show the proposed method can achieve the state-of-the-art performance and outperform the comparative methods with a significant margin.
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- 2024
6. SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification
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Fang, Heng, Huang, Sheng, Tang, Wenhao, Huangfu, Luwen, and Liu, Bo
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current MIL models predominantly rely on instance-level features derived from pretrained models such as ResNet. These models segment each WSI into independent patches and extract features from these local patches, leading to a significant loss of global spatial context and restricting the model's focus to merely local features. To address this issue, we propose a novel MIL framework, named SAM-MIL, that emphasizes spatial contextual awareness and explicitly incorporates spatial context by extracting comprehensive, image-level information. The Segment Anything Model (SAM) represents a pioneering visual segmentation foundational model that can capture segmentation features without the need for additional fine-tuning, rendering it an outstanding tool for extracting spatial context directly from raw WSIs. Our approach includes the design of group feature extraction based on spatial context and a SAM-Guided Group Masking strategy to mitigate class imbalance issues. We implement a dynamic mask ratio for different segmentation categories and supplement these with representative group features of categories. Moreover, SAM-MIL divides instances to generate additional pseudo-bags, thereby augmenting the training set, and introduces consistency of spatial context across pseudo-bags to further enhance the model's performance. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that our proposed SAM-MIL model outperforms existing mainstream methods in WSIs classification. Our open-source implementation code is is available at https://github.com/FangHeng/SAM-MIL., Comment: accepted by ACM Multimedia 2024
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- 2024
7. Relative Difficulty Distillation for Semantic Segmentation
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Liang, Dong, Sun, Yue, Du, Yun, Chen, Songcan, and Huang, Sheng-Jun
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student network to imitate the output of the teacher network. However, introducing too many additional optimization objectives may lead to unstable training, such as gradient conflicts. Moreover, these methods ignored the guidelines of relative learning difficulty between the teacher and student networks. Inspired by human cognitive science, in this paper, we redefine knowledge from a new perspective -- the student and teacher networks' relative difficulty of samples, and propose a pixel-level KD paradigm for semantic segmentation named Relative Difficulty Distillation (RDD). We propose a two-stage RDD framework: Teacher-Full Evaluated RDD (TFE-RDD) and Teacher-Student Evaluated RDD (TSE-RDD). RDD allows the teacher network to provide effective guidance on learning focus without additional optimization goals, thus avoiding adjusting learning weights for multiple losses. Extensive experimental evaluations using a general distillation loss function on popular datasets such as Cityscapes, CamVid, Pascal VOC, and ADE20k demonstrate the effectiveness of RDD against state-of-the-art KD methods. Additionally, our research showcases that RDD can integrate with existing KD methods to improve their upper performance bound.
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- 2024
8. Field Test of Quantum Key Distribution with High Key Creation Efficiency
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Kao, Yung-Cheng, Huang, Sheng-Hsuan, Chang, Chin-Hsuan, Wu, Chih-Hsiang, Chu, Shih-Hsien, Jiang, Jian, Zhang, An-Chi, Huang, Sheng-Yao, Yan, Jhih-Heng, Feng, Kai-Ming, and Chuu, Chih-Sung
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Quantum Physics - Abstract
Quantumkey distribution (QKD) promises unconditional security for communication. However, the random choices of the measurement basis in QKD usually result in low key creation efficiency. This drawback is overcome in the differential-phase-shift QKD, provided that each photon can be prepared in a large number of time bins with a proper waveform. In this work we develop a miniature 1550-nm single-photon source to generate narrowband single photon in 50 time bins with a nearly optimal waveform for achieving unity key creation efficiency. By utilizing these single photons in the field test, we demonstrate the differential-phase-shift QKD with a key creation efficiency of 97%. Our work shows that the practical QKD can benefit from the narrowband single photons with controllable waveforms., Comment: 9pages, 4figures
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- 2024
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9. Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers
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Chen, Zhao-Yun, Ma, Teng-Yang, Ye, Chuang-Chao, Xu, Liang, Tan, Ming-Yang, Zhuang, Xi-Ning, Xu, Xiao-Fan, Wang, Yun-Jie, Sun, Tai-Ping, Chen, Yong, Du, Lei, Guo, Liang-Liang, Zhang, Hai-Feng, Tao, Hao-Ran, Wang, Tian-Le, Yang, Xiao-Yan, Zhao, Ze-An, Wang, Peng, Zhang, Sheng, Zhang, Chi, Zhao, Ren-Ze, Jia, Zhi-Long, Kong, Wei-Cheng, Dou, Meng-Han, Wang, Jun-Chao, Liu, Huan-Yu, Xue, Cheng, Zhang, Peng-Jun-Yi, Huang, Sheng-Hong, Duan, Peng, Wu, Yu-Chun, and Guo, Guo-Ping
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Physics - Computational Physics ,Quantum Physics - Abstract
Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science., Comment: 31 pages, 10 figures
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- 2024
10. 3-Minute Oscillations in the Upper Corona: Evidence from Parker Solar Probe
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Huang, Zesen, Velli, Marco, Shi, Chen, Zhu, Yingjie, Chandran, B. D. G., Réville, Victor, Bowen, Trevor, Sioulas, Nikos, Pulupa, Marc, Huang, Jia, and Huang, Sheng
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Astrophysics - Solar and Stellar Astrophysics ,Physics - Plasma Physics ,Physics - Space Physics - Abstract
Recent observations of Parker Solar Probe (PSP) from around the Alfv\'en surface have shown that the trace magnetic power spectrum density (PSD) is often characterized by a shallow-inertial double power law, where in the low frequency energy injection range, the power spectrum is shallow (flatter than $1/f$), and in the inertial range the spectrum is steep, with a scaling index of [1.5, 1.67]. Consequently, close to the sun, the majority of the fluctuation energy concentrates in a small frequency range around the low frequency power spectral break. In this work, we conduct a systematic survey of PSP observations for the first 17 encounters to statistically study the energy behaviors of the magnetic fluctuations. Our results show that the center frequency of fluctuation energy systematically drifts to around 3-minute for the most pristine solar wind (smallest solar wind advection time). Moreover, the center frequency rapidly drifts to lower frequency as solar wind advection time increases, as expected for active turbulence. The concentration of fluctuation energy around 3-minutes suggests that Alfv\'enic fluctuations in solar wind might mostly be coming from resonant p-mode oscillations in the photosphere, though other potential sources are discussed.
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- 2024
11. One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
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Huang, Sheng-Jun, Li, Yi, Sun, Yiming, and Tang, Ying-Peng
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Computer Science - Machine Learning - Abstract
Active learning (AL) for multiple target models aims to reduce labeled data querying while effectively training multiple models concurrently. Existing AL algorithms often rely on iterative model training, which can be computationally expensive, particularly for deep models. In this paper, we propose a one-shot AL method to address this challenge, which performs all label queries without repeated model training. Specifically, we extract different representations of the same dataset using distinct network backbones, and actively learn the linear prediction layer on each representation via an $\ell_p$-regression formulation. The regression problems are solved approximately by sampling and reweighting the unlabeled instances based on their maximum Lewis weights across the representations. An upper bound on the number of samples needed is provided with a rigorous analysis for $p\in [1, +\infty)$. Experimental results on 11 benchmarks show that our one-shot approach achieves competitive performances with the state-of-the-art AL methods for multiple target models., Comment: A preliminary version appeared in the Proceedings of the 12th International Conference on Learning Representations (ICLR 2024)
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- 2024
12. Improving Generalization of Deep Neural Networks by Optimum Shifting
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Zhou, Yuyan, Li, Ye, Feng, Lei, and Huang, Sheng-Jun
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Computer Science - Machine Learning - Abstract
Recent studies showed that the generalization of neural networks is correlated with the sharpness of the loss landscape, and flat minima suggests a better generalization ability than sharp minima. In this paper, we propose a novel method called \emph{optimum shifting}, which changes the parameters of a neural network from a sharp minimum to a flatter one while maintaining the same training loss value. Our method is based on the observation that when the input and output of a neural network are fixed, the matrix multiplications within the network can be treated as systems of under-determined linear equations, enabling adjustment of parameters in the solution space, which can be simply accomplished by solving a constrained optimization problem. Furthermore, we introduce a practical stochastic optimum shifting technique utilizing the Neural Collapse theory to reduce computational costs and provide more degrees of freedom for optimum shifting. Extensive experiments (including classification and detection) with various deep neural network architectures on benchmark datasets demonstrate the effectiveness of our method.
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- 2024
13. Advances in Robust Federated Learning: Heterogeneity Considerations
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Chen, Chuan, Liao, Tianchi, Deng, Xiaojun, Wu, Zihou, Huang, Sheng, and Zheng, Zibin
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Computer Science - Machine Learning - Abstract
In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources. This diversity leads to significant heterogeneity, which increases the complexity of model training. In this paper, we first outline the basic concepts of heterogeneous federated learning and summarize the research challenges in federated learning in terms of five aspects: data, model, task, device, and communication. In addition, we explore how existing state-of-the-art approaches cope with the heterogeneity of federated learning, and categorize and review these approaches at three different levels: data-level, model-level, and architecture-level. Subsequently, the paper extensively discusses privacy-preserving strategies in heterogeneous federated learning environments. Finally, the paper discusses current open issues and directions for future research, aiming to promote the further development of heterogeneous federated learning.
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- 2024
14. Fermionic quantum criticality through the lens of topological holography
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Huang, Sheng-Jie
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Condensed Matter - Strongly Correlated Electrons ,High Energy Physics - Theory ,Mathematical Physics - Abstract
We utilize the topological holographic framework to characterize and gain insights into the nature of quantum critical points and gapless phases in fermionic quantum systems. Topological holography is a general framework that describes the generalized global symmetry and the symmetry charges of a local quantum system in terms of a slab of a topological order, termed as the symmetry topological field theory (SymTFT), in one higher dimension. In this work, we consider a generalization of the topological holographic picture for $(1+1)d$ fermionic quantum phases of matter. We discuss how spin structures are encoded in the SymTFT and establish the connection between the formal fermionization formula in quantum field theory and the choice of fermionic gapped boundary conditions of the SymTFT. We demonstrate the identification and the characterization of the fermionic gapped phases and phase transitions through detailed analysis of various examples, including the fermionic systems with $\mathbb{Z}_{2}^{F}$, $\mathbb{Z}_{2} \times \mathbb{Z}_{2}^{F}$, $\mathbb{Z}_{4}^{F}$, and the fermionic version of the non-invertible $\text{Rep}(S_{3})$ symmetry. Our work uncovers many exotic fermionic quantum critical points and gapless phases, including two kinds of fermionic symmetry enriched quantum critical points, a fermionic gapless symmetry protected topological (SPT) phase, and a fermionic gapless spontaneous symmetry breaking (SSB) phase that breaks the fermionic non-invertible symmetry., Comment: 33 pages, 7 figures, 6 tables; v2: minor changes, references added
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- 2024
15. Continual Learning in the Presence of Repetition
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Hemati, Hamed, Pellegrini, Lorenzo, Duan, Xiaotian, Zhao, Zixuan, Xia, Fangfang, Masana, Marc, Tscheschner, Benedikt, Veas, Eduardo, Zheng, Yuxiang, Zhao, Shiji, Li, Shao-Yuan, Huang, Sheng-Jun, Lomonaco, Vincenzo, and van de Ven, Gido M.
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design., Comment: Preprint; Challenge Report of the 4th Workshop on Continual Learning in Computer Vision at CVPR
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- 2024
16. Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
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Xie, Ming-Kun, Xiao, Jia-Hao, Peng, Pei, Niu, Gang, Sugiyama, Masashi, and Huang, Sheng-Jun
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
The key to multi-label image classification (MLC) is to improve model performance by leveraging label correlations. Unfortunately, it has been shown that overemphasizing co-occurrence relationships can cause the overfitting issue of the model, ultimately leading to performance degradation. In this paper, we provide a causal inference framework to show that the correlative features caused by the target object and its co-occurring objects can be regarded as a mediator, which has both positive and negative impacts on model predictions. On the positive side, the mediator enhances the recognition performance of the model by capturing co-occurrence relationships; on the negative side, it has the harmful causal effect that causes the model to make an incorrect prediction for the target object, even when only co-occurring objects are present in an image. To address this problem, we propose a counterfactual reasoning method to measure the total direct effect, achieved by enhancing the direct effect caused only by the target object. Due to the unknown location of the target object, we propose patching-based training and inference to accomplish this goal, which divides an image into multiple patches and identifies the pivot patch that contains the target object. Experimental results on multiple benchmark datasets with diverse configurations validate that the proposed method can achieve state-of-the-art performance.
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- 2024
17. Data-Efficient 3D Visual Grounding via Order-Aware Referring
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Wu, Tung-Yu, Huang, Sheng-Yu, and Wang, Yu-Chiang Frank
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Computer Science - Computer Vision and Pattern Recognition - Abstract
3D visual grounding aims to identify the target object within a 3D point cloud scene referred to by a natural language description. Previous works usually require significant data relating to point color and their descriptions to exploit the corresponding complicated verbo-visual relations. In our work, we introduce Vigor, a novel Data-Efficient 3D Visual Grounding framework via Order-aware Referring. Vigor leverages LLM to produce a desirable referential order from the input description for 3D visual grounding. With the proposed stacked object-referring blocks, the predicted anchor objects in the above order allow one to locate the target object progressively without supervision on the identities of anchor objects or exact relations between anchor/target objects. In addition, we present an order-aware warm-up training strategy, which augments referential orders for pre-training the visual grounding framework. This allows us to better capture the complex verbo-visual relations and benefit the desirable data-efficient learning scheme. Experimental results on the NR3D and ScanRefer datasets demonstrate our superiority in low-resource scenarios. In particular, Vigor surpasses current state-of-the-art frameworks by 9.3% and 7.6% grounding accuracy under 1% data and 10% data settings on the NR3D dataset, respectively.
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- 2024
18. GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding
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Chou, Zi-Ting, Huang, Sheng-Yu, Liu, I-Jieh, and Wang, Yu-Chiang Frank
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Generalizable Semantic Neural Radiance Field (GSNeRF), which uniquely takes image semantics into the synthesis process so that both novel view images and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic Geo-Reasoning and Depth-Guided Visual rendering. The former is able to observe multi-view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information, the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified., Comment: Accepted by CVPR2024
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- 2024
19. Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
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Tang, Wenhao, Zhou, Fengtao, Huang, Sheng, Zhu, Xiang, Zhang, Yi, and Liu, Bo
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Multiple instance learning (MIL) is the most widely used framework in computational pathology, encompassing sub-typing, diagnosis, prognosis, and more. However, the existing MIL paradigm typically requires an offline instance feature extractor, such as a pre-trained ResNet or a foundation model. This approach lacks the capability for feature fine-tuning within the specific downstream tasks, limiting its adaptability and performance. To address this issue, we propose a Re-embedded Regional Transformer (R$^2$T) for re-embedding the instance features online, which captures fine-grained local features and establishes connections across different regions. Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator, R$^2$T is tailored to re-embed instance features online. It serves as a portable module that can seamlessly integrate into mainstream MIL models. Extensive experimental results on common computational pathology tasks validate that: 1) feature re-embedding improves the performance of MIL models based on ResNet-50 features to the level of foundation model features, and further enhances the performance of foundation model features; 2) the R$^2$T can introduce more significant performance improvements to various MIL models; 3) R$^2$T-MIL, as an R$^2$T-enhanced AB-MIL, outperforms other latest methods by a large margin.The code is available at: https://github.com/DearCaat/RRT-MIL., Comment: Accepted by CVPR2024
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- 2024
20. Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
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Zong, Chen-Chen, Wang, Ye-Wen, Ning, Kun-Peng, Ye, Hai-Bo, and Huang, Sheng-Jun
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Computer Science - Machine Learning - Abstract
Active learning (AL) in open set scenarios presents a novel challenge of identifying the most valuable examples in an unlabeled data pool that comprises data from both known and unknown classes. Traditional methods prioritize selecting informative examples with low confidence, with the risk of mistakenly selecting unknown-class examples with similarly low confidence. Recent methods favor the most probable known-class examples, with the risk of picking simple already mastered examples. In this paper, we attempt to query examples that are both likely from known classes and highly informative, and propose a Bidirectional Uncertainty-based Active Learning (BUAL) framework. Specifically, we achieve this by first pushing the unknown class examples toward regions with high-confidence predictions, i.e., the proposed Random Label Negative Learning method. Then, we propose a Bidirectional Uncertainty sampling strategy by jointly estimating uncertainty posed by both positive and negative learning to perform consistent and stable sampling. BUAL successfully extends existing uncertainty-based AL methods to complex open-set scenarios. Extensive experiments on multiple datasets with varying openness demonstrate that BUAL achieves state-of-the-art performance. The code is available at https://github.com/chenchenzong/BUAL., Comment: Accepted to ECCV 2024
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- 2024
21. Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition
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Wang, Yijie, Hong, Mingjian, Huangfu, Luwen, and Huang, Sheng
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
In the realm of Zero-Shot Learning (ZSL), we address biases in Generalized Zero-Shot Learning (GZSL) models, which favor seen data. To counter this, we introduce an end-to-end generative GZSL framework called D$^3$GZSL. This framework respects seen and synthesized unseen data as in-distribution and out-of-distribution data, respectively, for a more balanced model. D$^3$GZSL comprises two core modules: in-distribution dual space distillation (ID$^2$SD) and out-of-distribution batch distillation (O$^2$DBD). ID$^2$SD aligns teacher-student outcomes in embedding and label spaces, enhancing learning coherence. O$^2$DBD introduces low-dimensional out-of-distribution representations per batch sample, capturing shared structures between seen and unseen categories. Our approach demonstrates its effectiveness across established GZSL benchmarks, seamlessly integrating into mainstream generative frameworks. Extensive experiments consistently showcase that D$^3$GZSL elevates the performance of existing generative GZSL methods, underscoring its potential to refine zero-shot learning practices.The code is available at: https://github.com/PJBQ/D3GZSL.git, Comment: accepted as AAAI 2024 oral paper
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- 2024
22. Empowering Language Models with Active Inquiry for Deeper Understanding
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Pang, Jing-Cheng, Fan, Heng-Bo, Wang, Pengyuan, Xiao, Jia-Hao, Tang, Nan, Yang, Si-Hang, Jia, Chengxing, Huang, Sheng-Jun, and Yu, Yang
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language. However, LLMs often misinterpret user queries because of their uncertain intention, leading to less helpful responses. In natural human interactions, clarification is sought through targeted questioning to uncover obscure information. Thus, in this paper, we introduce LaMAI (Language Model with Active Inquiry), designed to endow LLMs with this same level of interactive engagement. LaMAI leverages active learning techniques to raise the most informative questions, fostering a dynamic bidirectional dialogue. This approach not only narrows the contextual gap but also refines the output of the LLMs, aligning it more closely with user expectations. Our empirical studies, across a variety of complex datasets where LLMs have limited conversational context, demonstrate the effectiveness of LaMAI. The method improves answer accuracy from 31.9% to 50.9%, outperforming other leading question-answering frameworks. Moreover, in scenarios involving human participants, LaMAI consistently generates responses that are superior or comparable to baseline methods in more than 82% of the cases. The applicability of LaMAI is further evidenced by its successful integration with various LLMs, highlighting its potential for the future of interactive language models.
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- 2024
23. Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
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Zong, Chen-Chen, Wang, Ye-Wen, Xie, Ming-Kun, and Huang, Sheng-Jun
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs). Existing approaches address this issue through loss correction or example selection methods. However, these methods often rely on the model's predictions obtained from the softmax function, which can be over-confident and unreliable. In this study, we identify the translation invariance of the softmax function as the underlying cause of this problem and propose the \textit{Dirichlet-based Prediction Calibration} (DPC) method as a solution. Our method introduces a calibrated softmax function that breaks the translation invariance by incorporating a suitable constant in the exponent term, enabling more reliable model predictions. To ensure stable model training, we leverage a Dirichlet distribution to assign probabilities to predicted labels and introduce a novel evidence deep learning (EDL) loss. The proposed loss function encourages positive and sufficiently large logits for the given label, while penalizing negative and small logits for other labels, leading to more distinct logits and facilitating better example selection based on a large-margin criterion. Through extensive experiments on diverse benchmark datasets, we demonstrate that DPC achieves state-of-the-art performance. The code is available at https://github.com/chenchenzong/DPC.
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- 2024
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24. A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation
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Sun, Feng, Xie, Ming-Kun, and Huang, Sheng-Jun
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- 2024
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25. Validation of the constitution in Chinese medicine questionnaires with the newly developed blood-vacuity category: a rare Taiwanese case study
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Huang, Ting-Yu, Ou, Shi-Chen, Huang, Wei-Te, Lin, Yu-Chuan, Hung, Hao-Hsiu, Chang, Chin-Wei, Hsiao, Kai-Yuan, Kao, Yi-Wei, Wu, Chien-Hua, Chang, Wei-Shan, Shia, Ben-Chang, and Huang, Sheng-Teng
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- 2024
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26. A strong fracture-resistant high-entropy alloy with nano-bridged honeycomb microstructure intrinsically toughened by 3D-printing
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Kumar, Punit, Huang, Sheng, Cook, David H, Chen, Kai, Ramamurty, Upadrasta, Tan, Xipeng, and Ritchie, Robert O
- Subjects
Macromolecular and Materials Chemistry ,Chemical Sciences ,Engineering ,Manufacturing Engineering ,Materials Engineering - Abstract
Strengthening materials via conventional "top-down" processes generally involves restricting dislocation movement by precipitation or grain refinement, which invariably restricts the movement of dislocations away from, or towards, a crack tip, thereby severely compromising their fracture resistance. In the present study, a high-entropy alloy Al0.5CrCoFeNi is produced by the laser powder-bed fusion process, a "bottom-up" additive manufacturing process similar to how nature builds structures, with the microstructure resembling a nano-bridged honeycomb structure consisting of a face-centered cubic (fcc) matrix and an interwoven hexagonal net of an ordered body-centered cubic B2 phase. While the B2 phase, combined with high-dislocation density and solid-solution strengthening, provides strength to the material, the nano-bridges of dislocations connecting the fcc cells, i.e., the channels between the B2 phase on the cell boundaries, provide highways for dislocation movement away from the crack tip. Consequently, the nature-inspired microstructure imparts the material with an excellent combination of strength and toughness.
- Published
- 2024
27. Solar Wind Structures from the Gaussianity of Magnetic Magnitude
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Huang, Zesen, Shi, Chen, Velli, Marco, Sioulas, Nikos, Panasenco, Olga, Bowen, Trevor, Matteini, Lorenzo, Xia, Mingtao, Shi, Xiaofei, Huang, Sheng, Huang, Jia, and Casillas, Lizet
- Subjects
Physics - Space Physics ,Astrophysics - Solar and Stellar Astrophysics - Abstract
One of the primary science objectives of Parker Solar Probe (PSP) is to determine the structures and dynamics of the plasma and magnetic fields at the sources of the solar wind. However, establishing the connection between {\it in situ} measurements and structures and dynamics in the solar atmosphere is challenging: most of the magnetic footpoint mapping techniques have significant uncertainties in the source localization of a plasma parcel observed {\it in situ}, and the PSP plasma measurements suffer from a limited field of view. Therefore it is of interest to investigate whether {\it in situ} measurements can be used on their own to identify streams originating from the same structures in the corona more finely than the well known fast wind-coronal hole, slow wind-elsewhere distinction. Here we develop a novel time series visualization method \textcolor{red}{(time-frequency representation or TFR)} named Gaussianity Scalogram. Utilizing this method, by analyzing the magnetic magnitude data from both PSP and Ulysses, we successfully identify {\it in situ} structures that are possible remnants of solar atmospheric and magnetic structures spanning more than seven orders of magnitude, from years to seconds, including polar and mid-latitude coronal holes, as well as structures compatible with super-granulation , ``jetlets'' and ``picoflares''. \textcolor{red}{Furthermore, computer simulations of Alfv\'enic turbulence successfully reproduce the Gaussianization of the magnetic magnitude for locally homogeneous structures.} Building upon these discoveries, the Gaussianity Scalogram can help future studies to reveal the fractal-like fine structures in the solar wind time series from both PSP and decades-old data archive., Comment: Accepted by ApJL
- Published
- 2023
- Full Text
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28. TPA3D: Triplane Attention for Fast Text-to-3D Generation
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Wu, Bin-Shih, Chen, Hong-En, Huang, Sheng-Yu, and Wang, Yu-Chiang Frank
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Due to the lack of large-scale text-3D correspondence data, recent text-to-3D generation works mainly rely on utilizing 2D diffusion models for synthesizing 3D data. Since diffusion-based methods typically require significant optimization time for both training and inference, the use of GAN-based models would still be desirable for fast 3D generation. In this work, we propose Triplane Attention for text-guided 3D generation (TPA3D), an end-to-end trainable GAN-based deep learning model for fast text-to-3D generation. With only 3D shape data and their rendered 2D images observed during training, our TPA3D is designed to retrieve detailed visual descriptions for synthesizing the corresponding 3D mesh data. This is achieved by the proposed attention mechanisms on the extracted sentence and word-level text features. In our experiments, we show that TPA3D generates high-quality 3D textured shapes aligned with fine-grained descriptions, while impressive computation efficiency can be observed., Comment: ECCV2024
- Published
- 2023
29. Topological holography, quantum criticality, and boundary states
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Huang, Sheng-Jie and Cheng, Meng
- Subjects
Condensed Matter - Strongly Correlated Electrons ,High Energy Physics - Theory ,Mathematical Physics - Abstract
Topological holography is a holographic principle that describes the generalized global symmetry of a local quantum system in terms of a topological order in one higher dimension. This framework separates the topological data from the local dynamics of a theory and provides a unified description of the symmetry and duality in gapped and gapless phases of matter. In this work, we develop the topological holographic picture for (1+1)d quantum phases, including both gapped phases as well as a wide range of quantum critical points, including phase transitions between symmetry protected topological (SPT) phases, symmetry enriched quantum critical points, deconfined quantum critical points and intrinsically gapless SPT phases. Topological holography puts a strong constraint on the emergent symmetry and the anomaly for these critical theories. We show how the partition functions of these critical points can be obtained from dualizing (orbifolding) more familiar critical theories. The topological responses of the defect operators are also discussed in this framework. We further develop a topological holographic picture for conformal boundary states of (1+1)d rational conformal field theories. This framework provides a simple physical picture to understand conformal boundary states and also uncovers the nature of the gapped phases corresponding to the boundary states., Comment: 43 pages, 10 figures, 3 tables. v2: references added
- Published
- 2023
30. Polarization-entangled photons from a whispering gallery resonator
- Author
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Huang, Sheng-Hsuan, Dirmeier, Thomas, Shafiee, Golnoush, Laiho, Kaisa, Strekalov, Dmitry V., Leuchs, Gerd, and Marquardt, Christoph
- Subjects
Physics - Optics ,Quantum Physics - Abstract
Crystalline Whispering Gallery Mode Resonators (WGMRs) have been shown to facilitate versatile sources of quantum states that can efficiently interact with atomic systems. These features make WGMRs an efficient platform for quantum information processing. Here, we experimentally show that it is possible to generate polarization entanglement from WGMRs by using an interferometric scheme. Our scheme gives us the flexibility to control the phase of the generated entangled state by changing the relative phase of the interferometer. The S value of the Clauser-Horne-Shimony-Holt's inequality in the system is $2.45 \pm 0.07$, which violates the inequality by more than 6 standard deviations.
- Published
- 2023
31. Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow
- Author
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Huang, Sheng-Chi and Chiu, Wei-Chen
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Optical flow estimation is crucial for various applications in vision and robotics. As the difficulty of collecting ground truth optical flow in real-world scenarios, most of the existing methods of learning optical flow still adopt synthetic dataset for supervised training or utilize photometric consistency across temporally adjacent video frames to drive the unsupervised learning, where the former typically has issues of generalizability while the latter usually performs worse than the supervised ones. To tackle such challenges, we propose to leverage the geometric connection between optical flow estimation and stereo matching (based on the similarity upon finding pixel correspondences across images) to unify various real-world depth estimation datasets for generating supervised training data upon optical flow. Specifically, we turn the monocular depth datasets into stereo ones via synthesizing virtual disparity, thus leading to the flows along the horizontal direction; moreover, we introduce virtual camera motion into stereo data to produce additional flows along the vertical direction. Furthermore, we propose applying geometric augmentations on one image of an optical flow pair, encouraging the optical flow estimator to learn from more challenging cases. Lastly, as the optical flow maps under different geometric augmentations actually exhibit distinct characteristics, an auxiliary classifier which trains to identify the type of augmentation from the appearance of the flow map is utilized to further enhance the learning of the optical flow estimator. Our proposed method is general and is not tied to any particular flow estimator, where extensive experiments based on various datasets and optical flow estimation models verify its efficacy and superiority.
- Published
- 2023
32. Hydrophobic and anti-fouling polyelectrolyte complex coating for durable flame-retardant cotton fabric
- Author
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He, Jia-Lin, Luo, Wei, Deng, Jin-Ni, Wang, Ting, Fu, Zhi-Cheng, Xia, Chang-Lin, Huang, Sheng-Chao, and Chen, Ming-Jun
- Published
- 2024
- Full Text
- View/download PDF
33. Combined effect of falling difficulty and cardiovascular diseases on the risk of all-cause and cardiovascular mortality in older adults: an analysis of the National Health and Nutrition Examination Survey
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Huang, Sheng, Wang, Zhigang, Wu, Qiaohong, Fan, Jinmao, and Luo, Ji
- Published
- 2024
- Full Text
- View/download PDF
34. Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery
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Zhou, Yuyan, Liang, Dong, Chen, Songcan, Huang, Sheng-Jun, Yang, Shuo, and Li, Chongyi
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - Abstract
When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations., Comment: ICCV 2023
- Published
- 2023
35. Multi-Label Knowledge Distillation
- Author
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Yang, Penghui, Xie, Ming-Kun, Zong, Chen-Chen, Feng, Lei, Niu, Gang, Sugiyama, Masashi, and Huang, Sheng-Jun
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class single-label learning. However, these methods can hardly be extended to the multi-label learning scenario, where each instance is associated with multiple semantic labels, because the prediction probabilities do not sum to one and feature maps of the whole example may ignore minor classes in such a scenario. In this paper, we propose a novel multi-label knowledge distillation method. On one hand, it exploits the informative semantic knowledge from the logits by dividing the multi-label learning problem into a set of binary classification problems; on the other hand, it enhances the distinctiveness of the learned feature representations by leveraging the structural information of label-wise embeddings. Experimental results on multiple benchmark datasets validate that the proposed method can avoid knowledge counteraction among labels, thus achieving superior performance against diverse comparing methods. Our code is available at: https://github.com/penghui-yang/L2D, Comment: Accepted by ICCV 2023. The first two authors contributed equally to this work
- Published
- 2023
36. Gaussian-based Probabilistic Deep Supervision Network for Noise-Resistant QoS Prediction
- Author
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Wang, Ziliang, Zhang, Xiaohong, Huang, Sheng, Zhang, Wei, Yang, Dan, and Yan, Meng
- Subjects
Computer Science - Software Engineering ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Information Retrieval ,Computer Science - Machine Learning - Abstract
Quality of Service (QoS) prediction is an essential task in recommendation systems, where accurately predicting unknown QoS values can improve user satisfaction. However, existing QoS prediction techniques may perform poorly in the presence of noise data, such as fake location information or virtual gateways. In this paper, we propose the Probabilistic Deep Supervision Network (PDS-Net), a novel framework for QoS prediction that addresses this issue. PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate layers and learns probability spaces for both known features and true labels. Moreover, PDS-Net employs a condition-based multitasking loss function to identify objects with noise data and applies supervision directly to deep features sampled from the probability space by optimizing the Kullback-Leibler distance between the probability space of these objects and the real-label probability space. Thus, PDS-Net effectively reduces errors resulting from the propagation of corrupted data, leading to more accurate QoS predictions. Experimental evaluations on two real-world QoS datasets demonstrate that the proposed PDS-Net outperforms state-of-the-art baselines, validating the effectiveness of our approach.
- Published
- 2023
37. Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
- Author
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Tang, Wenhao, Huang, Sheng, Zhang, Xiaoxian, Zhou, Fengtao, Zhang, Yi, and Liu, Bo
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
The whole slide image (WSI) classification is often formulated as a multiple instance learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI, existing MIL methods intuitively focus on identifying salient instances via attention mechanisms. However, this leads to a bias towards easy-to-classify instances while neglecting hard-to-classify instances. Some literature has revealed that hard examples are beneficial for modeling a discriminative boundary accurately. By applying such an idea at the instance level, we elaborate a novel MIL framework with masked hard instance mining (MHIM-MIL), which uses a Siamese structure (Teacher-Student) with a consistency constraint to explore the potential hard instances. With several instance masking strategies based on attention scores, MHIM-MIL employs a momentum teacher to implicitly mine hard instances for training the student model, which can be any attention-based MIL model. This counter-intuitive strategy essentially enables the student to learn a better discriminating boundary. Moreover, the student is used to update the teacher with an exponential moving average (EMA), which in turn identifies new hard instances for subsequent training iterations and stabilizes the optimization. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that MHIM-MIL outperforms other latest methods in terms of performance and training cost. The code is available at: https://github.com/DearCaat/MHIM-MIL., Comment: Published on ICCV2023
- Published
- 2023
38. Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
- Author
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Wan, Wenhai, Wang, Xinrui, Xie, Ming-Kun, Li, Shao-Yuan, Huang, Sheng-Jun, and Chen, Songcan
- Subjects
Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both open-set and closed-set noise. Existing methods typically identify and handle these two types of label noise separately by designing a specific strategy for each type. However, in many real-world scenarios, it would be challenging to identify open-set examples, especially when the dataset has been severely corrupted. Unlike the previous works, we explore how models behave when faced with open-set examples, and find that \emph{a part of open-set examples gradually get integrated into certain known classes}, which is beneficial for the separation among known classes. Motivated by the phenomenon, we propose a novel two-step contrastive learning method CECL (Class Expansion Contrastive Learning) which aims to deal with both types of label noise by exploiting the useful information of open-set examples. Specifically, we incorporate some open-set examples into closed-set classes to enhance performance while treating others as delimiters to improve representative ability. Extensive experiments on synthetic and real-world datasets with diverse label noise demonstrate the effectiveness of CECL.
- Published
- 2023
39. Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
- Author
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Xie, Ming-Kun, Xiao, Jia-Hao, Liu, Hao-Zhe, Niu, Gang, Sugiyama, Masashi, and Huang, Sheng-Jun
- Subjects
Computer Science - Machine Learning - Abstract
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter difficulties when dealing with instances associated with multiple labels and an unknown label count. These limitations often result in the introduction of false positive labels or the neglect of true positive ones. To overcome these challenges, this paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner. The proposed approach introduces a regularized learning framework incorporating class-aware thresholds, which effectively control the assignment of positive and negative pseudo-labels for each class. Notably, even with a small proportion of labeled examples, our observations demonstrate that the estimated class distribution serves as a reliable approximation. Motivated by this finding, we develop a class-distribution-aware thresholding strategy to ensure the alignment of pseudo-label distribution with the true distribution. The correctness of the estimated class distribution is theoretically verified, and a generalization error bound is provided for our proposed method. Extensive experiments on multiple benchmark datasets confirm the efficacy of CAP in addressing the challenges of SSMLL problems.
- Published
- 2023
40. ALL-E: Aesthetics-guided Low-light Image Enhancement
- Author
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Li, Ling, Liang, Dong, Gao, Yuanhang, Huang, Sheng-Jun, and Chen, Songcan
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions, i.e., its corresponding adjustment curve is estimated sequentially. Extensive experiments show that integrating aesthetic assessment improves both subjective experience and objective evaluation. Our results on various benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods.
- Published
- 2023
41. Fusion Attention Graph Convolutional Network with Hyperskeleton for UAV Action Recognition
- Author
-
Liu, Fang, Huang, Sheng, Dai, Qin, Liu, Cuiwei, Shi, Xiangbin, 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, Pan, Yijie, editor, and Guo, Jiayang, editor
- Published
- 2024
- Full Text
- View/download PDF
42. Study on the Spectral Response Characteristics of Grounding Electrodes in Horizontal Double Layer Soil Based on CDEGS
- Author
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Xu, Yifan, Ma, Qiheng, Huang, Sheng, Zhou, Jie, Cao, Xiaobin, Li, Ruifang, 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, Yang, Qingxin, editor, Li, Zewen, editor, and Luo, An, editor
- Published
- 2024
- Full Text
- View/download PDF
43. Cooperative Deformation Behavior and Microstructure Evolution of High-Strength Titanium-Clad Steel Plate During Thermal Compression
- Author
-
Chen, Xuan, Xiao, Han, Shao, Peng, Huang, Sheng, Shi, Yaming, and Liu, Kun
- Published
- 2024
- Full Text
- View/download PDF
44. Conditional Denoising Diffusion Implicit Model for Speech Enhancement
- Author
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Yang, Chengyong, Yu, Xiukang, and Huang, Sheng
- Published
- 2024
- Full Text
- View/download PDF
45. Performance and mechanism analysis of sludge-based biochar loaded with Co and Mn as photothermal catalysts for simultaneous removal of acetone and NO at low temperature
- Author
-
Wang, Hongqiang, Huang, Sheng, Liao, Lei, Mo, Shengpeng, Zhou, Xiaobin, and Fan, Yinming
- Published
- 2024
- Full Text
- View/download PDF
46. Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection
- Author
-
He, Tao, Huang, Sheng, Tang, Wenhao, and Liu, Bo
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kernel Expansion (DKE), which incorporates the merits of both segmentation and contour-based detectors. DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the text kernel contour to obtain text boundary by regressing the vertex-wise offsets. Generating the text kernel by segmentation enables DKE to inherit the arbitrary-shaped text region modeling capability of segmentation-based detectors. Regressing the kernel contour with some sampled vertices enables DKE to avoid the complicated pixel-level post-processing and better learn contour deformation as the contour-based detectors. Moreover, we propose an Optimal Bipartite Graph Matching Loss (OBGML) that measures the matching error between the predicted contour and the ground truth, which efficiently minimizes the global contour matching distance. Extensive experiments on CTW1500, Total-Text, MSRA-TD500, and ICDAR2015 demonstrate that DKE achieves a good tradeoff between accuracy and efficiency in scene text detection.
- Published
- 2023
47. Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
- Author
-
Li, Ye, Chen, Song-Can, and Huang, Sheng-Jun
- Subjects
Computer Science - Machine Learning - Abstract
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit stochastic gradient descent (ISGD) method to train PINNs for improving the stability of training process. We heuristically analyze how ISGD overcome stiffness in the gradient flow dynamics of PINNs, especially for problems with multi-scale solutions. We theoretically prove that for two-layer fully connected neural networks with large hidden nodes, randomly initialized ISGD converges to a globally optimal solution for the quadratic loss function. Empirical results demonstrate that ISGD works well in practice and compares favorably to other gradient-based optimization methods such as SGD and Adam, while can also effectively address the numerical stiffness in training dynamics via gradient descent., Comment: 17 pages, published as a conference paper at AAAI23
- Published
- 2023
48. Electrostatic effect due to patch potentials between closely spaced surfaces
- Author
-
Ke, Jun, Dong, Wen-Can, Huang, Sheng-Hua, Tan, Yu-Jie, Tan, Wen-Hai, Yang, Shan-Qing, Shao, Cheng-Gang, and Luo, Jie
- Subjects
Condensed Matter - Soft Condensed Matter - Abstract
The spatial variation and temporal variation in surface potential are important error sources in various precision experiments and deserved to be considered carefully. In the former case, the theoretical analysis shows that this effect depends on the surface potentials through their spatial autocorrelation functions. By making some modification to the quasi-local correlation model, we obtain a rigorous formula for the patch force, where the magnitude is proportional to ${\frac{1}{{{a}^{2}}}{{(\frac{a}{w})}^{\beta (a/w)+2}}}$ with ${a}$ the distance between two parallel plates, ${w}$ the mean patch size, and ${\beta}$ the scaling coefficient from ${-2}$ to ${-4}$. A torsion balance experiment is then conducted, and obtain a 0.4 mm effective patch size and 20 mV potential variance. In the latter case, we apply an adatom diffusion model to describe this mechanism and predicts a ${f^{-3/4}}$ frequency dependence above 0.01 ${\rm mHz}$. This prediction meets well with a typical experimental results. Finally, we apply these models to analyze the patch effect for two typical experiments. Our analysis will help to investigate the properties of surface potentials.
- Published
- 2023
- Full Text
- View/download PDF
49. Review of High Voltage Ride-Through Control Method of Large-Scale Wind Farm
- Author
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WEI Juan, LI Canbing, HUANG Sheng, CHEN Sijie, GE Rui, SHEN Feifan, WEI Lai
- Subjects
wind power ,wind power generator ,wind farm ,high voltage ride-through (hvrt) ,voltage control ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Chemical engineering ,TP155-156 ,Naval architecture. Shipbuilding. Marine engineering ,VM1-989 - Abstract
As the major demand for the development and utilization of new energy, the large-scale development of wind power is a key support in achieving the strategic goal of “cabron peaking and carbon neutrality” for China. The problem of safe and stable operation of wind farms caused by external grid faults has become one of the key bottlenecks restricting the large-scale, clustered, and intelligent development of wind power. This paper mainly focuses on the voltage surge condition of the power grid. First, it analyzes the transient characteristics of high voltage ride-through (HVRT) of the doubly-fed induction generator-wind turbine, permanent magnet synchronous generator-wind turbine, and wind farms. Then, it summarizes the corresponding HVRT and post-fault voltage recovery coordinated optimal control strategies based on the different control areas, and it classifies and compares the working principles and advantages and disadvantages of various control strategies. Afterwards, it recapitulates the principle, advantages and disadvantages, and effects of the existing HVRT control method for large-scale wind farms, and analyzes the differences between the single wind turbine and the large-scale wind farms from the perspective of control structure. Finally, it discusses the development trend and potential research hotspots of wind farm voltage intelligent safety control in the future, aiming to provide reference for improving the large-scale application of wind power and the safe operation of power grids in China.
- Published
- 2024
- Full Text
- View/download PDF
50. Functional gene delivery to and across brain vasculature of systemic AAVs with endothelial-specific tropism in rodents and broad tropism in primates.
- Author
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Chen, Xinhong, Wolfe, Damien A, Bindu, Dhanesh Sivadasan, Zhang, Mengying, Taskin, Naz, Goertsen, David, Shay, Timothy F, Sullivan, Erin E, Huang, Sheng-Fu, Ravindra Kumar, Sripriya, Arokiaraj, Cynthia M, Plattner, Viktor M, Campos, Lillian J, Mich, John K, Monet, Deja, Ngo, Victoria, Ding, Xiaozhe, Omstead, Victoria, Weed, Natalie, Bishaw, Yeme, Gore, Bryan B, Lein, Ed S, Akrami, Athena, Miller, Cory, Levi, Boaz P, Keller, Annika, Ting, Jonathan T, Fox, Andrew S, Eroglu, Cagla, and Gradinaru, Viviana
- Subjects
Brain ,Endothelial Cells ,Animals ,Mice ,Knockout ,Macaca mulatta ,Rodentia ,Mice ,Rats ,Dependovirus ,Calcium-Binding Proteins ,Extracellular Matrix Proteins ,Transduction ,Genetic ,Tropism ,Genetic Vectors ,Biotechnology ,Genetics ,Gene Therapy ,Neurosciences ,Neurological - Abstract
Delivering genes to and across the brain vasculature efficiently and specifically across species remains a critical challenge for addressing neurological diseases. We have evolved adeno-associated virus (AAV9) capsids into vectors that transduce brain endothelial cells specifically and efficiently following systemic administration in wild-type mice with diverse genetic backgrounds, and in rats. These AAVs also exhibit superior transduction of the CNS across non-human primates (marmosets and rhesus macaques), and in ex vivo human brain slices, although the endothelial tropism is not conserved across species. The capsid modifications translate from AAV9 to other serotypes such as AAV1 and AAV-DJ, enabling serotype switching for sequential AAV administration in mice. We demonstrate that the endothelial-specific mouse capsids can be used to genetically engineer the blood-brain barrier by transforming the mouse brain vasculature into a functional biofactory. We apply this approach to Hevin knockout mice, where AAV-X1-mediated ectopic expression of the synaptogenic protein Sparcl1/Hevin in brain endothelial cells rescued synaptic deficits.
- Published
- 2023
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