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288 results on '"Li, Depeng"'

8. Anti-BCMA/GPRC5D bispecific CAR T cells in patients with relapsed or refractory multiple myeloma: a single-arm, single-centre, phase 1 trial

Author

Zhou, Dian, Sun, Qian, Xia, Jieyun, Gu, Weiying, Qian, Jun, Zhuang, Wanchuan, Yan, Zhiling, Cheng, Hai, Chen, Wei, Zhu, Feng, Qi, Kunming, Li, Depeng, Sang, Wei, Zhu, Lili, Ma, Sha, Li, Hujun, Zhang, Huanxin, Qiu, Tingting, Yan, Dongmei, Zhang, Yanlei, Peng, Shuixiu, Chang, Alex H, Xu, Kailin, and Li, Zhenyu

Published
2024
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27. Space charge-induced capacitance recovery in blue quantum dot light-emitting diodes.

Author

Qu, Xiangwei, Ma, Jingrui, Li, Depeng, Wang, Kai, and Sun, Xiao Wei

Subjects
LIGHT emitting diodes, ELECTRIC capacity, LOW temperatures, SPACE charge, VOLTAGE, BLUE light, QUANTUM dots, ARGUMENT
Abstract

In this work, we report the capacitance recovery behavior in the blue quantum dot light-emitting diode (QLED) by capacitance–voltage (C–V) characterizations. A comprehensive study of the C–V, dC/dV–V, and current density–voltage characteristics of pristine and shelf-aged devices suggests that capacitance recovery is associated with space charge-induced charge accumulation. At lower temperatures, the capacitance recovery in the shelf-aged device is efficiently suppressed due to the difficulty in building up the space charge, which supports our argument. Moreover, the capacitance recovery behavior of QLED only happens at low frequencies (a few hundred hertz), which is related to the time constant for charge accumulation at the selected voltage. Our work shows the effect of space charge on device capacitance and enriches the comprehension of carrier processes in QLED under AC measurement. [ABSTRACT FROM AUTHOR]

Published
2024
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42. P‐12.6: Quantum Dot Light‐Emitting Diodes with Sputtered TiO2 as Electron Transport Layer.

Author

Wei, Jiahao, Pan, Xinyi, Li, Depeng, Xu, Zhonghua, Zhang, Zhuofan, Ma, Jingrui, and Sun, Xiao Wei

Subjects
ELECTRON transport, LIGHT emitting diodes, QUANTUM dots, QUANTUM efficiency, ELECTRIC fields, QUANTUM dot LEDs
Abstract

Quantum dot light‐emitting diodes (QLEDs) is one of the most important components in the display field, and different structures have a certain impact on the final performance of the device. Previous QLEDs mainly use ZnO nanocrystals as the electron transport layer (ETL), but the chemical activity of ZnO nanocrystals under electric fields and moisture is not stable enough. To solve this problem, this paper studies the effects of TiO2 films with different thicknesses on the luminescence, current density and external quantum efficiency of QLED devices by using magnetron sputtered TiO2 as a new ETL. The experimental results show that the 50nm TiO2 film achieves the highest external quantum efficiency while maintaining favorable current density and brightness. [ABSTRACT FROM AUTHOR]

Published
2024
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44. P‐151: The Optimization of Quantum‐Dots Color Filter for Flexible Display Applications.

Author

Zhao, Bingxin, Li, Depeng, Hao, Junjie, Li, Shang, Bai, Xue, and Sun, Xiao Wei

Subjects
LIGHT filters, FLEXIBLE display systems, QUANTUM dots, OPACITY (Optics), PHOTOLITHOGRAPHY
Abstract

This paper introduces a flexible and uniformly‐dispersed quantum dot (QD) color filter (QDCF) fabricated for micro‐LED by photolithography. We studied the condition of PET films and concentration of QDs, the optimized QDCF exhibited high quantum yield (QY) up to over 70%, and a high optical density (OD) over 1.2. The QY increased and then decreased with the increase of the concentration. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning

Author

Li, Depeng and Zeng, Zhigang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. However, CIL models are challenged by the well-known catastrophic forgetting phenomenon. Typical methods such as rehearsal-based ones rely on storing exemplars of old classes to mitigate catastrophic forgetting, which limits real-world applications considering memory resources and privacy issues. In this paper, we propose a novel rehearsal-free CIL approach that learns continually via the synergy between two Complementary Learning Subnetworks. Our approach involves jointly optimizing a plastic CNN feature extractor and an analytical feed-forward classifier. The inaccessibility of historical data is tackled by holistically controlling the parameters of a well-trained model, ensuring that the decision boundary learned fits new classes while retaining recognition of previously learned classes. Specifically, the trainable CNN feature extractor provides task-dependent knowledge separately without interference; and the final classifier integrates task-specific knowledge incrementally for decision-making without forgetting. In each CIL session, it accommodates new tasks by attaching a tiny set of declarative parameters to its backbone, in which only one matrix per task or one vector per class is kept for knowledge retention. Extensive experiments on a variety of task sequences show that our method achieves competitive results against state-of-the-art methods, especially in accuracy gain, memory cost, training efficiency, and task-order robustness. Furthermore, to make the non-growing backbone (i.e., a model with limited network capacity) suffice to train on more incoming tasks, a graceful forgetting implementation on previously learned trivial tasks is empirically investigated., 13 pages, 4 figures. Under review

Published
2023

46. IF2Net: Innately Forgetting-Free Networks for Continual Learning

Author

Li, Depeng, Wang, Tianqi, Xu, Bingrong, Kawaguchi, Kenji, Zeng, Zhigang, and Suganthan, Ponnuthurai Nagaratnam

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which information is stored in weights on connections, we investigated how to design an Innately Forgetting-Free Network (IF2Net) for continual learning context. This study proposed a straightforward yet effective learning paradigm by ingeniously keeping the weights relative to each seen task untouched before and after learning a new task. We first presented the novel representation-level learning on task sequences with random weights. This technique refers to tweaking the drifted representations caused by randomization back to their separate task-optimal working states, but the involved weights are frozen and reused (opposite to well-known layer-wise updates of weights). Then, sequential decision-making without forgetting can be achieved by projecting the output weight updates into the parsimonious orthogonal space, making the adaptations not disturb old knowledge while maintaining model plasticity. IF2Net allows a single network to inherently learn unlimited mapping rules without telling task identities at test time by integrating the respective strengths of randomization and orthogonalization. We validated the effectiveness of our approach in the extensive theoretical analysis and empirical study., 16 pages, 8 figures. Under review

Published
2023

47. Multi-View Class Incremental Learning

Author

Li, Depeng, Wang, Tianqi, Chen, Junwei, Kawaguchi, Kenji, Lian, Cheng, and Zeng, Zhigang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia, Machine Learning (cs.LG), Multimedia (cs.MM)
Abstract

Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views, requiring no access to earlier views of data. However, MVCIL is challenged by the catastrophic forgetting of old information and the interference with learning new concepts. To address this, we first develop a randomization-based representation learning technique serving for feature extraction to guarantee their separate view-optimal working states, during which multiple views belonging to a class are presented sequentially; Then, we integrate them one by one in the orthogonality fusion subspace spanned by the extracted features; Finally, we introduce selective weight consolidation for learning-without-forgetting decision-making while encountering new classes. Extensive experiments on synthetic and real-world datasets validate the effectiveness of our approach., 34 pages,4 figures. Under review

Published
2023

50. P‐12.5: Microcavity Bottom‐Emitting Blue ZnSeTe Quantum Dot Light‐Emitting Diodes Based on ITO/Ag/ITO Electrode.

Author

Zhang, Zhuofan, Xu, Zhonghua, Li, Depeng, Pan, Xinyi, Wei, Jiahao, Mei, Guanding, Ma, Jingrui, and Sun, Xiao Wei

Subjects
LIGHT emitting diodes, ELECTRODES, QUANTUM dots, MOLECULAR spectra, QUANTUM dot LEDs
Abstract

In this report, ZnSeTe‐based blue bottom‐emitting quantum dot light‐emitting diodes (QLEDs) with optical microcavity design were demonstrated. The microcavity is formed by the top reflective Ag electrode and the bottom semi‐transparent multilayer electrode, ITO/Ag/ITO. The conventional normal QLED device structure is sandwiched between microcavity. The results show the full width at half maximum (FWHM) of the device emission spectrum is reduced to 50% compared to the device without microcavity, and the tail at longer emission wavelengths is notably suppressed. [ABSTRACT FROM AUTHOR]

Published
2024
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