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Tunable multicolor luminescence in vanadates from yttrium to indium with enhanced luminous efficiency and stability for its application in WLEDs and indoor photovoltaics

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Nano Research 2023年第8期16卷 11486-11494页

作者： Yaoyang Zhang Jing Mao Peifen Zhu Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouri 65211USA

The preparation of high-efficiency phosphor is the key to the construction of white light-emitting diode(WLED)devices and their application in indoor *** with YVO_(4),InVO_(4)is not suitable as the host material of lanthanide ions because of its strong ***,the work focused on combining the broadband emission from InVO_(4)and the red luminescence from YVO_(4):Eu^(3+)to obtain enhanced and stable multicolor *** band structure,density of state,and optical properties were studied by density functional *** spectral configuration of YVO_(4):In^(3+)/Eu^(3+)with(112)surface appears to be broadening and redshifts with increasing layer *** the In^(3+)concentration is 3.5 mol%,the YVO_(4):30%Eu^(3+)/In^(3+)emits the strongest *** Judd-Ofelt parameterΩ2 of YVO_(4):In^(3+)/Eu^(3+)increases with increaing In^(3+)concentration,indicating that the symmetry *** adjusting In^(3+)/Eu^(3+)contents,the YVO_(4):In^(3+)/Eu^(3+)not only can emit white light with a color rendering index of 95,but also can be used as high-efficiency red phosphor to build WLED devices with blue emitting N/Tb codoped carbon quantum dots(CQDs-N:Tb^(3+))and green emitting MOF:Tb^(3+)(MOF=metal organic framework),for which the color rendering index can also reach 95 and the color temperature is 5549 *** manufactured WLED devices were further used to excite the silicon solar cell and make it show good photoelectric characteristics.

关键词： luminescence enhancement multicolor stability density functional theory(DFT)calculation white light-emitting diodes(WLEDs) indoor photovoltaics

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Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge

Ensemble and Personalized Transformer Models for Subject Ide...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Salvatore Calcagno Raffaele Mineo Daniela Giordano Concetto Spampinato Department of Electrical Electronics and Computer Engineering Pattern Recognition and Computer Vision Laboratory (PeRCeiVe Lab) University of Catania Italy

In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We specifically design an ensemble scheme of six models - five transformer-based ones and a CNN model - for the identification of subjects from wearable devices, while a personalized - one for each subject - scheme is used for relapse detection in psychotic disorder. Our final submitted solutions yield top performance on both tracks of the challenge: we ranked 2 nd on the subject identification task (with an accuracy of 93.85%) and 1 st on the relapse detection task (with a ROC-AUC and PR-AUC of about 0.65). Code and details are available at https://***/perceivelab/e-prevention-icassp-2023.

关键词： Performance evaluation Codes Wearable computers Signal processing Transformers Acoustics Object recognition

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On the Emergence of Cross-Task Linearity in Pretraining-Finetuning Paradigm 41

On the Emergence of Cross-Task Linearity in Pretraining-Fine...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhanpeng Chen, Zijun Chen, Yilan Zhang, Bo Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China Shanghai Artificial Intelligence Laboratory China Computer Science and Engineering University of California San Diego United States

The pretraining-finetuning paradigm has become the prevailing trend in modern deep *** this work, we discover an intriguing linear phenomenon in models that are initialized from a common pretrained checkpoint and finetuned on different tasks1, termed as Cross-Task Linearity (CTL).Specifically, we show that if we linearly interpolate the weights of two finetuned models, the features in the weight-interpolated model are often approximately equal to the linear interpolation of features in two finetuned models at each *** provide comprehensive empirical evidence supporting that CTL consistently occurs for finetuned models that start from the same pretrained *** conjecture that in the pretraining-finetuning paradigm, neural networks approximately function as linear maps, mapping from the parameter space to the feature *** on this viewpoint, our study unveils novel insights into explaining model merging/editing, particularly by translating operations from the parameter space to the feature ***, we delve deeper into the root cause for the emergence of CTL, highlighting the role of *** released our source code at https://***/zzp1012/Cross-Task-Linearity. Copyright 2024 by the author(s)

关键词： Deep learning

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Predicting DNN run-times using a Graph Neural Network for heterogenous SoCs 19

Predicting DNN run-times using a Graph Neural Network for he...

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19th IEEE International Conference on Intelligent computer Communication and Processing Conference, ICCP 2023

作者： Viman, Andrei Negru, Mihai Cason, Catalin Engineering Systems Vision Robert Bosch SRL Cluj-Napoca Romania Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

Deep learning models are widely used for automated driving, but achieving the goal of performance in embedded Systems-on-Chip (SoCs) has a lot of challenges due to the tradeoff between accuracy and run-time. This paper addresses the task of finding a suitable neural architecture for heterogeneous Systems-on-Chip (SoCs) in order to strike a balance between accuracy and run-time efficiency. Our approach may be used to predict the inference run-time of DNN models, which is useful not only for neural architecture search, but also for the optimization of deep learning models. We propose a method for predicting the run-time of deep learning models using Graph Neural Networks, based on the Graph Convolution Network. We transform the DNN models into an acyclic graph structure, which incorporates encoded features for all nodes. Each node represents an operation of the model and all nodes are linked together. The experimental results show that our approach achieves accurate run-time inference predictions, with an overall error between 2% and 4% for ARM CPUs. © 2023 IEEE.

关键词： System-on-chip

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Deep learning-based few-shot person re-identification from top-view RGB and depth images

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Neural Computing and Applications 2024年第31期36卷 19365-19382页

作者： Abed, Almustafa Akrout, Belhassen Amous, Ikram Miracl Laboratory Enet’com University of Sfax National School of Electronics and Telecommunications of Sfax Road Tunis City El Ons Sfax3018 Tunisia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Sfax University Sfax3021 Tunisia

Person re-identification (re-id) attempts to match a person from the images of different time steps. Existing deep learning approaches either use appearance or geometry features for re-id which does not provide the required robustness because of higher intra-class similarity. Existing supervised re-id approaches utilize Convolutional Neural Networks (CNNs) and identity-labeled images to train, where the person images are taken by the sensors from a horizontal view. The horizontal view exposes the privacy of the people because of their facial appearance in the image. Moreover, person re-id includes new unseen people;however, CNN does not have the ability to identify the new unseen people because of a lack of continual learning. Privacy-preserved computer vision-assisted person re-id systems can benefit from visual appearance and geometry features extracted from top-view RGB and depth input. This paper presents the privacy-preserved person top-view re-id few-shot network which uses the appearance and geometry features. The EfficientNet is used for appearance-based features from RGB input, while PointNet is used to extract the geometry features from the point cloud which is made from the RGB-D image registration. Concatenated features from EfficientNet and PointNet are fed to the two-layer Bi-LSTM network for person identification. Finally, the whole network is converted into a few-shot network to achieve continual learning by removing the output layer and joining the similarity measurement unit. This approach is based on CNN and fine-tunes a TVPR/2 dataset acquired by using a top-view arrangement that is publicly available. The experimental results on TVPR/2 and GODPR datasets show that the proposed re-id network outperforms other state-of-the-art networks. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Retail stores

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An Opto-Electronic HfOx-Based Transparent Memristive Synapse for Neuromorphic Computing System

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IEEE Transactions on Electron Devices 2023年第3期70卷 1351-1358页

作者： Saleem, Aftab Kumar, Dayanand Wu, Facai Keong, Lai Boon Tseng, Tseung-Yuen National Yang Ming Chiao Tung University Department of Electrical Engineering and Computer Science Hsinchu30010 Taiwan Laboratory Electrical and Computer Engineering Thuwal23955 Saudi Arabia Institute of Electronics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

In this study, a transparent bilayer memristor showing both electrical and optical synapses along with good electrical properties after annealing is presented. In addition to 85% transparency, the device shows excellent electrical characteristics for 1000 cycles of stable LRS/HRS and more than 104 s retention at high temperatures. The annealed device also exhibits stable potentiation and depression cycles for more than 10 000 ac pulses with a low coefficient of nonlinearity. By applying consecutive ac pulses, synaptic properties of paired-pulse facilitation (PPF) and spike time-dependent plasticity (STDP) are calculated. The memristor is illuminated by a 405 nm light source in which different light intensities ranging from 20 to 40 mW/cm2 are used for achieving multilevel cell (MLC) characteristics. Learning/Forgetting curve (PSC) and optical PPF are measured to mimic optical synaptic function. An image recognition comparison of optical and electrical synaptic properties with a normalized loss rate of © 2023 IEEE.

关键词： Tin oxides

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Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition

Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-S...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Wu, Wenhan Hua, Yilei Zheng, Ce Wu, Shiqian Chen, Chen Lu, Aidong University of North Carolina at Charlotte Department of Computer Science United States School of Information Science and Engineering Wuhan University of Science and Technology China Center for Research in Computer Vision University of Central Florida United States

ISBN: (纸本)9798350313154

Self-supervised skeleton-based action recognition has attracted more attention in recent years. By utilizing the unlabeled data, more generalizable features can be learned to alleviate the overfitting problem and reduce the demand for massive labeled training data. Inspired by the MAE [1], we propose a spatial-temporal masked autoencoder framework for self-supervised 3D skeleton-based action recognition (SkeletonMAE). Following MAE's masking and reconstruction pipeline, we utilize a skeleton-based encoder-decoder transformer architecture to reconstruct the masked skeleton sequences. A novel masking strategy, named Spatial-Temporal Masking, is introduced in terms of both joint-level and frame-level for the skeleton sequence. This pre-training strategy makes the encoder output generalizable skeleton features with spatial and temporal dependencies. Given the unmasked skeleton sequence, the encoder is fine-tuned for the action recognition task. Extensive experiments show that our SkeletonMAE achieves remarkable performance and outperforms the state-of-the-art methods on both NTU RGB+D 60 and NTU RGB+D 120 datasets. © 2023 IEEE.

关键词： Learning systems

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