Wafer-scale synthesis of p-type TMD films is critical for its commercialization in next-generation electro/*** this work,wafer-scale intrinsic n-type WS_(2)films and in situ Nb-doped p-type WS_(2)films were synthesize...
详细信息
Wafer-scale synthesis of p-type TMD films is critical for its commercialization in next-generation electro/*** this work,wafer-scale intrinsic n-type WS_(2)films and in situ Nb-doped p-type WS_(2)films were synthesized through atomic layer deposition(ALD)on 8-inchα-Al_(2)O_(3)/Si wafers,2-inch sapphire,and 1 cm^(2)GaN substrate *** Nb doping concentration was precisely controlled by altering cycle number of Nb precursor and activated by ***_(2)n-FETs and Nb-doped p-FETs with different Nb concentrations have been fabricated using CMOS-compatible processes.X-ray photoelectron spectroscopy,Raman spectroscopy,and Hall measurements confirmed the effective substitutional doping with *** on/off ratio and electron mobility of WS_(2)n-FET are as high as 105 and 6.85 cm^(2)V^(-1)s^(-1),*** WS_(2)p-FET with 15-cycle Nb doping,the on/off ratio and hole mobility are 10 and 0.016 cm^(2)V^(-1)s^(-1),*** p-n structure based on n-and p-type WS_(2)films was proved with a 10^(4)rectifying *** realization of controllable in situ Nb-doped WS_(2)films paved a way for fabricating wafer-scale complementary WS_(2)FETs.
Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and co...
详细信息
The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks f...
详细信息
The emergence of microneedle arrays(MNAs)as a novel,simple,and minimally invasive administration approach largely addresses the challenges of traditional drug *** particular,the dissolvable MNAs act as a promising,mul...
详细信息
The emergence of microneedle arrays(MNAs)as a novel,simple,and minimally invasive administration approach largely addresses the challenges of traditional drug *** particular,the dissolvable MNAs act as a promising,multifarious,and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation *** effective delivery mostly depends on the behavior of the MNAs penetrated into the body,and accurate assessment is urgently *** imaging technologies offer high sensitivity and resolution visualization of cross-scale,multidimensional,and multiparameter information,which can be used as an important aid for the evaluation and development of new *** combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various *** addition,noninvasive imaging techniques allow rapid,receptive assessment of transdermal penetration and drug deposition in various tissues,which could greatly facilitate the translation of experimental MNAs into clinical *** on the recent promising development of bioimaging,this review is aimed at summarizing the current status,challenges,and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies.
The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of ...
详细信息
ISBN:
(数字)9798331520861
ISBN:
(纸本)9798331520878
The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.
Recommender systems are critical for mitigating information overload, assisting users in uncovering their latent interests, and enhancing their overall experience. Sequential recommendation leverages users' histor...
详细信息
ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
Recommender systems are critical for mitigating information overload, assisting users in uncovering their latent interests, and enhancing their overall experience. Sequential recommendation leverages users' historical interaction sequences to predict dynamic interests more effectively than traditional rec-ommendation approaches. However, existing models-including RNN-based and Transformer-based methods-face significant limitations. RNNs struggle with vanishing gradients and long-term dependency capture, while Transformers, though effective for long-range relationships, suffer from computational inefficiency due to their quadratic attention complexity. Recent advancements have employed contrastive learning for sequential recommendation, aiming to enhance the consistency between augmented views and improve self-supervised learning signals. Despite their promise, these methods often lack diversity in data augmentation strategies, which restricts their capacity for bias mitigation, resulting in augmented data that still retains inherent biases. To address these challenges, we propose MDEC, a novel sequential modeling framework that leverages State Space Models (SSM) combined with unbiased contrastive learning. MDEC utilizes Mamba to efficiently model user preferences as an alternative to Transformer-based models. Additionally, it integrates graph-based information, including item transition and co-interaction data, to improve data augmentation comprehensively. Finally, we introduce adaptive anchor-enhanced contrastive learning, which adaptively utilizes augmented samples to improve representation quality and bias mitigation. Extensive experiments on multiple datasets demonstrate that MDEC significantly out-performs existing models, showcasing improved efficiency, better mitigation of biases, and enhanced recommendation quality. Code is available at https://***/Echohuangyan/CSLP.
Cross-modal retrieval is a technique that uses one modality to query another modality in multimedia data (e.g., retrieving images based on text, or retrieving text based on images). It can break down the barriers betw...
详细信息
In personalized recommendation systems, Graph Neural networks (GNNs) have gained attention for their ability to effectively model complex user-item interactions. The core concept of GNNs is to leverage ample and high-...
详细信息
ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
In personalized recommendation systems, Graph Neural networks (GNNs) have gained attention for their ability to effectively model complex user-item interactions. The core concept of GNNs is to leverage ample and high-quality training data, recursively performing message passing along user-item interaction edges to progressively refine embedding representations. However, the highly imbalanced nature of user interaction data often leads traditional GNN-based recommendation systems to experience information redundancy, concentrating recommendations on a few popular items. Additionally, the long-tail problem further limits the visibility of less popular items, reducing recommendation diversity and impairing the user experience. In this paper, we propose a novel Diversity Augmented Graph Contrastive Learning method (DAGCL), which aims to increase recommendation diversity by improving the embedding generation process, while maintaining a balance between accuracy and diversity. Specifically, we design two graph augmentation methods, including a diversified subset selection module based on Deterministic Point Process (DPP) and a long-tail item interaction augmentation module, to generate diversified embeddings. Moreover, DAGCL incorporates contrastive learning, leveraging augmented views to optimize the recommendation process, alleviate data sparsity issue and capture potential user-item relationships. Experiments on two real-world datasets demonstrate that DAGCL significantly improves diversity while preserving recommendation accuracy, achieving an effective balance between these two aspects and validating its effectiveness. The code is available on https://***/XiaHaoZhi/DAGCL.
The goal of Emotion Recognition in Conversations (ERC) is to accurately identify the emotions expressed in each utterance within a dialogue. Despite advancements made by current ERC methods, particularly those using R...
详细信息
ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
The goal of Emotion Recognition in Conversations (ERC) is to accurately identify the emotions expressed in each utterance within a dialogue. Despite advancements made by current ERC methods, particularly those using RNN-based and GCN-based models to capture emotional dynamics and model speaker relationships, there remain two primary limitations: first, an insufficient integration of multiple feature representations and commonsense knowledge, which hampers the model's ability for deep emotional understanding; and second, the reliance on a single cross-entropy loss for classification optimization, which restricts the accuracy and robustness of emotion recognition. To address these issues, we propose a method for ERC using Multilayer Feature Fusion and Joint Loss Optimization (MFFJL). This approach combines contextual information, speaker dependency, and commonsense knowledge features by extracting feature vectors through RoBERTa and COMET, utilizing bidirectional LSTM and attention mechanisms to capture conversational context, and applying a cross-fusion module to deeply integrate various features, thus enhancing comprehension of complex emotional expressions. Additionally, the feature classification module incorporates joint cross-entropy and KL divergence optimization, further improving classification accuracy and consistency. Experimental results demonstrate the effectiveness of our method, as evidenced by superior performance on the IEMOCAP and MELD datasets. Our code is available at https://***/r/MFFJL-D9D8.
The article is dedicated to advancing technologies in chaotic optical communication and investigating chaotic laser generation in measurement systems and instruments. It conducts an analysis of the informational poten...
详细信息
暂无评论