Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces

A new Hardy space Hardy space approach of Dirichlet type problem based on Tikhonov regularization and Reproducing Hilbert kernel space is discussed in this paper, which turns out to be a typical extremal problem located on the upper upper-high complex plane. If considering this in the Hardy space, the optimization operator of this problem will be highly simplified and an efficient algorithm is possible. This is mainly realized by the help of reproducing properties of the functions in the Hardy space of upper-high complex plane, and the detail algorithm is proposed. Moreover, harmonic mappings, which is a significant geometric transformation, are commonly used in many applications such as image processing, since it describes the energy minimization mappings between individual manifolds. Particularly, when we focus on the planer mappings between two Euclid planer regions, the harmonic mappings are exist and unique, which is guaranteed solidly by the existence of harmonic function. This property is attractive and simulation results are shown in this paper to ensure the capability of applications such as planer shape distortion and surface registration.Comment: 2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS

E2Net: Resource-Efficient Continual Learning with Elastic Expansion Network

Continual Learning methods are designed to learn new tasks without erasing previous knowledge. However, Continual Learning often requires massive computational power and storage capacity for satisfactory performance. In this paper, we propose a resource-efficient continual learning method called the Elastic Expansion Network (E2Net). Leveraging core subnet distillation and precise replay sample selection, E2Net achieves superior average accuracy and diminished forgetting within the same computational and storage constraints, all while minimizing processing time. In E2Net, we propose Representative Network Distillation to identify the representative core subnet by assessing parameter quantity and output similarity with the working network, distilling analogous subnets within the working network to mitigate reliance on rehearsal buffers and facilitating knowledge transfer across previous tasks. To enhance storage resource utilization, we then propose Subnet Constraint Experience Replay to optimize rehearsal efficiency through a sample storage strategy based on the structures of representative networks. Extensive experiments conducted predominantly on cloud environments with diverse datasets and also spanning the edge environment demonstrate that E2Net consistently outperforms state-of-the-art methods. In addition, our method outperforms competitors in terms of both storage and computational requirements

Siamese Labels Auxiliary Network(SiLaNet)

Auxiliary information attracts more and more attention in the area of machine learning. Attempts so far to include such auxiliary information in state-of-the-art learning process have often been based on simply appending these auxiliary features to the data level or feature level. In this paper, we intend to propose a novel training method with new options and architectures. Siamese labels, which were used in the training phase as auxiliary modules. While in the testing phase, the auxiliary module should be removed. Siamese label module makes it easier to train and improves the performance in testing process. In general, the main contributions can be summarized as, 1) Siamese Labels are firstly proposed as auxiliary information to improve the learning efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary Network (SilaNet), which is to assist the training of the model; 3) Siamese Labels Auxiliary Network is applied to compress the model parameters by 50% and ensure the high accuracy at the same time. For the purpose of comparison, we tested the network on CIFAR-10 and CIFAR100 using some common models. The proposed SilaNet performs excellent efficiency both on the accuracy and robustness

Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction

Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of existing bundles. However, it is hard to acquire large-scale well-curated bundle dataset, especially for those platforms that have not offered bundle services before. Even for platforms with mature bundle services, there are still many items that are included in few or even zero bundles, which give rise to sparsity and cold-start challenges in the bundle construction models. To tackle these issues, we target at leveraging multimodal features, item-level user feedback signals, and the bundle composition information, to achieve a comprehensive formulation of bundle construction. Nevertheless, such formulation poses two new technical challenges: 1) how to learn effective representations by optimally unifying multiple features, and 2) how to address the problems of modality missing, noise, and sparsity problems induced by the incomplete query bundles. In this work, to address these technical challenges, we propose a Contrastive Learning-enhanced Hierarchical Encoder method (CLHE). Specifically, we use self-attention modules to combine the multimodal and multi-item features, and then leverage both item- and bundle-level contrastive learning to enhance the representation learning, thus to counter the modality missing, noise, and sparsity problems. Extensive experiments on four datasets in two application domains demonstrate that our method outperforms a list of SOTA methods. The code and dataset are available at https://github.com/Xiaohao-Liu/CLHE

Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty.Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask.The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the the annotations' union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex Nodule Validation on LIDC-IDRI, which tests UGMCS-Net's segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods.Comment: 10 pages, 10 figures. We have reported a preliminary version of this work in MICCAI 202

Plasmonic hot electrons for sensing, photodetection, and solar energy applications: A perspective

In plasmonic metals, surface plasmon resonance decays and generates hot electrons and hot holes through non-radiative Landau damping. These hot carriers are highly energetic, which can be modulated by the plasmonic material, size, shape, and surrounding dielectric medium. A plasmonic metal nanostructure, which can absorb incident light in an extended spectral range and transfer the absorbed light energy to adjacent molecules or semiconductors, functions as a “plasmonic photosensitizer.” This article deals with the generation, emission, transfer, and energetics of plasmonic hot carriers. It also describes the mechanisms of hot electron transfer from the plasmonic metal to the surface adsorbates or to the adjacent semiconductors. In addition, this article highlights the applications of plasmonic hot electrons in photodetectors, photocatalysts, photoelectrochemical cells, photovoltaics, biosensors, and chemical sensors. It discusses the applications and the design principles of plasmonic materials and devices

Family functioning as a moderator in the relation between perceived stress and psychotic-like experiences among adolescents during COVID-19

Background: The COVID-19 pandemic has increased psychological stress among adolescents, and the relation between perceived stress (PS) and psychotic-like experiences (PLEs) has been well-established. However, little is known about the role of family functioning (FF) in this relation, especially when adolescents experienced the extended lockdown period with family members. Methods: A total of 4807 adolescents completed this retrospective paper-and-pencil survey after school reopening between May 14th and June 6th, 2020 in Hunan Province, China. We measured PS with the Perceived stress scale (PSS-10), PLEs with the eight positive items from Community Assessment of Psychic Experiences (CAPE-8), and FF with the Family APGAR scale. We conducted subgroup analysis based on three FF levels (good, moderate, and poor) determined by previous studies. Finally, correlation and moderation analysis were performed to detect the effect of FF in the relation between PS and PLEs after adjusting for demographic variables. Results: Adolescents with poor FF had higher levels of PS and higher prevalence of PLEs compared to those with good FF (both p \u3c 0.001). FF was negatively associated with both PS (r = −0.34, p \u3c 0.001) and PLEs (r = −0.29, p \u3c 0.001). Higher FF significantly attenuated the effect of PS on PLEs after adjusting for sex and age (effect = −0.011, bootstrap 95% CI -0.018, −0.005). Conclusion: Our findings indicate that well-functioned family could protect against stress-induced PLEs among adolescents during this crisis. Thus family system could be an early interventional target for distressing psychotic-like experiences in youngsters

Enhanced secretion of hepatocyte growth factor in human umbilical cord mesenchymal stem cells ameliorates pulmonary fibrosis induced by bleomycin in rats

Umbilical cord mesenchymal stem cells (UCMSCs) are a reportedly promising choice in the treatment of irreversible pulmonary fibrosis and lethal interstitial lung disease with limited drug treatment options. In this study, we investigated the therapeutic efficacy of UCMSCs overexpressing hepatocyte growth factor (HGF), which is considered one of the main anti-fibrotic factors secreted by MSCs. Adenovirus vector carrying the HGF gene was transfected into UCMSCs to produce HGF-modified UCMSCs (HGF-UCMSCs). Transfection promoted the proliferation of UCMSCs and did not change the morphology, and differentiation ability, or biomarkers. Rats were injected with HGF-UCMSCs on days 7 and 11 after intratracheal administration of bleomycin (10 mg/kg). We performed an analysis of histopathology and lung function to evaluate the anti-fibrotic effect. The results showed that HGF-UCMSCs decreased the Ashcroft scores in hematoxylin and eosin-stained sections, the percentage positive area in Masson trichrome-stained sections, and the hydroxyproline level in lungs. Forced expiratory volume in the first 300 m/forced vital capacity was also improved by HGF-UCMSCs. To explore the possible therapeutic mechanism of HGF-UCMSCs, we detected inflammatory factors in the lungs and performed mRNA sequencing in UCMSCs and HGF-UCMSCs. The data indicated that inhibition of interleukin-17 in the lung may be related to the anti-fibrosis of HGF-UCMSCs, and overexpressed HGF probably played a primary role in the treatment. Collectively, our study findings suggested that the overexpression of HGF may improve the anti-fibrotic effect of UCMSCs through directly or indirectly interacting with interleukin-17-producing cells in fibrotic lungs

Expression of MK-1 and RegⅣ and its clinicopathological significances in the benign and malignant lesions of gallbladder

<p>Abstract</p> <p>Background</p> <p>To study the expression of MK-1 and RegⅣ and to detect their pathological significances in benign and malignant lesions of gallbladder.</p> <p>Methods</p> <p>The expression of MK-1 and RegⅣ was detected by immunohistochemical method in paraffin-embedded sections of surgical resected specimens from gallbladder adenocarcinoma (n = 108), peritumoral tissues (n = 46), adenomatous polyp (n = 15), and chronic cholecystitis (n = 35).</p> <p>Results</p> <p>The positive rate of MK-1 or RegⅣ expression was significantly higher in gallbladder adenocarcinoma than that in peritumoral tissues (χ²_MK-1= 18.76, <it>P </it>< 0.01; χ²_RegⅣ= 9.92, <it>P </it>< 0.01), denomatous polyp (χ²_MK-1= 9.49, <it>P </it>< 0.01; χ²_RegⅣ= 8.59, <it>P </it>< 0.01) and chronic cholecystitis (χ²_MK-1= 24.11, <it>P </it>< 0.01; χ²_RegⅣ= 19.24, <it>P </it>< 0.01). The positive cases of MK-1 and/or RegⅣ in the benign lesions showed moderately- or severe-atypical hyperplasia of gallbladder epitheli. The positive rates of MK-1 were significantly higher in the cases of well-differentiated adenocarcinoma, no-metastasis of lymph node, and no-invasiveness of regional tissues than those in the ones of differentiated adenocarcinoma, metastasis of lymph node, and invasiveness of regional tissues in gallbladder adenocarcinoma (<it>P </it>< 0.05 or <it>P </it>< 0.01). On the contrary, the positive rates of RegⅣ were significantly lower in the cases of well-differentiated adenocarcinoma, no-metastasis of lymph node, and no-invasiveness of regional tissues than those in the ones of differentiated adenocarcinoma, metastasis of lymph node, and invasiveness of regional tissues in gallbladder adenocarcinoma (<it>P </it>< 0.05 or <it>P </it>< 0.01). Univariate Kaplan-Meier analysis showed that decreased expression of MK-1 (<it>P </it>= 0.09) or increased expression of RegⅣ (<it>P </it>= 0.003) was associated with decreased overall survival. Multivariate Cox regression analysis showed that decreased expression of MK-1 (<it>P </it>= 0.033) and increased expression of RegⅣ (<it>P </it>= 0.008) was an independent prognostic predictor in gallbladder adenocarcinoma.</p> <p>Conclusions</p> <p>The expression of MK-1 and/or RegⅣ might be closely related to the carcinogenesis, clinical biological behaviors, and prognosis of gallbladder adenocarcinoma.</p