Probability-Dependent Gradient Decay in Large Margin Softmax

In the past few years, Softmax has become a common component in neural network frameworks. In this paper, a gradient decay hyperparameter is introduced in Softmax to control the probability-dependent gradient decay rate during training. By following the theoretical analysis and empirical results of a variety of model architectures trained on MNIST, CIFAR-10/100 and SVHN, we find that the generalization performance depends significantly on the gradient decay rate as the confidence probability rises, i.e., the gradient decreases convexly or concavely as the sample probability increases. Moreover, optimization with the small gradient decay shows a similar curriculum learning sequence where hard samples are in the spotlight only after easy samples are convinced sufficiently, and well-separated samples gain a higher gradient to reduce intra-class distance. Based on the analysis results, we can provide evidence that the large margin Softmax will affect the local Lipschitz constraint of the loss function by regulating the probability-dependent gradient decay rate. This paper provides a new perspective and understanding of the relationship among concepts of large margin Softmax, local Lipschitz constraint and curriculum learning by analyzing the gradient decay rate. Besides, we propose a warm-up strategy to dynamically adjust Softmax loss in training, where the gradient decay rate increases from over-small to speed up the convergence rate

The Design Concept and Realization of University Library’s Online Lecture System

The traditional library lectures are based on the physical space environment, which is problematic in terms of user needs analysis, time selection, and resource utilization, so that the value of the library unexploited.University library’s online lecture system adopts the three-tier architecture design pattern, which based on ASP.NET as the development platform and SQL Server as the back-end database, designing the system to solve the problems in the traditional library service.The design and implementation of the University library’s online lecture system broke the traditional library lecture mode, and further optimized the library service concept, improve the quality of service to meet the diverse needs of the user groups. Integrating online test, interactive question and answer, feedback mechanism to the system, the system has a good user experience, visual way to show the audience’s learning situation and other information, optimize the content and quality of services to make comments and suggestions

Distribution shift mitigation at test time with performance guarantees

Due to inappropriate sample selection and limited training data, a distribution shift often exists between the training and test sets. This shift can adversely affect the test performance of Graph Neural Networks (GNNs). Existing approaches mitigate this issue by either enhancing the robustness of GNNs to distribution shift or reducing the shift itself. However, both approaches necessitate retraining the model, which becomes unfeasible when the model structure and parameters are inaccessible. To address this challenge, we propose FR-GNN, a general framework for GNNs to conduct feature reconstruction. FRGNN constructs a mapping relationship between the output and input of a well-trained GNN to obtain class representative embeddings and then uses these embeddings to reconstruct the features of labeled nodes. These reconstructed features are then incorporated into the message passing mechanism of GNNs to influence the predictions of unlabeled nodes at test time. Notably, the reconstructed node features can be directly utilized for testing the well-trained model, effectively reducing the distribution shift and leading to improved test performance. This remarkable achievement is attained without any modifications to the model structure or parameters. We provide theoretical guarantees for the effectiveness of our framework. Furthermore, we conduct comprehensive experiments on various public datasets. The experimental results demonstrate the superior performance of FRGNN in comparison to mainstream methods

Biobased poly(ethylene-co-hexamethylene 2,5-furandicarboxylate) (PEHF) copolyesters with superior tensile properties

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Modification of poly(ethylene 2,5-furandicarboxylate) with aliphatic polycarbonate diols : 1.Randomnized copolymers with significantly improved ductility and high CO2 barrier performance

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Poly(ethylene 2,5-furandicarboxylate-mb-poly(tetramethylene glycol) multiblock copolymers : from high tough thermoplastics to elastomers

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Modification of poly(ethylene 2,5-furandicarboxylate)with biobased 1,5-pentanediol : significantly toughened copolyesters retaining high tensile strength ad O2 barrier property

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In-situ synthesis, thermal and mechanical properties of biobased poly (ethylene 2,5-furandicarboxylate)/montmorillonite (PEF/MMT) nanocomposites

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Aliphatic polycarbonate modified poly(ethylene furandicarboxylate) materials with improved ductility, toughness and high CO2 barrier performance

peer reviewedPoly(ethylene 2,5-furandicarboxylate) (PEF) is a promising biobased polymer possessing high strength, rigidity and gas barrier performance, but its poor ductility and toughness may limit its practical applications. In order to obtain PEF materials with improved ductility and impact toughness as well as high strength, modulus and excellent gas barrier performance, PEF with relatively low molecular weight was modified with aliphatic polycarbonate (APC) diols by chain extension/coupling in this study. The resulting products were mixtures composed of randomly segmented copolymers, chain extended APC and chain extended PEF. The APC moiety was proved to be partially miscible with the PEF matrix, and therefore plasticized the PEF matrix and promoted its cold crystallization. In comparison with PEF, the modified PEFs possess significantly enhanced tensile ductility and impact toughness. Particularly, the modified PEF containing 15 wt% poly(hexamethylene carbonate) exhibits balanced mechanical properties and CO2 barrier 5 times to PET

A retrospective comparative study on the diagnostic efficacy and the complications: between CassiII rotational core biopsy and core needle biopsy

Accurate pathologic diagnosis and molecular classification of breast mass biopsy tissue is important for determining individualized therapy for (neo)adjuvant systemic therapies for invasive breast cancer. The CassiII rotational core biopsy system is a novel biopsy technique with a guide needle and a “stick-freeze” technology. The comprehensive assessments including the concordance rates of diagnosis and biomarker status between CassiII and core needle biopsy were evaluated in this study. Estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed through immunohistochemistry. In total, 655 patients with breast cancer who underwent surgery after biopsy at Sir Run Run Shaw Hospital between January 2019 to December 2021 were evaluated. The concordance rates (CRs) of malignant surgical specimens with CassiII needle biopsy was significantly high compared with core needle biopsy. Moreover, CassiII needle biopsy had about 20% improvement in sensitivity and about 5% improvement in positive predictive value compared to Core needle biopsy. The characteristics including age and tumor size were identified the risk factors for pathological inconsistencies with core needle biopsies. However, CassiII needle biopsy was associated with tumor diameter only. The CRs of ER, PgR, HER2, and Ki67 using Cassi needle were 98.08% (kappa, 0.941; p<.001), 90.77% (kappa, 0.812; p<.001), 69.62% (kappa, 0.482; p<.001), and 86.92% (kappa, 0.552; p<.001), respectively. Post-biopsy complications with CassiII needle biopsy were also collected. The complications of CassiII needle biopsy including chest stuffiness, pain and subcutaneous ecchymosis are not rare. The underlying mechanism of subcutaneous congestion or hematoma after CassiII needle biopsy might be the larger needle diameter and the effect of temperature on coagulation function. In summary, CassiII needle biopsy is age-independent and has a better accuracy than CNB for distinguishing carcinoma in situ and invasive carcinoma