A study of instrumental method for suiting fabric hand evaluation and classification

In the textile and apparel industry, fabric end-use preference and selection criteria are largely based on fabric hand because it relates to both the mechanical properties and aesthetic appearance of fabrics. This paper examines a method to grade fabric hand based on Kawabata’s measurements and neural network modeling. The proposed method is verified by comparing the hand graded by the neural network model to Kawabata’s total hand value. Ninety-five commercial fabrics from different manufacturers were tested using Kawabata evaluation system (KES-FB). Cluster analysis using SAS classified the suiting fabric samples into four groups in this study. The test results of fabric mechanical properties show similarities and dissimilarities between woven and knitted suiting fabrics. In comparison, woven suiting fabrics are less subject to shear and bending deformation. Knitted fabrics have a higher total hand value than woven fabrics with a smoother surface. Cluster analysis well divided the suiting fabric samples into four groups describing different fabric performance. The training dataset in the neural network model was selected based on information from the clustering results. The training model was proved to be accurate with a low MSE of 4 × 10-8. The model successfully graded the test samples with values ranged from 0 to 1. Additionally, the validity for grading fabric hand using the neural network technique was examined by analyzing the correlation between the hand graded by neural network model and Kawabata’s equations. The regression analysis shows a relatively strong correlation (p<0.0001, R2= 0.6363) between neural network grades and Kawabata’s grades.Textile and Apparel Technolog

Metasomatized lithospheric mantle for Mesozoic giant gold deposits in the North China craton

The origin of giant lode gold deposits of Mesozoic age in the North China craton (NCC) is enigmatic because high-grade metamorphic ancient crust would be highly depleted in gold. Instead, lithospheric mantle beneath the crust is the likely source of the gold, which may have been anomalously enriched by metasomatic processes. However, the role of gold enrichment and metasomatism in the lithospheric mantle remains unclear. Here, we present comprehensive data on gold and platinum group element contents of mantle xenoliths (n = 28) and basalts (n = 47) representing the temporal evolution of the eastern NCC. The results indicate that extensive mantle metasomatism and hydration introduced some gold (<1–2 ppb) but did not lead to a gold-enriched mantle. However, volatile-rich basalts formed mainly from the metasomatized lithospheric mantle display noticeably elevated gold contents as compared to those from the asthenosphere. Combined with the significant inheritance of mantle-derived volatiles in auriferous fluids of ore bodies, the new data reveal that the mechanism for the formation of the lode gold deposits was related to the volatile-rich components that accumulated during metasomatism and facilitated the release of gold during extensional craton destruction and mantle melting. Gold-bearing, hydrous magmas ascended rapidly along translithospheric fault zones and evolved auriferous fluids to form the giant deposits in the crust

Deep Learning Framework for Online Interactive Service Recommendation in Iterative Mashup Development

Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases the selection burden of software developers in developing service-based systems (such as mashups). How to recommend suitable follow-up component services to develop new mashups has become a fundamental problem in service-oriented software engineering. Most of the existing service recommendation approaches are designed for mashup development in the single-round recommendation scenario. It is hard for them to update recommendation results in time according to developers' requirements and behaviors (e.g., instant service selection). To address this issue, we propose a deep-learning-based interactive service recommendation framework named DLISR, which aims to capture the interactions among the target mashup, selected services, and the next service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending the next service. We also design two separate models for learning interactions from the perspectives of content information and historical invocation information, respectively, as well as a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR outperforms several state-of-the-art service recommendation methods in the online interactive scenario for developing new mashups iteratively.Comment: 15 pages, 6 figures, and 3 table

Heterodimerisation between VEGFR-1 and VEGFR-2 and not the homodimers of VEGFR-1 inhibit VEGFR-2 activity

Vascular endothelial growth factor (VEGF) signaling is tightly regulated by specific VEGF receptors (VEGF-R). Recently, we identified heterodimerisation between VEGFR-1 and VEGFR-2 (VEGFR1–2) to regulate VEGFR-2 function. However, both the mechanism of action and the relationship with VEGFR-1 homodimers remain unknown. The current study shows that activation of VEGFR1–2, but not VEGFR-1 homodimers, inhibits VEGFR-2 receptor phosphorylation under VEGF stimulation in human endothelial cells. Furthermore, inhibition of phosphatidylinositol 3-kinase (PI3K) increases VEGFR-2 phosphorylation under VEGF stimulation. More importantly, inhibition of PI3K pathway abolishes the VEGFR1–2 mediated inhibition of VEGFR-2 phosphorylation. We further demonstrate that inhibition of PI3K pathway promotes capillary tube formation. Finally, the inhibition of PI3K abrogates the inhibition of in vitro angiogenesis mediated by VEGFR1–2 heterodimers. These findings demonstrate that VEGFR1–2 heterodimers and not VEGFR-1 homodimers inhibit VEGF-VEGFR-2 signaling by suppressing VEGFR-2 phosphorylation via PI3K pathway

FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue

Pre-trained language models based on general text enable huge success in the NLP scenario. But the intrinsical difference of linguistic patterns between general text and task-oriented dialogues makes existing pre-trained language models less useful in practice. Current dialogue pre-training methods rely on a contrastive framework and face the challenges of both selecting true positives and hard negatives. In this paper, we propose a novel dialogue pre-training model, FutureTOD, which distills future knowledge to the representation of the previous dialogue context using a self-training framework. Our intuition is that a good dialogue representation both learns local context information and predicts future information. Extensive experiments on diverse downstream dialogue tasks demonstrate the effectiveness of our model, especially the generalization, robustness, and learning discriminative dialogue representations capabilities.Comment: ACL 2023 Main Conferenc

Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery

Generalized intent discovery aims to extend a closed-set in-domain intent classifier to an open-world intent set including in-domain and out-of-domain intents. The key challenges lie in pseudo label disambiguation and representation learning. Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn. In this paper, we propose a decoupled prototype learning framework (DPL) to decouple pseudo label disambiguation and representation learning. Specifically, we firstly introduce prototypical contrastive representation learning (PCL) to get discriminative representations. And then we adopt a prototype-based label disambiguation method (PLD) to obtain pseudo labels. We theoretically prove that PCL and PLD work in a collaborative fashion and facilitate pseudo label disambiguation. Experiments and analysis on three benchmark datasets show the effectiveness of our method.Comment: Accepted at ACL2023 main conferenc

Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation

Existing controllable dialogue generation work focuses on the single-attribute control and lacks generalization capability to out-of-distribution multiple attribute combinations. In this paper, we explore the compositional generalization for multi-attribute controllable dialogue generation where a model can learn from seen attribute values and generalize to unseen combinations. We propose a prompt-based disentangled controllable dialogue generation model, DCG. It learns attribute concept composition by generating attribute-oriented prompt vectors and uses a disentanglement loss to disentangle different attributes for better generalization. Besides, we design a unified reference-free evaluation framework for multiple attributes with different levels of granularities. Experiment results on two benchmarks prove the effectiveness of our method and the evaluation metric.Comment: ACL 2023 Main Conferenc

Angiogenic properties of placenta-derived extracellular vesicles in normal pregnancy and in preeclampsia

Angiogenesis is one of the main processes that coordinate the biological events leading to a successful pregnancy, and its imbalance characterizes several pregnancy-related diseases, including preeclampsia. Intracellular interactions via extracellular vesicles (EVs) contribute to pregnancy’s physiology and pathophysiology, and to the fetal–maternal interaction. The present review outlines the implications of EV-mediated crosstalk in the angiogenic process in healthy pregnancy and its dysregulation in preeclampsia. In particular, the effect of EVs derived from gestational tissues in pro and anti-angiogenic processes in the physiological and pathological setting is described. Moreover, the application of EVs from placental stem cells in the clinical setting is reported

Comprehensive molecular diagnosis of 67 Chinese Usher syndrome probands: high rate of ethnicity specific mutations in Chinese USH patients

Both Novel missense alleles identified in MYO7A genes are conserved between human, zebrafish and Drosophila melanogaster. (PPTX 676 kb