Overcoming barriers to reaching nativelikeness in adult second language acquisition

This thesis examines the complex questions of what the obstacles are to becoming nativelike and how they can be overcome. Questions for framing the literature review are developed by means of a down-to-earth preliminary case study of a nativelike French learner of English. The subsequent literature review focuses on key issues such as the supply of input, attention to input, output practise opportunities, attention to output, identity, and learning motivation. An 'ideal' model for reaching nativelikeness is established for further investigation. More specifically, five conditions for overcoming barriers to reaching nativelikeness are hypothesised. In order to test these five conditions, an investigation is reported into the learning of Mandarin by a cohort of undergraduate students of Mandarin at a British university. Using carefully constructed interview questions and questionnaires, details were gathered of their knowledge, approach and attitude to learning, and how they lived during their year abroad in China. Their nativelikeness was judged by independent monolingual Chinese listeners. The main findings are that there are different learning obstacles in the process of L2 learning for different learners, due to both their different language learning experiences and their particular stances relative to the target language. The key conclusion of the study is that nativelikeness is most likely to be achieved when learners have a persistent motivation to speak in a nativelike manner, develop an open/adaptive sense of identification with the L2 native group, have a guaranteed supply of on-going 'ideal' input, and achieve a 'balanced' attention to both input and output

VeriQR:A robustness verification tool for quantum machine learning models

Adversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce VeriQR, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware’s noisy impacts by incorporating random noise to formally validate a QML model’s robustness. VeriQR supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, VeriQR can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess VeriQR using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing

A cross-chain access control mechanism based on blockchain and the threshold Paillier cryptosystem

Si, H., Li, W., Su, N., Li, T., Li, Y., Zhang, C., Bação, F., & Sun, C. (2024). A cross-chain access control mechanism based on blockchain and the threshold Paillier cryptosystem. Computer Communications. https://doi.org/10.1016/j.comcom.2024.05.012 --- The authors acknowledge the Henan Province key research and development project under Grant No. 231111110100, the Henan Provincial Science and Technology Research Project under Grant No. 232102520006, the Henan Province key research and development project under Grant No. 231111211300, the Henan Province Key Science-technology Research Project under Grant No. 232102210122, the Key Research Project of Henan Provincial Higher Education Institution under Grant No. 23A520005.With the continuous maturation of blockchain technology and the increasing demands for various industry applications, data sharing and interoperability among different blockchain networks face significant challenges. Research on cross-chain interoperability mechanisms has facilitated data collaboration across organizations and industries, enhancing the value and utility of data. When engaging in cross-chain data interactions, access control ensures the security and privacy of data while promoting collaboration and information exchange among multiple chains. Attribute-based access control can provide fine-grained authorization support, matching complex business scenarios. However, publicly disclosed policies and attributes in a transparent blockchain network may pose privacy and security issues. To address these issues, this paper proposes a cross-heterogeneous multichain data access control scheme based on attributes and threshold homomorphic encryption, achieving fine-grained and secure cross-domain access control in cross-chain networks. This scheme uses the threshold Paillier cryptosystem to encrypt and conceal user attributes and access policies. Through smart contracts, homomorphic differential computation is performed on the ciphertext of policies and attributes, to protect data privacy. This solution leverages private key decryption shares from multiple relay nodes in the cross-chain network to jointly decrypt the computation results, providing secure access control in complex cross-chain scenarios. Security analysis and experimental results demonstrate that the proposed scheme ensures security with reasonable computational overhead, to meet the access control requirements in cross-chain networks.publishersversionpublishe

DUAL EFFECTS OF BILIRUBIN ON THE PROLIFERATION OF RAT RENAL NRK52E CELLS AND ITS ASSOCIATION WITH GAP JUNCTIONS

Objective: The effect of bilirubin on renal pathophysiology is controversial. This study aimed to observe the effects of bilirubin on the proliferation of normal rat renal tubular epithelial cell line (NRK52E) and its potential interplay with gap junction function. Methods: Cultured NRK52E cells, seeded respectively at high- or low- densities, were treated with varying concentrations of bilirubin for 24 hours. Cell injury was assessed by measuring cell viability and proliferation, and gap junction function was assessed by Parachute dye-coupling assay. Connexin 43 protein was assessed by Western blotting. Results: At doses from 17.1 to 513μmol/L, bilirubin dose-dependently enhanced cell viability and colony-formation rates when cells were seeded at either high- or low- densities (all p\u3c0.05 vs. solvent group) accompanied with enhanced intercellular fluorescence transmission and increased Cx43 protein expression in high-density cells. However, the above effects of BR were gradually reversed when its concentration increased from 684 to 1026μmol/L. In high-density cells, gap junction inhibitor 12-O-tetradecanoylphorbol 13- acetate attenuated bilirubin-induced enhancement of colony-formation and fluorescence transmission. However, in the presence of high concentration bilirubin (1026μmol/L), activation of gap junction with retinoid acid decreased colony-formation rates. Conclusion: Bilirubin can confer biphasic effects on renal NRK52E cell proliferation potentially by differentially affecting gap junction functions

Reactive Astrocytes in Glial Scar Attract Olfactory Ensheathing Cells Migration by Secreted TNF-α in Spinal Cord Lesion of Rat

BACKGROUND:After spinal cord injury (SCI), the formation of glial scar contributes to the failure of injured adult axons to regenerate past the lesion. Increasing evidence indicates that olfactory ensheathing cells (OECs) implanted into spinal cord are found to migrate into the lesion site and induce axons regeneration beyond glial scar and resumption of functions. However, little is known about the mechanisms of OECs migrating from injection site to glial scar/lesion site. METHODS AND FINDINGS:In the present study, we identified a link between OECs migration and reactive astrocytes in glial scar that was mediated by the tumor necrosis factor-alpha (TNF-alpha). Initially, the Boyden chamber migration assay showed that both glial scar tissue and reactive astrocyte-conditioned medium promoted OECs migration in vitro. Reactive astrocyte-derived TNF-alpha and its type 1 receptor TNFR1 expressed on OECs were identified to be responsible for the promoting effect on OECs migration. TNF-alpha-induced OECs migration was demonstrated depending on activation of the extracellular signal-regulated kinase (ERK) signaling cascades. Furthermore, TNF-alpha secreted by reactive astrocytes in glial scar was also showed to attract OECs migration in a spinal cord hemisection injury model of rat. CONCLUSIONS:These findings showed that TNF-alpha was released by reactive astrocytes in glial scar and attracted OECs migration by interacting with TNFR1 expressed on OECs via regulation of ERK signaling. This migration-attracting effect of reactive astrocytes on OECs may suggest a mechanism for guiding OECs migration into glial scar, which is crucial for OECs-mediated axons regrowth beyond the spinal cord lesion site

VeriQR: A Robustness Verification Tool for Quantum Machine Learning Models

Adversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce \textit{VeriQR}, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware\u27s noisy impacts by incorporating random noise to formally validate a QML model\u27s robustness. \textit{VeriQR} supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, \textit{VeriQR} can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess \textit{VeriQR} using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by \textit{VeriQR}, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing