{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":65928,"offset":65918,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"The seven canonical networks from the Schaefer atlas identified in the dataset are:\n\n1. Frontoparietal Control Network\n2. Limbic Network\n3. Somatic-Motor Network\n4. Visual-Auditory Network\n5. Default Mode Network\n6. Dorsal Attention Network\n7. Ventral Attention Network","behavior":"degenerate_repetition","conversation_hash":"313fac85b797b2d2d3152fb5175dab91","conversation_redacted":false,"conversation_toxic":false,"country":"Iran","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-3.5-turbo-0301","n_chars":1898,"n_turns_in_conversation":1,"n_words":278,"source_row_offset":65918,"source_shard":"turns-00004.parquet","timestamp":"Sun, 11 Jun 2023 11:15:16 GMT","turn_id":"cfeffc57c96011f0d906caac","turn_index":0,"user_text":"In the end of the second paragraph of the discussion, please list all seven canonical networks from the Schaefer atlas.A disconnected subnetwork comprising of 30 reduced connections between 31 brain regions was identified in the raw connectome, whereas filtered tractogram identified 5 reduced connections between 6 brain regions in total. Raw and COMMIT2-weighted structural connectivity patterns overlapped in 5 networks, although the major differences were observed in subnetworks and laterality. In the filtered connectomes, we found a decrease in density compared to the raw connectomes in both patients with insomnia and controls, as reported previously (Schiavi et al., 2020b). This increased sparsity does not mean that valid connections are lost. Instead, the connections that are filtered out are incompatible with the underlying data (Schiavi et al., 2020a). According to this study, density change did not affect the variations in global and local features produced by COMMIT2-weighted connectomes (Schiavi et al., 2020b). The COMMIT2 framework improved the predicted brain networks\u2019 specificity while retaining their sensitivity (Schiavi et al., 2020a). We identified seven canonical networks that are based on the Schaefer atlas in our dataset (Schaefer et al., 2018). Goal-directed executive control is the responsibility of the frontoparietal control network. The limbic network processes emotions and affect. The SM network is in charge of motor movements and somatic sensations. The VA network handles attention to salient stimuli, and the DMN, which is active during rest as the default setting of the brain. 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dense release","language":"Chinese","model":"gpt-3.5-turbo-0301","n_chars":305,"n_turns_in_conversation":4,"n_words":4,"source_row_offset":65922,"source_shard":"turns-00004.parquet","timestamp":"Sun, 11 Jun 2023 11:17:23 GMT","turn_id":"fc7b8162d716218ad1769505","turn_index":3,"user_text":"\u4ece\u5366\u8c61\u6765\u770b\u600e\u4e48\u8bf4"},{"assistant_initiates":false,"asst_text":"1. Cheung, Y. W., & He, H. (2019). China's capital account liberalization: a review of developments, impact, and policy responses. Journal of Asian Economics, 65, 101170.\n\n2. Wang, Y., & Liu, J. (2020). Monetary policy and the internationalization of the renminbi. Journal of International Money and Finance, 108, 102102.\n\n3. Chen, C., & Zhang, H. (2019). Exchange rate volatility and exports of Chinese firms. Applied Economics Letters, 26(17), 1442-1446.\n\n4. Hu, Z., & Huang, Y. (2020). Interest rate liberalization and its impact on the Chinese economy. China Economic Review, 62, 101435.\n\n5. Zhu, H., & Nie, R. (2020). Exploring the dynamic relationship between exchange rate and trade: Evidence from China. International Review of Economics & Finance, 69, 947-960.\n\n6. Ma, Q. (2018). The impact of exchange rate movements on China's international trade. Contemporary Economics, 12(4), 349-361.\n\n7. Yang, Q., & Tang, J. (2019). Interest rate deregulation, exchange rate volatility and real estate prices in China. Pacific-Basin Finance Journal, 55, 290-306.\n\n8. Li, Y., Du, X., & Chen, B. (2020). Investigation on the impact of exchange rate fluctuations on China's cross-border e-commerce export trade. Journal of Cleaner Production, 272, 122626.\n\n9. Guo, Y., & Zhang, H. (2019). The influence of monetary policy on the internationalization of the renminbi. Economic Research Journal, (1), 119-133.\n\n10. Zhang, S., Li, H., & Gong, P. (2020). Interest rate liberalization and China's corporate financing structure: The case of manufacturing firms. Economic Modelling, 89, 232-242.\n\n11. Ding, N., & Li, H. (2019). Exchange rate fluctuations and foreign direct investment: Evidence from China. Review of Development Finance, 9(1), 15-24.\n\n12. Liu, K., & Liu, L. (2020). The impact of the exchange rate on exports: Evidence from China's agricultural products. Journal of International Trade & Economic Development, 29(1), 75-97.\n\n13. Wang, J., Qin, F., & Wang, X. (2019). RMB exchange rate and China's energy imports: Evidence from a simultaneous equation model. Energy Economics, 81, 1029-1039.\n\n14. Zhang, Y., & Liu, M. (2020). Interest rate liberalization and capital structure: Evidence from Chinese listed firms. Emerging Markets Finance and Trade, 56(7), 1671-1685.\n\n15. Shan, Q., & Ngai, L. R. T. (2019). Impact of interest rate changes on China's stock market. 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Review of International Economics, 23(5), 956-967.\n\n2. Steinberg, F. (2016). Monetary sovereignty in an interdependent world. Cornell International Law Journal, 49(2), 223-252.\n\n3. Ghaghada, Y. (2017). Financial liberalization, globalization and economic growth nexus: evidence from Brazil. Journal of Economic Studies, 44(2), 143-156.\n\n4. Bao, H., & Ouyang, Y. (2018). The impact of RMB internationalization on the spillover of Chinese monetary policy. Journal of International Money and Finance, 88, 100-111.\n\n5. Devereux, M. P., & Yetman, J. (2016). Globalisation, pass-through and the optimal policy response to exchange rates. Review of International Economics, 24(3), 550-568.\n\n6. Han, X., He, N., Li, S., & Zhang, B. (2019). Can natural gas spur sustainable development in China? Energy Strategy Reviews, 24, 70-80.\n\n7. Albinowski, M., & Pietrzak, M. B. (2018). International currency flows and capital mobility in emerging and advanced economies. Journal of International Financial Markets, Institutions and Money, 53, 28-45.\n\n8. Lalonde, R. (2017). Uncertainty, global banking, and macroeconomic coordination. Journal of International Money and Finance, 72, 101-123.\n\n9. Belloc, F., Girardone, C., & Mistrulli, P. E. (2017). Bank geographical diversification and productivity: Evidence from Italy. Journal of International Money and Finance, 79, 142-162.\n\n10. Kim, H. K., Qian, X., & Yeung, J. H. Y. (2016). Renminbi internationalization and the People's Bank of China's swap line network in East Asia. Journal of Contemporary China, 25(99), 198-216.\n\n11. Gao, K. (2019). The implications of the inclusion of the Chinese RMB in the SDR basket for its internationalization. Journal of Chinese Economic and Foreign Trade Studies, 12(1), 26-41.\n\n12. Mohseni Zonoozi, M., & Hedayati, H. (2018). Global financial architecture and the role of international monetary institutions. Journal of Financial Regulation and Compliance, 26(2), 304-314.\n\n13. Sadiq, B., & Jadoon, I. A. (2018). The determinants of corporate capital structure: Evidence from Pakistan. Journal of International Financial Markets, Institutions and Money, 53, 270-283.\n\n14. Ho, S. Y., & Suardi, S. (2017). The relationship between economic growth, energy consumption and CO2 emissions: Empirical evidence from China. Journal of Asian Economics, 48, 101-109.\n\n15. Hashimoto, H., & McKenzie, M. D. (2017). Is the Renminbi becoming a world currency? 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