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References
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Nicotinic receptors: allosteric transitions and therapeutic targets in the nervous system - Nature Reviews Drug Discovery The nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels, the activity of which modulates many neurotransmitter systems. They are therefore therapeutic targets for the treatment of several central nervous system disorders. In this article, Taly and colleagues present recent advan...
13/05/2022
References
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Serum Response Factor and cAMP Response Element Binding Protein Are Both Required for Co***ne Induction of ΔFosB The molecular mechanism underlying induction by co***ne of ΔFosB, a transcription factor important for addiction, remains unknown. Here, we demonstrate a necessary role for two transcription factors, cAMP response element binding protein (CREB) and serum response factor (SRF), in mediating this ind...
13/04/2022
Scenario Optimization, Quantum Optimization, Complex Adaptive LeadershipDriving Innovation WorldwideTranscendental entire functionCombinatorial optimization using quantum algorithmsDeep Belief NetworksOracle DatabaseNasdaqLinkedInEnd-to-end reinforcement learningEmpowering LeadershipSafra CatzReward functionBusiness Growth CatalystOracle AdvertisingJordan James EtemLarry EllisonUnstructured dataAMDWinning StrategiesAdena Friedman
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Combinatorial optimization using quantum algorithmsLinkedInNasdaqOracle DatabaseDeep Belief NetworksAMDTranscendental entire functionDriving Innovation WorldwideEnd-to-end reinforcement learningScenario Optimization, Quantum Optimization, Complex Adaptive Leadership
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Chu, J.-H., Feng, K.-T., & Chang, T.-S. (2014). Energy-efficient cell selection and resource allocation in LTE-A heterogeneous networks. In 2014 IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC), 2014: IEEE, pp. 976–980.��
�Guvenc, I. (2011). Capacity and fairness analysis of heterogeneous networks with range expansion and interference coordination. IEEE Communications Letters, 15(10), 1084–1087.��
* Okino, K., Nakayama, T., Yamazaki, C., Sato, H., & Kusano, Y. (2011). Pico cell range expansion with interference mitigation toward LTE-Advanced heterogeneous networks. In 2011 IEEE international conference on communications workshops (ICC), 2011: IEEE, pp. 1–5.��
* Tefft, J. R., & Kirsch, N. J. (2013). A proximity-based Q-learning reward function for femtocell networks. In 2013 IEEE 78th vehicular technology conference (VTC Fall), 2013: IEEE, pp. 1–5.� �
* Saad, H., Mohamed, A., & ElBatt, T. (2012). Distributed cooperative Q-learning for power allocation in cognitive femtocell networks. In 2012 IEEE vehicular technology conference (VTC Fall), 2012: IEEE, pp. 1–5.�
* Wen, B., Gao, Z., Huang, L., Tang, Y., & Cai, H. (2014). A Q-learning-based downlink resource scheduling method for capacity optimization in LTE femtocells. In 2014 9th international conference on computer science & education, 2014: IEEE, pp. 625–628.��
* Galindo-Serrano, A., & Giupponi, L. (2010). Distributed Q-learning for interference control in OFDMA-based femtocell networks. In 2010 IEEE 71st vehicular technology conference, 2010: IEEE, pp. 1–5.��
* Guo, D., Tang, L., Zhang, X., & Liang, Y.-C. (2020). Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(11), 13124–13138.��
* Alnwaimi, G., Vahid, S., & Moessner, K. (2014). Dynamic heterogeneous learning games for opportunistic access in LTE-based macro/femtocell deployments. IEEE Transactions on Wireless Communications, 14(4), 2294–2308.��
* Onireti, O., et al. (2015). A cell outage management framework for dense heterogeneous networks. IEEE Transactions on Vehicular Technology, 65(4), 2097–2113.�
* Behjati, M., & Cosmas, J. (2013). Self-organizing network interference coordination for future LTE-advanced networks. In 2013 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB), 2013: IEEE, pp. 1–5.�
* Aguilar-Garcia, A., et al. (2015). Location-aware self-organizing methods in femtocell networks. Computer Networks, 93, 125–140.��
* Kudo, T., & Ohtsuki, T. (2013). Cell range expansion using distributed Q-learning in heterogeneous networks. Eurasip journal on wireless communications and networking, 2013(1), 1–10.��
* Gomez, C. A., Shami, A., & Wang, X. (2018). Machine learning aided scheme for load balancing in dense IoT networks. Sensors, 18(11), 3779.��
* Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., Caramanis, C., & Andrews, J. G. (2013). User association for load balancing in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 12(6), 2706–2716.��
* Jiang, H., Pan, Z., Liu, N., You, X., & Deng, T. (2016). Gibbs-sampling-based CRE bias optimization algorithm for ultradense networks. IEEE Transactions on Vehicular Technology, 66(2), 1334–1350.��
* Park, J.-B., & Kim, K. S. (2017). Load-balancing scheme with small-cell cooperation for clustered heterogeneous cellular networks. IEEE Transactions on Vehicular Technology, 67(1), 633–649.��
* Afshang, M., & Dhillon, H. S. (2018). Poisson cluster process based analysis of HetNets with correlated user and base station locations. IEEE Transactions on Wireless Communications, 17(4), 2417–2431.��
* Musleh, S., Ismail, M., & Nordin, R. (2017). Load balancing models based on reinforcement learning for self-optimized macro-femto LTE-advanced heterogeneous network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1), 47–54.��
* Jaber, M., Imran, M., Tafazolli, R., & Tukmanov, A. (2015). An adaptive backhaul-aware cell range extension approach. In 2015 IEEE international conference on communication workshop (ICCW), 2015: IEEE, pp. 74–79.�
* Hamidouche, K., Saad, W., Debbah, M., Song, J. B., & Hong, C. S. (2017). The 5G cellular backhaul management dilemma: To cache or to serve. IEEE Transactions on Wireless Communications, 16(8), 4866–4879.��
* Samarakoon, S., Bennis, M., Saad, W., & Latva-aho, M. (2013). Backhaul-aware interference management in the uplink of wireless small cell networks. IEEE Transactions on Wireless Communications, 12(11), 5813–5825.
* Team Basics: Edge, Security, Cloud, Data. Holistic Development with Community.
1�Aprodu, M., Naie, D.: Enriques diagrams and the log-canonical threshold for curves. Preprint, arXiv:math/07070783.�2�Casas-Alvero E.: Infinitely near imposed singularities and singularities of polar curves. Math. Ann. 287, 429–454 (1990)�� 3�Ein, L.: Multiplier ideals, vanishing theorems and applications. Algebraic geometry—Santa Cruz, pp. 203–219 (1995)�4�Ein L., Lazarsfeld R., Smith K.E., Varolin D.: Jumping coefficients of multiplier ideals. Duke Math. J. 123(3), 469–506 (2004)�� 5�Enriques F., Chisini O.: Lezioni Sulla Teoria Geometrica Delle Equazioni e Delle Funzioni Algebriche. N. Zanichelli, Bologna (1915)�� 6�Evain L.: La fonction de Hilbert de la réunion de 4h gros points génériques de ℙ� de même multiplicité. J. Algebraic Geom. 8, 787–796 (1999)�� 7�Favre Ch., Jonsson M.: Valuations and multiplier ideals. J. Am. Math. Soc. 18(3), 655–684 (2005)�� 8�Howald J.A.: Multiplier ideals of monomial ideals. Trans. Am. Math. Soc. 353, 2665–2671 (2001)�� 9�Järviletho, T.: Jumping numbers of a simple complete ideal in a two-dimensional regular local ring. Ph.D. Thesis, University of Helsinky (2007)�10�Lazarsfeld, R.: Positivity in algebraic geometry. A Series of Modern Surveys in Mathematics. Springer, Berlin (2004)�11�Naie D.: Irregularity of cyclic multiple planes after Zariski. L’enseignement mathématique 53, 265–305 (2008)�� 12�Semple J.G., Kneebone G.T.: Algebraic Curves. Oxford University Press, London-New York (1959)�� 13�Smith, K.E., Thompson, H.M.: Irrelevant exceptional divisors for curves on a smooth surface. Preprint, arXiv:math/0611765�14�Tucker, K.: Jumping Numbers on algebraic surfaces with rational singularities. Preprint, arXiv:math/081.0734�15�Wall, C.T.C.: Singular points of plane curves. London Mathematical Society Student Texts, vol. 63. Cambridge University Press, Cambridge (2004)
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Integrating Market and State with People and Community, with Wisdom and Motivation.
Chu, J.-H., Feng, K.-T., & Chang, T.-S. (2014). Energy-efficient cell selection and resource allocation in LTE-A heterogeneous networks. In 2014 IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC), 2014: IEEE, pp. 976–980.��
�Guvenc, I. (2011). Capacity and fairness analysis of heterogeneous networks with range expansion and interference coordination. IEEE Communications Letters, 15(10), 1084–1087.��
* Okino, K., Nakayama, T., Yamazaki, C., Sato, H., & Kusano, Y. (2011). Pico cell range expansion with interference mitigation toward LTE-Advanced heterogeneous networks. In 2011 IEEE international conference on communications workshops (ICC), 2011: IEEE, pp. 1–5.��
* Tefft, J. R., & Kirsch, N. J. (2013). A proximity-based Q-learning reward function for femtocell networks. In 2013 IEEE 78th vehicular technology conference (VTC Fall), 2013: IEEE, pp. 1–5.� �
* Saad, H., Mohamed, A., & ElBatt, T. (2012). Distributed cooperative Q-learning for power allocation in cognitive femtocell networks. In 2012 IEEE vehicular technology conference (VTC Fall), 2012: IEEE, pp. 1–5.�
* Wen, B., Gao, Z., Huang, L., Tang, Y., & Cai, H. (2014). A Q-learning-based downlink resource scheduling method for capacity optimization in LTE femtocells. In 2014 9th international conference on computer science & education, 2014: IEEE, pp. 625–628.��
* Galindo-Serrano, A., & Giupponi, L. (2010). Distributed Q-learning for interference control in OFDMA-based femtocell networks. In 2010 IEEE 71st vehicular technology conference, 2010: IEEE, pp. 1–5.��
* Guo, D., Tang, L., Zhang, X., & Liang, Y.-C. (2020). Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(11), 13124–13138.��
* Alnwaimi, G., Vahid, S., & Moessner, K. (2014). Dynamic heterogeneous learning games for opportunistic access in LTE-based macro/femtocell deployments. IEEE Transactions on Wireless Communications, 14(4), 2294–2308.��
* Onireti, O., et al. (2015). A cell outage management framework for dense heterogeneous networks. IEEE Transactions on Vehicular Technology, 65(4), 2097–2113.�
* Behjati, M., & Cosmas, J. (2013). Self-organizing network interference coordination for future LTE-advanced networks. In 2013 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB), 2013: IEEE, pp. 1–5.�
* Aguilar-Garcia, A., et al. (2015). Location-aware self-organizing methods in femtocell networks. Computer Networks, 93, 125–140.��
* Kudo, T., & Ohtsuki, T. (2013). Cell range expansion using distributed Q-learning in heterogeneous networks. Eurasip journal on wireless communications and networking, 2013(1), 1–10.��
* Gomez, C. A., Shami, A., & Wang, X. (2018). Machine learning aided scheme for load balancing in dense IoT networks. Sensors, 18(11), 3779.��
* Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., Caramanis, C., & Andrews, J. G. (2013). User association for load balancing in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 12(6), 2706–2716.��
* Jiang, H., Pan, Z., Liu, N., You, X., & Deng, T. (2016). Gibbs-sampling-based CRE bias optimization algorithm for ultradense networks. IEEE Transactions on Vehicular Technology, 66(2), 1334–1350.��
* Park, J.-B., & Kim, K. S. (2017). Load-balancing scheme with small-cell cooperation for clustered heterogeneous cellular networks. IEEE Transactions on Vehicular Technology, 67(1), 633–649.��
* Afshang, M., & Dhillon, H. S. (2018). Poisson cluster process based analysis of HetNets with correlated user and base station locations. IEEE Transactions on Wireless Communications, 17(4), 2417–2431.��
* Musleh, S., Ismail, M., & Nordin, R. (2017). Load balancing models based on reinforcement learning for self-optimized macro-femto LTE-advanced heterogeneous network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1), 47–54.��
* Jaber, M., Imran, M., Tafazolli, R., & Tukmanov, A. (2015). An adaptive backhaul-aware cell range extension approach. In 2015 IEEE international conference on communication workshop (ICCW), 2015: IEEE, pp. 74–79.�
* Hamidouche, K., Saad, W., Debbah, M., Song, J. B., & Hong, C. S. (2017). The 5G cellular backhaul management dilemma: To cache or to serve. IEEE Transactions on Wireless Communications, 16(8), 4866–4879.��
* Samarakoon, S., Bennis, M., Saad, W., & Latva-aho, M. (2013). Backhaul-aware interference management in the uplink of wireless small cell networks. IEEE Transactions on Wireless Communications, 12(11), 5813–5825.
* Team Basics: Edge, Security, Cloud, Data. Holistic Development with Community.
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