Arabidopsis RCD1 coordinates chloroplast and mitochondrial functions through interaction with ANAC transcription factors by Shapiguzov, Alexey, Fernie, Alisdair R., Vainonen, Julia P., Hunter, Kerri, Tossavainen, Helena, Tiwari, Arjun, Järvi, Sari, Hellman, Maarit, Aarabi, Fayezeh, Alseekh, Saleh, Wybouw, Brecht, Van Der Kelen, Katrien, Nikkanen, Lauri, Krasensky-Wrzaczek, Julia, Sipari, Nina, Keinänen, Markku, Tyystjärvi, Esa, Rintamäki, Eevi, De Rybel, Bert, Salojärvi, Jarkko, Van Breusegem, Frank, Brosché, Mikael, Permi, Perttu, Aro, Eva-Mari, Wrzaczek, Michael, Kangasjärvi, Jaakko

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Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes by McAllan, Liam, Baranasic, Damir, Villicaña, Sergio, Brown, Scarlett, Zhang, Weihua, Lehne, Benjamin, Adamo, Marco, Jenkinson, Andrew, Elkalaawy, Mohamed, Mohammadi, Borzoueh, Hashemi, Majid, Fernandes, Nadia, Lambie, Nathalie, Williams, Richard, Christiansen, Colette, Yang, Youwen, Zudina, Liudmila, Lagou, Vasiliki, Tan, Sili, Castillo-Fernandez, Juan, King, James W. D., Soong, Richie, Elliott, Paul, Scott, James, Prokopenko, Inga, Cebola, Inês, Loh, Marie, Lenhard, Boris, Batterham, Rachel L., Bell, Jordana T., Chambers, John Campbell, Kooner, Jaspal S., Scott, William R.

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Filamentation of a relativistic proton bunch in plasma by Verra, L, Amoedo, C, Torrado, N, Clairembaud, A, Mezger, J, Pannell, F, Pucek, J, van Gils, N, Bergamaschi, M, Zevi Della Porta, G, Lopes, N, Sublet, A, Turner, M, Gschwendtner, E, Muggli, P, Agnello, R, Ahdida, CC, Andrebe, Y, Apsimon, O, Apsimon, R, Arnesano, JM, Bencini, V, Blanchard, P, Burrows, PN, Buttenschön, B, Caldwell, A, Chung, M, Cooke, DA, Davut, C, Demeter, G, Dexter, AC, Doebert, S, Farmer, J, Fasoli, A, Fonseca, R, Furno, I, Granados, E, Granetzny, M, Graubner, T, Grulke, O, Guran, E, Henderson, J, Kedves, MÁ, Kraus, F, Krupa, M, Lefevre, T, Liang, L, Liu, S, Lotov, K, Pakuza, C, Ramjiawan, RL, Spear, B

Global, regional, and national mortality among young people aged 10–24 years, 1950–2019: a systematic analysis for the Global Burden of Disease Study 2019 by Ward, Joseph L., Azzopardi, Peter S., Francis, Kate L., Santelli, John S., Skirbekk, Vegard, Sawyer, Susan M., Kassebaum, Nicholas J., Mokdad, Ali H., Hay, Simon I., Abd-Allah, Foad, Abdoli, Amir, Mohammad Abdollahi, Abedi, Aidin, Abolhassani, Hassan, Abreu, Lucas Guimaraes, Abrigo, Michael R. M., Abu-Gharbieh, Eman, Abushouk, Abdelrahman I., Adebayo, Oladimeji M., Adekanmbi, Victor, Adham, Davoud, Advani, Shailesh M., Afshari, Khashayar, Agrawal, Anurag, Ahmad, Tauseef, Ahmadi, Keivan, Ahmed, Anwar E., Aji, Budi, Akombi-Inyang, Blessing, Alahdab, Fares, Al-Aly, Ziyad, Alam, Khurshid, Alanezi, Fahad Mashhour, Alanzi, Turki M., Alcalde-Rabanal, Jacqueline Elizabeth, Alemu, Biresaw Wassihun, Al-Hajj, Samar, Alhassan, Robert Kaba, Ali, Saqib, Alicandro, Gianfranco, Alijanzadeh, Mehran, Aljunid, Syed Mohamed, Almasi-Hashiani, Amir, Almasri, Nihad A., Al-Mekhlafi, Hesham M., Alonso, Jordi, Al-Raddadi, Rajaa M., Altirkawi, Khalid A., Alvis-Guzman, Nelson, Amare, Azmeraw T., Amini, Saeed, Aminorroaya, Arya, Amit, Arianna Maever L., Amugsi, Dickson A., Ancuceanu, Robert, Anderlini, Deanna, Andrei, Catalina Liliana, Androudi, Sofia, Ansari, Fereshteh, Ansari, Iman, Antonio, Carl Abelardo T., Anvari, Davood, Anwer, Razique, Appiah, Seth Christopher Yaw, Arabloo, Jalal, Arab-Zozani, Morteza, Ärnlöv, Johan, Asaad, Malke, Asadi-Aliabadi, Mehran, Asadi-Pooya, Ali A., Atout, Maha Moh'd Wahbi, Ausloos, Marcel, Avenyo, Elvis Korku, Avila-Burgos, Leticia, Quintanilla, Beatriz Paulina Ayala, Ayano, Getinet, Aynalem, Yared Asmare, Azari, Samad, Azene, Zelalem Nigussie, Bakhshaei, Mohammad Hossein, Bakkannavar, Shankar M., Banach, Maciej, Banik, Palash Chandra, Barboza, Miguel A., Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Basu, Sanjay, Baune, Bernhard T., Bayati, Mohsen, Bedi, Neeraj, Beghi, Ettore, Bekuma, Tariku Tesfaye, Bell, Arielle Wilder, Bell, Michelle L., Benjet, Corina, Bensenor, Isabela M., Berhe, Abadi Kidanemariam, Berhe, Kidanemaryam, Berman, Adam E., Bhagavathula, Akshaya Srikanth, Car, Lorainne Tudor

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Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter by Abadjiev, D., Adams, T., Adzic, P., Ahmad, M., Amendola, C., Andrews, M. B., Arcidiacono, R., Argiro, S., Askew, A., Auffray, E., Azzolini, V., Bailleux, D., Band, R., Barney, D., Barria, P., Bartosik, N., Basile, C., Bastos, D., Bell, K. W., Besancon, M., Bianco, R., Biino, C., Blinov, V., Borca, C., Bornheim, A., Brown, R. M., Campana, M., Castells, S., Cavallari, F., Cetorelli, F., Chatterjee, R. M., Chatterjee, S., Chaudhary, G., Chen, J. A., Chernyavskaya, N., Chung, H., Cipriani, M., Cokic, L., Cooke, C., Cossio, F., Couderc, F., Cristoforetti, D., Cucciati, G., Cunqueiro Mendez, L., Da Silva Di Calafiori, D., Dafinei, I., Cockerill, D. J. A., Dejardin, M., Re, D. Del, Ricca, G. Della, Depasse, P., Dervan, J., Marco, E. Di, Diemoz, M., Dimova, T., Dissertori, G., Dittmar, M., Dolgopolov, A., Donegà, M., Dordevic, M., Mamouni, H. El, Errico, F., Espinosa, F., Faure, J. L., Fay, J., Menendez, J. Fernandez, Ferri, F., Finco, L., Fiori, F., Frahm, E., Funk, W., Gadek, T., Gajownik, J., Galli, M., Ganjour, S., Gascon, S., Ghezzi, A., Ghose, P., Gninenko, S., Goadhouse, S., Godinovic, N., Golubev, N., Govoni, P., Gras, P., Hakala, J., de Monchenault, G. Hamel, Harilal, A., Härringer, N., Hashmi, R., Heath, H. F., Hirosky, R., Ho, K. W., Hou, X., Ingram, Q., Jain, Sh., Javaid, T., Jessop, C., Jimènez, R., Joshi, B. M., Jourd‘hui, E., Kaadze, K., Kao, Y.-W., Kardapoltsev, L., Khurana, R., King, J., Kirilovas, A., Konstantinov, D., Kovac, M., Krishna, A., Kuo, C. M., Lambrecht, L., Lavizzari, G., Lecoq, P., Ledovskoy, A., Legger, F., Lelas, D., Li, Y. y., Liang, Z., Lin, W., Longo, E., Loukas, N., Lu, R. -S., Lustermann, W., Lutton, L., Lyon, A. -M., Maeshima, K., Malcles, J., Mandrik, P., Manzoni, R. A., Marchese, L., Marinelli, N., Marini, A. C., Martin, L., Marzocchi, B., Mascellani, A., Massironi, A., Matveev, V., Mazza, G., Meridiani, P., Mijic, M., Mijuskovic, J., Milenovic, P., Milosevic, J., Monteno, M., Monti, F., Moortgat, F., Mousa, J., Mudholkar, T., Nessi-Tedaldi, F., Nicolaou, C., Nigamova, A., Obertino, M. M., Organtini, G., Orimoto, T., Orlandi, F., Ovtin, I., Paganis, E., Papagiannis, D., Pandolfi, F., Paramatti, R., Park, K., Pastrone, N., Paulini, M., Pauss, F., Petkovic, , A., Petraityte, E., Pettinacci, V., Petyt, D., Pigazzini, S., Pinolini, B. S., Prova, P. R., Quaranta, C., Ragazzi, S., Rahatlou, S., Rasteiro Da Silva, J. C., Razis, P. A., Teles, P. Rebello, Reis, T., Riti, F., Rogan, C., Romanteau, T., Rosowsky, A., Rovelli, C., Rovere, M., Rusack, R., Salvi, G., Sancar, O., Sanchez, A., Sandever, C., Santanastasio, F., Saradhy, R., Sarkar, U., Schneider, M., Schroeder, N., Sculac, A., Sculac, T., Shahzad, M. A., Shepherd-Themistocleous, C. H., Simkina, P., Singla, A., Singovsky, A., Skovpen, Y., Smith, V. J., Soffi, L., Stachon, K., Steen, A., Steggemann, J., Succar, M., Tao, J., Tishelman-Charny, A., Tiwari, P. C., Tornago, M., Tramontano, R., Tsai, L. -S., Usai, E., Valsecchi, D., Vagnerini, A., Varela, J., Venditti, R., Verma, P., Vlassov, E., Wachirapusitanand, V., Wamorkar, T., Wang, C., Wang, J., Wadud, M. A., Yu, S. S., Zabi, A., Zghiche, A., Zhang, L., Zhu, R. Y., Zygal, L.

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Routing in mobile ad hoc networks with periodic broadcasts by Li, Geng Zhen.

“…Many ad hoc networks utilize hello message to determine local connectivity. The factors that influence the utility of hello messages based on Ad hoc On-Demand Distance Vector (AODV) were investigated by many researchers. …”
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Daya tahan usahawan Program Agropreneur Muda Terengganu berdasarkan perspektif pegawai pelaksana by Siti Aishah Sudin, Siti Hajar Kamaruddin, Roslina Ismail

“…Kadar pengangguran dalam kalangan belia telah menjadi kebimbangan negara pada masa kini. …”
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Extreme learning machines for feature learning by Liyanaarachchi Lekamalage, Chamara Kasun

“…Hence this thesis extends ELM for feature learning. This thesis introduces an ELM based feature learning framework with linear hidden layer activation function referred to as linear Extreme Learning Machine Auto-Encoder (ELM-AE) and linear Sparse Extreme Learning Machine Auto-Encoder (SELM-AE); ELM-AE and SELM-AE with sigmoid hidden layer activation function referred to as non-linear ELM-AE and non-linear SELM-AE. …”
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L’urgenza del suono come espressione propria: conversazione con Anna Oxa by Ammaturo, Francesca Romana

“…In questa conversazione la cantante italiana Anna Oxa riflette sulla sua carriera, sulla sua relazione con i fan, e sugli elementi chiave della sua pratica artistica, come ad esempio l’importanza di creare la propria musica al di là delle pressioni del mondo della celebrità, i dettami dell’industria musicale, o le aspettative tradizionali di genere che esistono nei confronti delle artiste donne nel mondo della musica. …”
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Indoor occupancy estimation using environmental parameters by Masood, Khalid Mustafa

“…The FS-ELM is in fact a novel architecture of the ELM, in which a feature layer is added to the standard ELM. …”
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Weighted online sequential extreme learning machine for class imbalance learning by Lin, Zhiping, Mirza, Bilal., Toh, Kar-Ann.

“…In this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) algorithm for class imbalance learning (CIL). …”
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Further studies of extreme learning machine and compressed signal detection by Cao, Jiuwen

“…In Chapter 1, we give literature reviews of extreme learning machine (ELM) and compressed sensing (CS). In part I of the thesis (Chapters 2, 3, and 4), we consider the recent ELM for training neural networks and present several improved algorithms. …”
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Extreme learning machine for classification and clustering on multiview data by Chen, Jichao

“…The ELM auto-encoder (ELM-AE) is adopted for feature extraction and feature fusion, and an ELM classifier is ultilized for the final classification. …”
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Inferno XVI: from the circling sodomites to Geryon’s cord by Gilson, S

“…Questa lectura analizza le principali sezioni del canto XVI dell’Inferno con particolare attenzione al dialogo fra Dante personaggio e i tre dannati fiorentini, al trattamento della sodomia nella cultura medievale e ai suoi contesti teologici, e al ruolo della profezia in tale canto. …”

Development of localization algorithms for a WiFi based indoor positioning system with machine learning techniques by Lu, Xiaoxuan

“…We model the indoor positioning problem under a non-parametric stochastic framework, and modify the well-known ML tool, extreme learning machine (ELM), to achieve the above goal. Firstly, under the assumption that noises merely lie in input data, we modify ELM by introducing a dead zone, which is called DZ-ELM, and integrate it into our IPS. …”
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Extreme learning machine based speaker recognition by Hu, Zongjiang.

“…Finally and most importantly, the performance of ELM and SVM will be compared. With the results shown in Chapter 6, we can draw the conclusion that ELM is superior to SVM in terms of tuning simplicity, time efficiency and testing accuracy.…”
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Enhanced extreme learning machines for image classification by Cui, Dongshun

“…Among numerous machine learning methods, we choose the Extreme Learning Machine (ELM) for our image classification applications. …”
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Developing a Xen hypervisor based alternate defense against cipher suite downgrade attacks for virtual TLS servers by Pathangi Janardhanan Jatinshravan

“…In this project, I attempted to defend a TLS virtual server running on a Xen Hypervisor against downgrade attack attempts by intercepting the TLS Client Hello, which is the first message in a TLS communication, and examining the client’s preferred cipher suite, dropping the client hello if the cipher suite is not of the desired standard, thus ensuring that a TLS connection does not take place. …”
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Sequential learning for extreme learning machine by Liang, Nanying

“…A novel sequential learning algorihtm for training Single Hidden Layer Feedforward Neural Network (SLFN), Online Sequential Extreme Learning Machine (OS-ELM) is proposed. OS-ELM is based on the combination of Extreme Learning Machine (ELM) and the recursive least-squares (RLS) algorithm. …”
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Short-term load forecasting by wavelet transform and evolutionary extreme learning machine by Li, Song, Wang, Peng, Goel, Lalit

“…Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. …”
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