Search

Your search keyword '"Zhu, Libin"' showing total 309 results
Start Over Author "Zhu, Libin"
309 results on '"Zhu, Libin"'

1. Emergence in non-neural models: grokking modular arithmetic via average gradient outer product

Author

Mallinar, Neil, Beaglehole, Daniel, Zhu, Libin, Radhakrishnan, Adityanarayanan, Pandit, Parthe, and Belkin, Mikhail

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Neural networks trained to solve modular arithmetic tasks exhibit grokking, a phenomenon where the test accuracy starts improving long after the model achieves 100% training accuracy in the training process. It is often taken as an example of "emergence", where model ability manifests sharply through a phase transition. In this work, we show that the phenomenon of grokking is not specific to neural networks nor to gradient descent-based optimization. Specifically, we show that this phenomenon occurs when learning modular arithmetic with Recursive Feature Machines (RFM), an iterative algorithm that uses the Average Gradient Outer Product (AGOP) to enable task-specific feature learning with general machine learning models. When used in conjunction with kernel machines, iterating RFM results in a fast transition from random, near zero, test accuracy to perfect test accuracy. This transition cannot be predicted from the training loss, which is identically zero, nor from the test loss, which remains constant in initial iterations. Instead, as we show, the transition is completely determined by feature learning: RFM gradually learns block-circulant features to solve modular arithmetic. Paralleling the results for RFM, we show that neural networks that solve modular arithmetic also learn block-circulant features. Furthermore, we present theoretical evidence that RFM uses such block-circulant features to implement the Fourier Multiplication Algorithm, which prior work posited as the generalizing solution neural networks learn on these tasks. Our results demonstrate that emergence can result purely from learning task-relevant features and is not specific to neural architectures nor gradient descent-based optimization methods. Furthermore, our work provides more evidence for AGOP as a key mechanism for feature learning in neural networks.

Published
2024

2. Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning

Author

Zhu, Libin, Liu, Chaoyue, Radhakrishnan, Adityanarayanan, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

In this paper, we first present an explanation regarding the common occurrence of spikes in the training loss when neural networks are trained with stochastic gradient descent (SGD). We provide evidence that the spikes in the training loss of SGD are "catapults", an optimization phenomenon originally observed in GD with large learning rates in [Lewkowycz et al. 2020]. We empirically show that these catapults occur in a low-dimensional subspace spanned by the top eigenvectors of the tangent kernel, for both GD and SGD. Second, we posit an explanation for how catapults lead to better generalization by demonstrating that catapults promote feature learning by increasing alignment with the Average Gradient Outer Product (AGOP) of the true predictor. Furthermore, we demonstrate that a smaller batch size in SGD induces a larger number of catapults, thereby improving AGOP alignment and test performance., Comment: ICML 2024

Published
2023

3. Restricted Strong Convexity of Deep Learning Models with Smooth Activations

Author

Banerjee, Arindam, Cisneros-Velarde, Pedro, Zhu, Libin, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract

We consider the problem of optimization of deep learning models with smooth activation functions. While there exist influential results on the problem from the ``near initialization'' perspective, we shed considerable new light on the problem. In particular, we make two key technical contributions for such models with $L$ layers, $m$ width, and $\sigma_0^2$ initialization variance. First, for suitable $\sigma_0^2$, we establish a $O(\frac{\text{poly}(L)}{\sqrt{m}})$ upper bound on the spectral norm of the Hessian of such models, considerably sharpening prior results. Second, we introduce a new analysis of optimization based on Restricted Strong Convexity (RSC) which holds as long as the squared norm of the average gradient of predictors is $\Omega(\frac{\text{poly}(L)}{\sqrt{m}})$ for the square loss. We also present results for more general losses. The RSC based analysis does not need the ``near initialization" perspective and guarantees geometric convergence for gradient descent (GD). To the best of our knowledge, ours is the first result on establishing geometric convergence of GD based on RSC for deep learning models, thus becoming an alternative sufficient condition for convergence that does not depend on the widely-used Neural Tangent Kernel (NTK). We share preliminary experimental results supporting our theoretical advances.

Published
2022

4. A note on Linear Bottleneck networks and their Transition to Multilinearity

Author

Zhu, Libin, Pandit, Parthe, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Randomly initialized wide neural networks transition to linear functions of weights as the width grows, in a ball of radius $O(1)$ around initialization. A necessary condition for this result is that all layers of the network are wide enough, i.e., all widths tend to infinity. However, the transition to linearity breaks down when this infinite width assumption is violated. In this work we show that linear networks with a bottleneck layer learn bilinear functions of the weights, in a ball of radius $O(1)$ around initialization. In general, for $B-1$ bottleneck layers, the network is a degree $B$ multilinear function of weights. Importantly, the degree only depends on the number of bottlenecks and not the total depth of the network.

Published
2022

5. Quadratic models for understanding catapult dynamics of neural networks

Author

Zhu, Libin, Liu, Chaoyue, Radhakrishnan, Adityanarayanan, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

While neural networks can be approximated by linear models as their width increases, certain properties of wide neural networks cannot be captured by linear models. In this work we show that recently proposed Neural Quadratic Models can exhibit the "catapult phase" [Lewkowycz et al. 2020] that arises when training such models with large learning rates. We then empirically show that the behaviour of neural quadratic models parallels that of neural networks in generalization, especially in the catapult phase regime. Our analysis further demonstrates that quadratic models can be an effective tool for analysis of neural networks., Comment: accepted in ICLR 2024; changed the title

Published
2022

6. Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture

Author

Zhu, Libin, Liu, Chaoyue, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

In this paper we show that feedforward neural networks corresponding to arbitrary directed acyclic graphs undergo transition to linearity as their "width" approaches infinity. The width of these general networks is characterized by the minimum in-degree of their neurons, except for the input and first layers. Our results identify the mathematical structure underlying transition to linearity and generalize a number of recent works aimed at characterizing transition to linearity or constancy of the Neural Tangent Kernel for standard architectures., Comment: NeurIPS 2022

Published
2022

7. Variations in Bacterial Communities and Antibiotic Resistance Genes Across Diverse Recycled and Surface Water Irrigation Sources in the Mid-Atlantic and Southwest United States: A CONSERVE Two-Year Field Study

Author

Malayil, Leena, Ramachandran, Padmini, Chattopadhyay, Suhana, Allard, Sarah M, Bui, Anthony, Butron, Jicell, Callahan, Mary Theresa, Craddock, Hillary A, Murray, Rianna, East, Cheryl, Sharma, Manan, Kniel, Kalmia, Micallef, Shirley, Hashem, Fawzy, Gerba, Charles P, Ravishankar, Sadhana, Parveen, Salina, May, Eric, Handy, Eric, Kulkarni, Prachi, Anderson-Coughlin, Brienna, Craighead, Shani, Gartley, Samantha, Vanore, Adam, Duncan, Rico, Foust, Derek, Haymaker, Joseph, Betancourt, Walter, Zhu, Libin, Mongodin, Emmanuel F, Sapkota, Amir, Pop, Mihai, and Sapkota, Amy R

Subjects
Microbiology, Biological Sciences, Biomedical and Clinical Sciences, Infectious Diseases, Genetics, Emerging Infectious Diseases, Infection, Zero Hunger, United States, RNA, Ribosomal, 16S, Anti-Bacterial Agents, Longitudinal Studies, Bacteria, Drug Resistance, Microbial, Water, Disinfectants, Agricultural Irrigation, Wastewater, Genes, Bacterial, recycled water, reclaimed water, wastewater, irrigation water quality, pathogens, water microbiome, antibiotic resistance genes, antibiotic-resistant bacteria, metagenomics, food safety, Environmental Sciences
Abstract

Reduced availability of agricultural water has spurred increased interest in using recycled irrigation water for U.S. food crop production. However, there are significant knowledge gaps concerning the microbiological quality of these water sources. To address these gaps, we used 16S rRNA gene and metagenomic sequencing to characterize taxonomic and functional variations (e.g., antimicrobial resistance) in bacterial communities across diverse recycled and surface water irrigation sources. We collected 1 L water samples (n = 410) between 2016 and 2018 from the Mid-Atlantic (12 sites) and Southwest (10 sites) U.S. Samples were filtered, and DNA was extracted. The V3-V4 regions of the 16S rRNA gene were then PCR amplified and sequenced. Metagenomic sequencing was also performed to characterize antibiotic, metal, and biocide resistance genes. Bacterial alpha and beta diversities were significantly different (p < 0.001) across water types and seasons. Pathogenic bacteria, such as Salmonella enterica, Staphylococcus aureus, and Aeromonas hydrophilia were observed across sample types. The most common antibiotic resistance genes identified coded against macrolides/lincosamides/streptogramins, aminoglycosides, rifampin and elfamycins, and their read counts fluctuated across seasons. We also observed multi-metal and multi-biocide resistance across all water types. To our knowledge, this is the most comprehensive longitudinal study to date of U.S. recycled water and surface water used for irrigation. Our findings improve understanding of the potential differences in the risk of exposure to bacterial pathogens and antibiotic resistance genes originating from diverse irrigation water sources across seasons and U.S. regions.

Published
2022

8. Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models

Author

Liu, Chaoyue, Zhu, Libin, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning
Abstract

Wide neural networks with linear output layer have been shown to be near-linear, and to have near-constant neural tangent kernel (NTK), in a region containing the optimization path of gradient descent. These findings seem counter-intuitive since in general neural networks are highly complex models. Why does a linear structure emerge when the networks become wide? In this work, we provide a new perspective on this "transition to linearity" by considering a neural network as an assembly model recursively built from a set of sub-models corresponding to individual neurons. In this view, we show that the linearity of wide neural networks is, in fact, an emerging property of assembling a large number of diverse "weak" sub-models, none of which dominate the assembly., Comment: Published at ICLR 2022 (spotlight paper)

Published
2022

13. On the linearity of large non-linear models: when and why the tangent kernel is constant

Author

Liu, Chaoyue, Zhu, Libin, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The goal of this work is to shed light on the remarkable phenomenon of transition to linearity of certain neural networks as their width approaches infinity. We show that the transition to linearity of the model and, equivalently, constancy of the (neural) tangent kernel (NTK) result from the scaling properties of the norm of the Hessian matrix of the network as a function of the network width. We present a general framework for understanding the constancy of the tangent kernel via Hessian scaling applicable to the standard classes of neural networks. Our analysis provides a new perspective on the phenomenon of constant tangent kernel, which is different from the widely accepted "lazy training". Furthermore, we show that the transition to linearity is not a general property of wide neural networks and does not hold when the last layer of the network is non-linear. It is also not necessary for successful optimization by gradient descent., Comment: accepted as Spotlight in NeurIPS 2020; made correction to proof

Published
2020

14. Loss landscapes and optimization in over-parameterized non-linear systems and neural networks

Author

Liu, Chaoyue, Zhu, Libin, and Belkin, Mikhail

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Statistics - Machine Learning
Abstract

The success of deep learning is due, to a large extent, to the remarkable effectiveness of gradient-based optimization methods applied to large neural networks. The purpose of this work is to propose a modern view and a general mathematical framework for loss landscapes and efficient optimization in over-parameterized machine learning models and systems of non-linear equations, a setting that includes over-parameterized deep neural networks. Our starting observation is that optimization problems corresponding to such systems are generally not convex, even locally. We argue that instead they satisfy PL$^*$, a variant of the Polyak-Lojasiewicz condition on most (but not all) of the parameter space, which guarantees both the existence of solutions and efficient optimization by (stochastic) gradient descent (SGD/GD). The PL$^*$ condition of these systems is closely related to the condition number of the tangent kernel associated to a non-linear system showing how a PL$^*$-based non-linear theory parallels classical analyses of over-parameterized linear equations. We show that wide neural networks satisfy the PL$^*$ condition, which explains the (S)GD convergence to a global minimum. Finally we propose a relaxation of the PL$^*$ condition applicable to "almost" over-parameterized systems., Comment: The discussion on transition to linearity in Version 1 has been moved to arXiv:2010.01092 (appeared in NeurIPS 2020)

Published
2020

17. Toward Understanding the Dynamics of Over-parameterized Neural Networks

Author

Zhu, Libin

Subjects
Computer science, catapult dynamics, feature learning, neural networks, overparameterization, quadratic models, transition to linearity
Abstract

The practical applications of neural networks are vast and varied, yet a comprehensive understanding of their underlying principles remains incomplete. This dissertation advances the theoretical understanding of neural networks, with a particular focus on over-parameterized models. It investigates their optimization and generalization dynamics and sheds light on various deep-learning phenomena observed in practice. This research deepens the understanding of the complex behaviors of these models and establishes theoretical insights that closely align with their empirical behaviours across diverse computational tasks.In the first part of the thesis, we analyze the fundamental properties of over-parameterized neural networks and we demonstrate that these properties can lead to the success of their optimization. We show that feedforward neural networks corresponding to arbitrary directed acyclic graphs undergo transition to linearity. The transition to linearity is characterized by the networks converging to their first-order Taylor expansion of parameters as their ``width'' approaches infinity. The width of these general networks is characterized by the minimum indegree of their neurons, except for the input and first layers. We further demonstrate that the property of transition to linearity plays an important role in the success of the optimization of over-parameterized neural networks.In this second part of the thesis, we investigate the modern training regime of over-parameterized neural networks, particularly focusing on the large learning rate regime. While neural networks can be approximated by linear models as their width increases, certain properties of wide neural networks cannot be captured by linear models. We show that recently proposed Neural Quadratic Models can exhibit the ``catapult phase''~\cite{lewkowycz2020large} that arises when training such models with large learning rates. We then empirically show that the behaviour of neural quadratic models parallels that of neural networks in generalization, especially in the catapult phase regime. Our analysis further demonstrates that quadratic models can be an effective tool for analysis of neural networks.Moreover, we extend the analysis of catapult dynamics to stochastic gradient descent (SGD). We first present an explanation regarding the common occurrence of spikes in the training loss when neural networks are trained with SGD. We provide evidence that the spikes in the training loss of SGD are caused by catapults. Second, we posit an explanation for how catapults lead to better generalization by demonstrating that catapults increase feature learning by increasing alignment with the Average Gradient Outer Product (AGOP) of the true predictor. Furthermore, we demonstrate that a smaller batch size in SGD induces a larger number of catapults, thereby improving AGOP alignment and test performance. Overall, by integrating theoretical insights with empirical validations, this dissertation provides a new understanding of the complex dynamics governing neural network training and generalization.

Published
2024

30. Research Status and Hotspots Analysis of Cranberry Food Based on Bibliometric.

Author

ZHAO Chenyu, ZHU Dan, ZHU Libin, YANG Tianyou, NIU Guangcai, WEI Wenyi, and ZHANG Yufan

Subjects
CRANBERRY juice, URINARY tract infections, BIBLIOMETRICS, AGRICULTURAL chemistry, FOOD chemistry
Abstract

Based on the bibliometric method, relevant research literature in the field of cranberry food included in the CNKI database from 2013 to 2023 was searched and further analyzed. A total of 444 Chinese and English literatures were obtained through screening, and CiteSpace software was used to analyze the current research status and hotspots in this field from the perspectives of annual publication volume, issuing journals, countries, authors and their institutions, as well as cooccurrences, clustering, and emergent words of keywords. The results showed that the overall trend of the number of publications from 2013 to 2023 was stable, with an average of about 40 publications per year. The journal with the highest number of publications was Food Chemistry (25), which accounted for 6% of the total literature, followed by Food & Function (22) and Journal of Agricultural and Food Chemistry (20). The top 3 countries in terms of the number of articles published were the United States, Poland, and Canada. The author with the most postings was Christina Khoo (17), a researcher from Ocean Spray, USA. The keyword analysis in the CNKI database showed that the keywords with the highest frequency were cranberry, proanthocyanidins, cranberry juice, bioactive components, urinary tract infection, antioxidant properties, intestinal microbiota and processing, health functions, and so on. The hotspots of the literature research in the field of cranberry food were mostly focused on the study of cranberry composition and function, and the exploration of the application of new processed products such as cranberry juice in the food industry. By comprehensively analyzing the literature in the CNKI database, this article can point out the direction for researchers in China cranberry food industry to engage in related research, and provide data references and help in predicting the future development trend of the industry. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

35. Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Author

Wright, Naomi Jane, Leather, Andrew J.M., Ade-Ajayi, Niyi, Sevdalis, Nick, Davies, Justine, Poenaru, Dan, Ameh, Emmanuel, Ademuyiwa, Adesoji, Lakhoo, Kokila, Smith, Emily Rose, Douiri, Abdel, Elstad, Maria, Sim, Marcus, Riboni, Cristiana, Martinez-Leo, Bruno, Akhbari, Melika, Tabiri, Stephen, Mitul, Ashrarur, Aziz, Dayang Anita Abdul, Fachin, Camila, Niyukuri, Alliance, Arshad, Muhammad, Ibrahim, Fowzia, Moitt, Natalie, Doheim, Mohamed Fahmy, Thompson, Hannah, Ubhi, Harmony, Williams, Isabelle, Hashim, Sophia, Philipo, Godfrey Sama, Herrera, Laura, Yunus, Aayenah, Vervoort, Dominique, Parker, Samuel, Benaskeur, Yousra-Imane, Alser, Osaid H., Adofo-Ansong, Nana, Alhamid, Ahmad, Salem, Hosni khairy, Saleh, Mahmoud, Elrais, Safa Abdal, Abukhalaf, Sadi, Shinondo, Patricia, Nour, Ibrahim, Aydin, Emrah, Vaitkiene, Agota, Naranjo, Kelly, Dube, Andile Maqhawe, Ngwenya, Sodumisa, Yacoub, Mina A., Kwasau, Henang, Hyman, Gabriella, Elghazaly, Shrouk Mahmoud, Al-Slaibi, Ibrahim, Hisham, Intisar, Franco, Helena, Arbab, Hana, Samad, Lubna, Soomro, Aqil, Chaudhry, Muhammad Amjad, Karim, Safina, Khattak, Muhammad Adnan Khan, Nah, Shireen Anne, Dimatatac, Doris Mae, Choo, Candy SC, Maistry, Niveshni, Mitul, Ashrarur Rahman, Hasan, Samiul, Karim, Sabbir, Yousuf, Hina, Qureshi, Taimur, Nour, Ibrahim Rabi, Al-Taher, Raed Nael, Sarhan, Osama Abdul Kareem, Garcia-Aparicio, Luis, Prat, Jordi, Blazquez-Gomez, Eva, Tarrado, Xavier, Iriondo, Martí, Bragagnini, Paolo, Rite, Segundo, Hagander, Lars, Svensson, Emma, Owusu, Sheila, Abdul-Mumin, Alhassan, Bagbio, Dominic, Ismavel, Vijay Anand, Miriam, Ann, T, Shajin, Anaya Dominguez, Marlene, Ivanov, Monica, Serban, Andreea Madalina, Derbew, Miliard, Elfiky, Mahmoud, Olivos Perez, Maricarmen, Abrunhosa Matias, Marcia, Arnaud, Alexis P, Negida, Ahmed, King, Sebastian, Fazli, Mohamad Rafi, Hamidi, Nadia, Touabti, Souhem, Chipalavela, Rossana Francisco, Lobos, Pablo, Jones, Brendan, Ljuhar, Damir, Singer, Georg, Cordonnier, Annelien, Jáuregui, Lorena, Zvizdic, Zlatan, Wong, Janice, St-Louis, Etienne, Shu, Qiang, Lui, Yang, Correa, Catalina, Pos, Lucie, Alcántara, Elvyn, Féliz, Erick, Zea-Salazar, Luis Enrique, Ali, Liza, Peycelon, Matthieu, Anatole, Nzanzu Kipata, Jallow, Cherno S., Lindert, Judith, Ghosh, Dhruv, Adhiwidjaja, Cathline Freya, Tabari, Ahmad Khaleghnejad, Lotfollahzadeh, Saran, Mussein, Haidar Mohammad, Vatta, Fabrizio, Pasqua, Noemi, Kihiko, David, Gohil, Hetal, Nour, Ibrahim R., Elhadi, Muhammed, Almada, Suad Ahmed, Verkauskas, Gilvydas, Risteski, Toni, Peñarrieta Daher, Alejandro, Outani, Oumaima, Hamill, James, Lawal, Taiwo, Mulu, Jack, Yapo, Benjamin, Saldaña, Lily, Espineda, Beda, Toczewski, Krystian, Tuyishime, Eugene, Ndayishimiye, Isaac, Raboe, Enaam, Hammond, Philip, Walker, Gregor, Djordjevic, Ivona, Chitnis, Milind, Son, Joonhyuk, Lee, Sanghoon, Hussien, Muaad, Malik, Sawazen, Ismail, Enas Musa, Boonthai, Ampaipan, Dahman, Nesrine Ben Hadj, Hall, Nigel, Castedo Camacho, Fabiola Ruth, Sobrero, Helena, Butler, Marilyn, Makhmud, Aliev, Novotny, Nathan, Hammouri, Ahmad G., Al-Rayyes, Maisara, Bvulani, Bruce, Muraveji, Qais, Murzaie, Muhammad Yousuf, Sherzad, Ajmal, Haidari, Sayed Aman, Monawar, Abdul Baqi, Samadi, Dr. Ahmad Zia, Thiessen, Jesh, Venant, Ntakarutimana, Hospital, Sonia Inamuco, Jérémie, Niyonkuru, Mbonicura, Jean Claude, Vianney, Butoyi Jean Marie, Tadesse, Amezene, Negash, Samuel, Roberts, Charles A., Jabang, John N., Bah, Abdoulie, Camamra, Kajali, Correa, Armandou, Sowe, Babucarr, Gai, A., Jaiteh, Musa, Raymond, Kwizera Jean, Mvukiyehe, Jean Paul, Itangishaka, Innocent, Kayibanda, Emmanuel, Manirambona, Emery, Lule, Joseph, Costas-Chavarri, Ainhoa, Shyaka Gashugi, Ian, Ndata, Albert, Gasana, Georges, Nezerwa, Yves Castar, Simeon, Turatsinze, Muragijimana, Jean De Dieu, Rashid, Sakina, Msuya, David, Elisante, Joseph, Solanki, Meghna, Manjira, Emmanuel, Lodhia, Jay, Jusabani, Mubashir, Tarmohamed, Murad, Koipapi, Sengua, Souhem, Touabti, Sara, Nabti, Sihem, Brahimi, Dania, Bouguermouh, Toufik, Iaiche Achour, Mounira, Baghdadi Nour el islam, Habiba, Alouani, Aragão, Liliana, Gonçalves, Victor, Lino Urquizo, Marcelo Mauricio, Varela, Maria Florencia, Mercado, Pedro, Horacio, Bonavia, Damiani, Andrea, Mac, Carlos, Putruele, Daniel, Liljesthrom, Karen, Bernaus, Marianela, Jauri, Cesar, Cripovich, Alejandrina, Bianchin, Ezequiel, Puig, Maria Gabriela, Andreussi, Lorna, Iracelay, Susana, Marcos, Dolores, Herrera, Carina, Palacios, Nelly, Avile, Romina, Serezo, Belen, Montoya, Debora, Cepeda, Rodrigo, Vaquila, Justo, Veronica, Sofficci, Pardo, Liliana, Valeria, Pelussi, Julio, Lapalma, Martin, Aranda Diego, Lucio, Palazzi, Gabriel, Comba, Marianella, Depetrini, Calderón Arancibia, José Alfredo, Huespe, Enrique, Losa, Gabriela Natalia, Arancibia Gutiérrez, Elsa, Scherl, Humberto, Gonzalez, Daniel Emilio, Baistrocchi, Valentina, Silva, Yanina, Galdeano, Marcelo, Medard, Pablo, Sueiras, Ines, Romero Manteola, Enrique, Defago, Victor Hugo, Mieres, Carlos, Alberto, Carlos, Cornelli, Fabio, Molina, Marcelo, Ravetta, Pablo, Patiño Gonzalez, Celeste Carolina, Dallegre, Maria Belen, Szklarz, Maria Tatiana, Leyba, Marcos Federico, Rivarola, Nahuel Ignacio, Charras, Maria Delia, Morales, Adriana, Caseb, Paloma, Toselli, Luzia, Millán, Carolina, Junes, Maria del Carmen, Di Siervi, Oscar, Gilardi, Jose, Simon, Soledad, Contreras, Carla Sofia, Rojas, Nair, Arnoletto, Lucia Beatriz, Blain, Otilia Eva, Bravo, Mauro Nicolas, Sanchez, Nancy, Herrera Pesara, Luciana Martina, Moreno, Maria Eugenia, Sferco, Carlos Ariel, Huq, Umama, Ferdousi, Tamanna, Al-Mamun, Abdullah, Sultana, Sadia, Mahmud, Refoyez, Mahmud, Khalid, Sayeed, Fatema, Svirsky, Alexander, Sempertegui, Denisse, Negrete, Amalia, Teran, Araceli, Sadagurschi, Mariana, Popovic, Nusret, Karavdic, Kenan, Milisic, Emir, Jonuzi, Asmir, Mesic, Amira, Terzic, Sabina, Dendusic, Nejra, Biber, Elna, Sehic, Anesa, Zvizdic, Nada, Letic, Emina, Saracevic, Adna, Hamidovic, Ajla, Selak, Nejra, Horozic, Dzan, Hukic, Lamija, Muhic, Amila, Vanis, Nedim, Sokolovic, Emir, Sabic, Adnan, Becker, Karin, Novochadlo Klüppel, Elis, dos Santos Dias, André Iván Bradley, Agulham, Miguel Angelo, Bischoff, Cristiano, Sabbatini, Stella, Fernandes de Souza, Rachel, Souza Machado, Ana Beatriz, Werneck Raposo, Juliana, da Silva Augusto, Maria Lucia, Martins, Bianca M.R., de Souza Santos Ferreira, Mariana, Fernandes de Oliveira, Darli, Silva dos Santos, Carla, Ribeiro de Fernández y Alcázar, Fernanda, Alves Dutra da Silva, Érika, Furtado, Mariana, Tamada, Horácio, Silva Ferreira dos Santos, Marília, Lopes de Almeida, Thayná, Oliveira de Andrade, Susy, Gurgel do Amaral, Antonio Cipriano, Jr, Sartori Giovanoni, Lais, de Deus Passos Leles, Kamila, Corrêa Costa, Eduardo, Feldens, Leticia, Ferraz Schopf, Luciano, Soares de Fraga, José Carlos, Colombo de Holanda, Felipe, Brolin Santis Isolan, Paola Maria, Loyola Ferreira, Julia, Bruxel, Carla Luisa, Lopes Teixeira Ferdinando, Danielle, Zottis Barcelos, Fabricio, Baseggio, Natalia, Knorr Brenner, Nicole, Trindade Deyl, Rafael, Dure, Carolina, Nunes Kist, Iuri, Bueno Mazzuca, Rafael, Bueno Motter, Sarah, Ramos, Yna, Suzana Trein, Cristine, Rezende Rosa, Bianca, de Assis Silva, Murilo, Menin, Flavio Augusto, Semensato Carloni, Isabela Cristina, Norberto da Silva, Juliana Antinarelli, Gomes, Adriano Luis, Girão Tauffer, Mariana, Bassan Gonçalves, Paulo César, Nogueira Marques, Geraldo Magela, Moriya, Eliane, Labonia, Carla, Carrasco, Ana Lucia, Furtado Meyer, Karine, Farion-Aguiar, Luiz, Amado, Fernando, Antunes, Amanda, Silva, Elisângela, Telles, Leila, Almeida, Giovana, Belmino Gadelha, Aluísio Augusto, de Azevedo Belesa, Flavia, Gonçalves da Cunha, Jr, Acimar, Souza Barros, Beatriz, Zanellato, Josiane Bernartt, Guimarães, Patricia, Silva, Karina Ilheu da, Ribas, Bianca, Reuter, Cristina, Casado, Francis Tanise, Correa Leite, Mila Torii, Testoni, Daniela, Guinsburg, Ruth, de Campos Vieira Abib, Simone, Khodor Cury, Edson, Dornellas do Nascimento, Suely, Almeida Aguiar, Arthur, Melo Gallindo, Rodrigo, Gonçalves Borges, Carolina, Liu, Yang, Duote, Cai, Wang, Jinhu, Gao, Zhigang, Liang, Liang, Luo, Wenjuan, Zhao, Xiaoxia, Chen, Rui, Wang, Peng, Han, Yijiang, Huang, Ting, Donglai, Hu, Xiaodong, Guo, Junjie, Chen, Zhu, Libin, Wu, Guowei, Bao, Xiaozhou, Li, Haijing, Lv, Junying, Li, Zhongrong, Yong, Feng, Gao, Zhou Chong, Bai, Qiang, Tang, Weibing, Xie, Hua, Motee, Jethishka, Zhu, Jianming, Wen, Gang, Ruan, Weiwei, Li, Shungen, Chen, Lulu, Huang, Shungen, Lv, Zhibao, Lu, Jinjing, Huang, Liuming, Yu, Mengnan, Dajia, Wang, Bai, Yu Zuo, Rincon, Luis Carlos, Mancera, Juliana, Alzate Gallego, Edgar, Torres-Canchala, Laura, Silva Beltrán, Nathalia, Osorio Fory, Ghordana, Castaño Avila, Daniela, Forero Ladino, Angelica Maria, Gomez, Juanita, Jaramillo, Martha, Morales, Otto, Sanchez, Beatriz, Tinoco Guzmán, Nestor Julien, Castañeda Espinosa, Sergio, Prieto Vargas, Osbaldo, Pardo, Lina Maria, Toral, Eliana, Cáceres Aucatoma, Freud, Hinostroza, Daniel, Valencia, Santiago, Salinas, Vicente, Landivar Cino, Enrique, Ponce Fajardo, Gabriela Yulissa, Astudillo, Miguel, Garcia, Virginia, Muñoz, Guillermo, Verduga, Leonardo, Verduga, Ivan, Murillo, Ericka, Bucaram, Elena, Guayelema, Marisol, Marmol, Monica, Sanchez, Janina, Vergara, Carolina, Mena, Adriana, Velaña, Junior, Salazar, Karla, Lara, Sandra, Chiriboga, Elena, Silva, Julian, Gad, Dalia, Samy, Doaa, Elsadek, Menan Ahmed, Mohammed, Hanan Mahmoud, Abouheba, Mohamed, Ali, Karim Osamy, Rashwan, Hayssam, Fawzy, Omar Moustafa, Kamel, Tarek mohamed, Nemer, Rawan, Hassan, Mohamed Abada, Falah, Eyad Hassan, Abdelhady, Dina Sobhy, Zain, Mostafa, Ibrahim, Eman Abouzeid Abouzeid, Elsiraffy, Omar Ossama, Aboelela, Ahmed, Farag, Eman mohamed, Oshiba, Ahmed Mohamed, Emam, Omar Sameh, Attia, Alaa Mobarak, Laymouna, Moustafa A., Ghorab, Islam Abdelmonem, Mohammed, Mansour Mkayed, Soliman, Nourhan Akram, Ghaly, Khaled Abd elrahman, Sadek, Kareem, Elsherbiny, Mohamed, Saleh, Amr, Sheir, Hesham, Wafa, Tamer, Elmenam, Mohamed Abd, Abdelmaksoud, Sherif, Reda, Ahmed, Mansour, Islam, Elzohiri, Mohamed, Waseem, Basma, Elewaily, Mohamed, El-Ghazaly, Mohammed, Elhattab, Ahmad, Shalaby, Amr, Elsaied, Adham, Adawy, Ahmad, Sadek, Mirna, Ahmed, Mahmoud Abdelfattah, Herdan, Mohamed Omar, Elassall, Gena Mohamed Hamed, Mohammed, Azhar Arabi, Takrouney, Mohammed Hamada, Essa, Tarek Mohamed, Mahmoud, Ahmed Mokhtar, Saad, Alshaimaa M., Fouly, Mariam Albatoul Nageh, Ibrahim, Mahmoud abdelshakour, Nageh, Mohammad, Saad, Mahmoud M., Badr, Helmy, Fouda, Mohamed Fayez, Nofal, Ahmed Hassan, Almohamady, Hisham, Arafa, Mohamed Ahmed, Amad, Mohamed, Mansour, Mohamed Awad, O'Connor, Jennifer, O'Connor, Zachary, Anatole, Nzanzu, Nkunzimana, Elysé, Machemedze, Solomon, Dieudonné, Lemfuka, Appeadu-Mensah, William, Anyomih, Theophilus Teddy Kojo, Alhassan, Priscilla, Abantanga, Francis A., Michael, Vishal, Mary Koshy, Roshine, Raj, Ankit, Kumar, Vijay, PT, Sundeep, Prabhu, P Santosh, Vosoughi, Armin, Al-Mayoof, Ali Farooq, Fadhle, Muhamed Jassim, Joda, Ali Egab, Algabri, Hayder Nadhim Obaid, Abdelhamid, Sultan S., Al-Momani, Hashem M., Amarin, Marzouq, Zaghlol, Louay Y., Alsaadi, Nijmeh Nasser, Qwaider, Yasmeen Z., Qutishat, Hibah, Aliwisat, Ahmad Hasan, Arabiat, Esraa, Bsisu, Isam, Murshidi, Raghad M., Jabaiti, Mohammad S., Bataineh, Ziad A., Abuhayyeh, Husam Aldean, Quran, Thekraiat M. Al, Za'nouneh, Faris J. Abu, Alebbini, Mohanad Mutasem, Qudah, Hamzah Abullah, Hussein, Omar Ghazi, Murad, Amir M.I., Amarin, Justin Z., Suradi, Haya H., Alzraikat, Sayel H., Omari, Rand Y., Matour, Bashar M., Al-Halbouni, Layana, Zurikat, Rajai O., Yanis, Ahmad H., Hussein, Sara Al, Shoubaki, Ali, Ghanem, Waleed H., David, Kuria, Chitiavi, Soita Wycliffe, Mose, Moraa, Mugo, Robert, Ndungu, James, Mwai, Timothy, Shahbal, Swaleh, Malik, Janan, Chauhan, Nirav, Syovata, Francisa, Ochieng, Kevin, Omendo Liyenzero, Polycarp, Hussain, Syeda Ra'ana, Mugambi, Stanley, Ochieng, Roseline, Elkhazmi, Ebtesam Othman Abdulsalam, Khaled, Ala, Albozidi, Aya, Enbaya, Manal Ben, Elgammudi, Mala, Soula, Enas, Khalel, Wegden ibrahim almabrouk, Elhajjaji, Yasmine Ali, Alwaggaa, Nouriyah Ali, Ghayth, Sumayyah, Zreeg, Dafer abdulhakim .S., Tantush, Sara Abobaker, Bibas, Fatma, Layas, Tesneem, Sharif, Randa Alamen M, Aljadidi, Wesal Omar F. Saied, Tarek, Ahmed, Ahmed, Hazem, Essamilghi, Kamila Almabrouk Mohammed, Alfoghi, Mabroka, Abuhlega, Ma'aly A., Arrmali, Saddam, Abduljawad, Fatima Mousa, Alosta, Hasan Mustafa, Abuajaila, Abdulsalam, Abdelmutalib, Fakereldeen, Bashir, Fatma, Almengar, Inas, Annajjar, Mohammad hasan, Deyab, Abdelaziz, Elzowawi, Fathi, Krayem, Yousef, Drah, Weam, Meftah, Asma, Mohammed, Abobaker, Arrmalli, Lina Ali, Aljaboo, Hajir, Elayeb, Abdallah, Altomi, Mohamed, Altaweel, Ahmed, Tumi, Mohamed, Bazozi, Hana Milad, Shaklawoon, Aisha, Alglaib, Mohammed Meftah, Elkaloush, Abdullahn Abdousalam, Trainba, Sara, Swessi, Hisham, Alnaeri, Ali, Shnishah, Aya Essam, Mustufa, Hamassat, Gargum, Sondas Ali, Tarniba, Sara Ali, Shalluf, Hawa Ahmed, Shokri, Hajer Ali, Sarkaz, Taher L., Tababa, Osama, Elhadi, Ahmed, Naunova, Vesna Cvetanovska, Jovcheski, Laze, Kamilovski, Marjan, Gavrilovska-Brzanov, Aleksandra, Latiff, Zarina Abdul, Pauzi, Siti Farhan Moh, Osman, Marjmin, Lim, Felicia, Bakar, Ainal Huda Abu, Zaman, Azrina SK, Ishak, Shareena, Teo, Rufinah, Qi, Dr. Tammy Teoh Han, Othman, Mohd Yusran Bin, Zahari, Dato' Dr Zakaria bin, Hassan, Zulfitri bin Md, Shan, Cheah Hui, Lechmiannandan, Abhirrami, Tamaddun, Hafatin Fairos bt, Adanan, Mohd Fitri Shukri bin Mohamed, Abdullah, Mohd Yusof bin, Junyi, Wang, Nor, Mohd. Tarmizi Mohd, Noor, Wan Ruzaimie, Hassan, Mohd Razin bin, Dalek, Noor Fa'izatul Rahil Ambok, Hashim, Hidayah Hayati binti, Zarwawi, Ahmad Zulhisyam bin, Vellusamy, V Muthualhagi M, Yuen, Quah Soong, Kannessan, Hemasutha a/p, Ramli, Najua binti, Bujarimin, Ahmad Shafiee bin, Anntinea, Jessmine, Dass, Anthony, Khalid, Hazlina Mohd., Hanifah, Nur Atiqah binti Mohd, Jyun, Keily Wong Yue, Razak, Rahilah binti Abd, Naim, Nur Atifah binti Mohd, Hamzah, Siti Nur Aien binti Hamid, Vidal, Cristian R. Zalles, Bracho Blanchet, Eduardo, Dávila Perez, Roberto, Fernandez Portilla, Emilio, Villegas Silva, Raúl, Ibarra, Daniel, Calderon Moore, Antonio, Carrasco-Ortega, Cesar, Noguez Castillo, Monica, Herappe Mellado, Dorihela, Yanowsky Reyes, Guillermo, Gonzalez Cortez, Luis Fernando, Santana Ortiz, Rafael, Orozco Perez, Jamie, Corona C.Rivera, Jorge Román, Cardenas Ruiz Velasco, Juan Jose, Quiles Corona, Moises, Peña Padilla, Christian, Bobadilla Morales, Lucina, Corona Rivera, Alfredo, Rios Flores, Izabel Maryalexandra, Aranda Sánchez, Cristian Irela, Ambriz-González, Gabriela, Martínez Hernández Magro, Nestor, León Frutos, Francisco Javier, Cárdenas Barón, José de Jesús, González Ojeda, Alejandro, Yarza Fernández, Jessica, Porras, Juan Domingo, Aguirre-Lopez, Pastor, Sánchez Paredes, Vicente, Montalvo Marin, Arturo, Diaz Gomez, Jose Manuel, Caamal, Lorenzo Juvencio, Bulnes Mendizabal, David, Sanchez Valladares, Pablo, Garcia Martinez, Humberto, Adesanya, Opeoluwa, Olanrewaju, Moses, Adegboyega, Rilwan, Abdulraheem, Nurudeen, Aremo, Anuoluwapo, Dedeke, Florence, Chukwuemeka, Anyanwu Lofty-John, Mohammad, Mohammad Aminu, Lawalbarau, Abdullahi, Collins, Nwokoro, Ibukunolu, Ogundele, Shonubi, Amo, Ladipo-Ajayi, Oluwaseun, Elebute, Olumide Abiodun, Seyi-Olajide, Justina, Alakaloko, Felix, Ihediwa, George, Olayade, Kayode, Bode, Christopher, Ogundoyin, Olakayode, Olulana, Dare I., Egbuchulem, Ifeanyichukwu Kelvin, Kumolalo, Felix O., Ulasi, Ikechukwu, Ezomike, Uchechukwu Obiora, Ekenze, Sebastian Okwuchukwu, Nwankwo, Elochukwu Perpetua, Nwangwu, Emmanuel Ifeanyi, Chukwu, Isaac, Amah, Christopher Chim, Obianyo, Nene Elsie, Williams, Omolara, Osuoji, Roland Iheanyichukwu, Faboya, Omolara Moronkeji, Ajai, Olalekan Temitope, Abdulsalam, Moruf Adekunle, Agboola, Titiloye Hannah, Temilade, Bolarinwa Bolanle, Osazuwa, Maryrose, Salawu, Morayo Monsurat, Ejinkeonye, Eze Chukwuemeka, Yola, Mariya Mukhtar, Mairami, Amsa B., Otuneye, Adekunle T., Igoche, Matthias, Tanimola, Adebayo Gbenga, Ajao, Emmanuel Akinlabi, Agelebe, Efeturi, Olori, Samson, Mshelbwala, Philip Mari, Osagie, Olabisi, Oyinloye, Adewale, Abubakar, Auwal M, Oyebanji, Lateef, Shehu, Ibrahim, Cletus, Cyril, Bamanga, Ahmed, Suleiman, Faruk, Adamu, Sani, C.Nwosu, David, S.Alkali, Yahya, Jalo, Iliya, Rasaki, Aliu, T.Sambo, Yusuf, A.Mohammed, Kalakwa, M.Ballah, Abubakar, Modekwe, Victor, Ekwunife, Okechukwu Hyginus, Ezidiegwu, Ugochukwu S, Osuigwe, Andrew N, Ugwu, Jideofor O, Ugwunne, Chuka A, Akhter, Nadeem, Gondal, Mudassir Fayaz, Raza, Rafee, Chaudary, Ali Raza, Ali, Hassan, Nisar, Muhammad Umar, Jamal, Muhammad Umer, Pandit, Ghuri Shankar, Mumtaz, Uzma, Amjad, Muhammad Bin, Talat, Nabila, Rehman, Wajeeh ur, Saleem, Muhammad, Mirza, Muhammad Bilal, Hashim, Imran, Haider, Naveed, Hameed, Soban, Saleem, Ayesha, Dogar, Sohail, Sharif, Muhammad, Bashir, Muhammad Kashif, Naumeri, Fatima, Rani, Zarqa, Baniowda, Muath A.M., Ba'baa', Basheer, Hassan, Majd Yousef Mohammed, Darwish, Ammar, Sehwiel, Abrar Shaheen, Shehada, Mohammed, Balousha, Abrar ghassan, Ajrami, Yara, Alzamari, Ainaa Ata Mohammad, Yaghi, Bashar, Al-saleem, Hasan Subhi Hasan Abu, Farha, Mervat Sufian Abu, Abdelhafez, Mohammad Omar Mohammad, Anaya, Firas, Qadomi, Asef belal, Odi, Abd Al-Naser Bany, Assi, Muath Abdelrahem Fuad, Sharabati, Fadwa, Abueideh, Ahmad, Beshtawi, Doha mustafa saleh, Arafat, Hasan, Khatatba, Lara Zahi Adel, Abatli, Safa' Jamal, Al-Tammam, Hiba, Jaber, Dania, Kayed, Yara Imad Omar, Abumunshar, Ali Abdelhay, Misk, Rami Anwar, Alzeer, Asmahan Mohammad Suliman, Sharabati, Mutassem, Ghazzawi, Ihsan, Darras, Osama Majed, M.Qabaja, Mahmoud, Hajajreh, Ma'alem sameer, Samarah, Yasmeen Ahmad, Yaghi, Dua Hasan, Qunaibi, Moradallah Asad Fahmi, Mayaleh, Abdelrazzaq Abu, Joubeh, Sharehan, Ebeido, Annan, Adawi, Samer, Adawi, Ihda, Alqor, Mohammad Omar Ibrahim, Arar, Ahmad Samih, Awad, Hadeel, Abu-Nejmah, Fawzi, Shabana, Osaid Shaher, Alqarajeh, Firas, Alzughayyar, Tareq Z., Madieh, Jomana, Sbaih, Mahmoud Fuad, Alkareem, Raghad mohammad abdu, Lahlooh, Raghad abdullateef, Halabi, Yasmeen Adly, Baker, Wisam, Almusleh, Tasneem Fathi Hasan, Tahyneh, Abdulraheem Adnan Abdulraheem, Atatri, Yazid yousef mahmoud, Jamie, Najlaa Abu, Massry, Nasrallah Ashraf Al, Lubbad, Walaa, A.Nemer, Ayoub, Alser, Mohammed, Salha, Aya Azmi Shehda, Alnahhal, Khaled, Elmzyyen, Aya Mahmoud, Ghabayen, Amir Talat Sheda, Alamrain, Abdulwhhab Ayman Abu, Al-Shwaikh, Samar H., Elshaer, Omar Adly, Shaheen, Nureddin, Fares, Jehad, Dalloul, Hisham, Qawwash, Anas, Jayyab, Mustafa abu, Ashour, Dina Ayman, Shaheen, Ahmad Ashraf, Naim, Samy Rafat Ramadan, Shiha, Eman Abu, Dammagh, Nagham Mohammed Al, Almadhoun, Walaa, Al-Salhi, Ashraf Ayman, Hammato, Abdalkarim Yhya, Salim, Jamal Mohammed, Hasanain, Doaa Khalil, Alwadia, Soha Marwan Salem, Nassar, Ismail, Al-Attar, Hala M., Alshaikhkhalil, Haya Abdulnasser Ali, Jamie, Yasmin Mohammed Khalil Abu, Ashour, Yara shareef, Alijla, Sharif S., Tallaa, Mohamed Anwer El, Abuattaya, Adham Ashraf, Wishah, Bisan D.M., ALDIRAWI, MOHAMMED A.M., Darwish, Ahmed S, Alzerei, Sulaiman T., Wishah, Nidal, Alijla, Sharif, Garcia, Isidora, Diaz Echegaray, Marlene, Cañapataña Sahuanay, Veronica Raquel, Trigoso Mori, Fernando, Alvarado Zelada, Jackelyne, Salinas Barreto, Juan Jose, Rivera Altamirano, Porfirio, Torres Miranda, Cesar, Anicama Elias, Rocio, Rivera Alvarez, Julio, Vasquez Matos, Juan Pedro, Ayque Rosas, Fernando, Ledesma Peraza, Jesmarina, Gutarra Palomino, Andrea, Vega Centen, Stephany, Casquero, Victor, Ortiz Argomedo, María Rosa, Lapouble, Francisco, Llap Unchón, Genaro, Delgado Malaga, Florangel Patricia, Ortega Sotelo, Luis, Gamboa Kcomt, Segundo, Villalba Villalba, Araceli, Mendoza Leon, Nancy Rossana, Cardenas Alva, Loreley Raquel, Loo Neyra, Maria Susana, Alanguia Chipana, Cathy Lee, Torres Picón, Cintya Maria de Jesus, Huaytalla Quiroz, Natalia, Dominguez, Danny, Segura Calle, Carlos, Arauco, Jenny, Ormeño Calderón, Luis, Ghilardi Silva, Ximena, Fernandez Wilson, Miriam Daniela, Gutierrez Maldonado, Joan Elizabeth, Diaz Leon, Cesar, Berrocal Anaya, Waldo, Chavez Galvez, Patricia, Aguilar Gargurevich, Prince Pamela, Diaz Castañeda, Flor de Maria, Guisse, Carmen, Ramos Paredes, Erika, Apaza Leon, Jose Luis, Aguilar Aguilar, Faye, Ramirez De La Cruz, Raul, Flores Carbajal, Lenny, Mendoza Chiroque, Carlos, Sulca Cruzado, Gladys Johana, Tovar Gutierrez, Natalia, Sotelo Sanchez, Jennifer, Paz Soldan, Carolina, Hernández Córdova, Karina, Delgado Quinteros, Edgar Fernando, Brito Quevedo, Luz Mery, Mendoza Oviedo, Juan Jose, Samanez Obeso, Angel, Paredes Espinoza, Patricia, de Guzman, Johann, Yu, Raisa, Cosoreanu, Vlad, Ionescu, Sebastian, Mironescu, Aurel, Vida, Lucian, Papa, Adrian, Verdeata, Roxana, Gavrila, Bogdan, Muntean, Liviu, Lukac, Marija, Stojanovic, Miona, Toplicic, Djordje, Slavkovic, Milan, Slavkovi, Andjelka, Zivanovic, Dragoljub, Kostic, Ana, Raicevic, Maja, Nkuliza, Delphine, Sidler, Daniel, Vos, Corné de, Merwe, Elmarie vd, Tasker, David, Khamag, Omar, Rengura, Cecilia, Siyotula, Thozama, Jooma, Uzair, Delft, Dirk von, Arnold, Marion, Mangray, Hansraj, Harilal, Shamaman, Madziba, Sanele, Wijekoon, Naveen, Gamage, Tharanga, Bright, Benedict Paul, Abdulrahman, Alaa, Mohammed, Ola Ahmed Abdulmjeed, Salah, Mohammed, Ajwa, Ahmad Elian Abu, Morjan, Mohammed, Batal, Mohammad Mohannad, Faks, Vivian, Mouti, Mohamad Bassel, Assi, Ahmadfateh, Al-Mouakeh, Ahmad, Tarabishi, Ahmad Sankari, Aljarad, Ziad, Alhamid, Aos, Khorana, Jiraporn, Poocharoen, Wannisa, Liukitithara, Sirima, Sriniworn, Anan, Nuntasunti, Wasun, Ngerncham, Monawat, Phannua, Ratiyaporn, Thaiwatcharamas, Kanokrat, Tanming, Patchareeporn, Sahnoun, Lassaad, Kchiche, Nahla, Abdelmoumen, Roua, Eroğlu, Egemen, Ozen, Mehmet Ali, Cömert, Hatice Sonay Yalçın, İmamoğlu, Mustafa, Sarıhan, Haluk, Kader, Şebnem, Mutlu, Mehmet, Aslan, Yakup, Beşir, Ahmet, Geze, Şükran, Çekiç, Bahanur, Yalcinkaya, Ali, Sönmez, Kaan, Karabulut, Ramazan, Türkyılmaz, Zafer, Şeref, Kıvanç, Altın, Merve, Aykut, Merve, Akan, M.Eren, Erdem, Melisa, Ergenekon, Ebru, Türkyılmaz, Canan, Keleş, Elif, Canözer, Ali, Yeniay, Aslı Öztürk, Eren, Elif, Cesur, İlknur Banlı, Özçelik, Zerrin, Kurt, Gökmen, Mert, Mustafa Kurthan, Kaya, Hatice, Çelik, Müge, Karakus, Suleyman Cuneyt, Erturk, Nazile, Suzen, Alev, Hakan, Nilay, Akova, Fatih, Pasaoglu, Mehmet, Eshkabilov, Shukurali, Yuldashev, Rustam Z., Abdunomonovich, Dekhkonboev Avazjon, Muslimovich, Aliev Makhmudjan, Patel, Azad, Kapihya, Chisengo, Ensar, Nicholas, Nataraja, Ramesh M, Sivasubramaniam, Mithila, Jones, Matthew, Teague, Warwick, Tanny, Sharman Tan, Thomas, Gordon, Roberts, Kiera, Venkatraman, Soundappan Sannappa, Till, Holger, Pigeolet, Manon, Dassonville, Martine, Shikha, Anas, Win, Win Sabai Phyu, Ahmad, Zahidah Adlynee Haji, Meloche-Dumas, Léamarie, Caouette-Laberge, Louise, St-Vil, Dickens, Aspirot, Ann, Piché, Nelson, Joharifard, Shahrzad, Safa, Nadia, Laberge, Jean-Martin, Emil, Sherif, Puligandla, Pramod, Shaw, Kenneth, Wissanji, Hussein, Duggan, Eileen, Guadagno, Elena, Puentes, Maria Consuelo, Leal, Paola Osses, Mendez Benavente, Carolina, Rygl, Michal, Trojanová, Barbora, Berková, Klára, Racková, Tereza, Planka, Ladislav, Škvařil, Jan, Štichhauer, Radek, Sabti, Shahad, Macdonald, Alex, Bouhadiba, Nordeen, Kufeji, Dorothy, Pardy, Caroline, Mccluney, Simon, Keshtgar, Alireza, Roberts, Rebecca, Rhodes, Hannah, Burns, Kate, Garrett-Cox, Robin, Ford, Kat, Cornwall, Hannah, Ravi, Krithi, Arthur, Felicity, Losty, Paul, Lander, Tony, Jester, Ingo, Arul, Suren, Gee, Oliver, Soccorso, Giampiero, Singh, Michael, Pachl, Max, Martin, Benjamin, Alzubair, Afnan, Kelay, Arun, Sutcliffe, Jonathan, Middleton, Thomas, Thomas, Amy Hughes, Kurian, Merina, Cameron, Fraser, Sivaraj, Jayaram, Thomas, Mark C, Rex, Dean, Jones, Ceri, Bradshaw, Kate, Bonnard, Arnaud, Delforge, Xavier, Duchesne, Camille, Gall, Caroline Le, Defert, Coralie, Laraqui Hossini, Samia, Guerin, Florent, Hery, Géraldine, Fouquet-Languillat, Virginie, Kohaut, Jules, Broch, Aline, Blanc, Thomas, Harper, Luke, Delefortrie, Thomas, Ballouhey, Quentin, Fourcade, Laurent, Grosos, Céline, Parmentier, Benoit, Levard, Guillaume, Grella, Maria Giovanna, Renaux Petel, Mariette, Grynberg, Lucie, Abbo, Olivier, Mouttalib, Sofia, Juricic, Mélodie, Scalabre, Aurelien, Haraux, Elodie, Rissmann, Anke, Krause, Hardy, Goebel, Peter, Patzer, Ludwig, Rolle, Udo, Schmedding, Andrea, Antunez-Mora, Alexandra, Tillig, Bernd, Bismarck, Sylvester von, Barbosa, Patricia Reis, Knorr, Christian, Stark, Domitille, Brunero, Marco, Avolio, Luigi, Manni, Francesco, Molinelli, Matilde, Guazzotti, Marinella, Raffaele, Alessandro, Romano, Piero Giovanni, Cavaiuolo, Silvia, Parigi, Gian Battista, Juhasz, Laszlo, Rieth, Anna, Strumila, Arunas, Dagilytė, Rūta, Liubsys, Arunas, Gurskas, Pranas, Malcius, Dalius, Mikneviciute, Agne, Vinskaite, Asta, Barauskas, Vidmantas, Vierboom, Liam, Hall, Timothy, Beasley, Spencer, Goddard, Lucy, Stringer, Mark, Weeratunga, Naveen, Adams, Stephen, Cama, Jitoko, Wong, Marilyn, Jayaratnam, Sridharan, Kukkady, Askar, Samarakkody, Udaya, Gerus, Sylwester, Patkowski, Dariusz, Wolny, Agnieszka, Koszutski, Tomasz, Tobor, Szymon, Osowicka, Marta, Czauderna, Piotr, Wyrzykowski, Dariusz, Garnier, Hanna, Anzelewicz, Stefan, Marta, Osowicka, Knurowska, Agata, Weiszewsk, Alicja, Grabowski, Andrzej, Korlacki, Wojciech, Pasierbek, Michal, Wolak, Przemyslaw, Piotrowska, Aneta, Roszkiewicz, Anna, Kalicińsk, Piotr, Trypens, Agata, Kowalewsk, Grzegorz, Sigalet, David, Alsaied, Amer, Ali, Mansour, Alsaggaf, Ameen, Ghallab, Alaa, Owiwi, Yazeed, Zeinelabdeen, Ali, Fayez, Mohamed, Atta, Ahmed, Zidan, Mazen, Radwan, Asaad saleh, Shalaby, Hanin, Abdelbaqi, Reem, Alattas, Khalid, Kano, Yar, Sindi, Omar, Alshehri, Abdullah, Altokhais, Tariq, Alturki, Fahad, Almosaibli, Mohammad, Krisanova, Dasha, Abbas, Wisam, Yang, Hee-Beom, Kim, Hyun-Young, Youn, Joong Kee, Chung, Jae Hee, Cho, Seok Hyeon, Hwang, In ji, Lee, Ju yeon, Song, Eung song, Arboleda, Jenny, Ruiz de Temiño Bravo, Mercedes, Siles Hinojosa, Alexander, García, Miriam, Casal Beloy, Isabel, Oliu San Miguel, Detlef, Molina Vazquez, Maria Elena, Alonso, Verónica, Sanchez, Alberto, Gomez, Oscar, Carrillo, Isabel, Wester, Tomas, Mesas Burgos, Carmen, Salö, Martin, Omling, Erik, Rudolfson, Niclas, Granéli, Christina, Arnadóttir, Helena, Grottling, Emma, Abrahamsson, Kate, Gatzinsky, Vladimir, Dellenmark Blom, Michaela, Borbonet, Daniel, Puglia, Paul, Jimenez Morejon, Vinicio, Acuna, Gaston, Moraes, Mario, Chan, Jonathan, Brahmamdam, Pavan, Tom, Alan, Sherer, Karen, Gonzales, Brandy, Cunningham, Aaron, Krishnaswami, Sanjay, Baertschiger, Reto, Leech, Mary, Williams, Regan, Camp, Lauren, Gosain, Ankush, Mora, Maria, Lyttle, Bailey D., Chang, Jeremy, McColl Makepeace, Lydia, Fowler, Kathryn L, Mansfield, Sara, Hodgman, Erica, Amaechi, Chukwubinyelum, Beres, Alana, Pernik, Mark N., Dosselman, Luke J., Almasri, Murad, Jain, Sunil, Modi, Varun, Fernandez Ferrer, Marianelly, Coon, John, Gonzalez, Joann, Honhar, Medhavi, Ruzgar, Nensi, Coghill, Griffin, Ullrich, Sarah, Cheung, Maija, Løfberg, Katrine, Greenberg, Jodie, Davenport, Kate, Gadepalli, Samir, Fox, Sarah, Johnson, Stephanie, Pilkington, Mercedes, Hamilton, April, Lin, Nicole, Sola, Juan, Yao, Yang, Davis, Jenna Kylene, Langer, Monica, Vacek, Jonathan, Abdullah, Fizan, Khlevner, Julie, Middlesworth, William, Levitt, Marc, Ahmad, Hira, Siddiqui, Sabina M, Bowder, Alex, Derks, Terry, Amoabin, Afua Amoabin, Pinar, Brooke, Owusu-Sekyere, Frank, Saousen, Benmanseur, Naidoo, Rasika, Karamustafic, Azra, Oliveira, Danielle Paula de, Motter, Sarah Bueno, Andrade, Jerhy, Šafus, Antonín, Langley, Jason, Wilke, Alexandra, Deya, Corazone, Murtadi, Habib Mansour, Berzanskis, Mindaugas, Calistus, Nwachukwu, Ajiboye, Olalekan S., Felix, Michael, Olabisi, Osagie O, Erçin, Seçil, Muradi, Teymursha, Burks, Stephen S., Lerma, Sergio, Jacobson, Jillian, Calancea, Calin, Valerio-Vazquez, Rafael, Sikwete, Guigui, Sekyere, Owusu, Mbonisweni, Akhona, Syed, Shahnoor, Hyeon, Cho Seok, Pajouhandeh, Fatemeh, and Kunfah, Sheba Mary Pognaa

Published
2021
Full Text
View/download PDF

36. Information Entropy Evaluation Method for the Disassemblability of Smartphones.

Author

Huang, Haihong, Xue, Yuhao, Zhu, Libin, and Cui, Chuangchuang

Abstract

Smartphones are a vast category of consumer electronic products. Design For Disassembly (DFD) is a methodology that can effectively reduce the disassembly, recycling, and maintenance costs of smartphones. However, due to the diverse constraints posed by the connections among smartphone components, the variability of disassembling tools makes it challenging to objectively characterize the disassemblability of the smartphone. Therefore, information entropy is introduced to characterize the complex state of the system. Disassemblability can be expressed by calculating the operating time of the disassembling tool through information entropy. After multiplying by a constrained quantity factor, the Improved Disassembling Tool Entropy (IDTE) responds to changes in disassemblability as the structural level changes. According to the structure and recycling direction, the disassembling level of smartphones can be divided into module level, part level, and hybrid part-module level. Based on the Maynard Operational Sequencing Technique (MOST), the basic unit of operating time of the disassembling tool is calculated. For the hybrid selective Disassembly Sequence Planning (DSP) of the part and module levels, the Improved Double Genetic Algorithm (IDGA) model is established to compute the optimal disassembly sequence corresponding to each program level. This model calculates the IDTE corresponding to the optimal disassembly sequences at each disassembling level. The validity of IDTE was verified by a coupled comparison test between IDTE and theoretical disassembly time. Finally, an analysis of the disassemblability variations was conducted for two different models of smartphones based on their structure. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

49. Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning.

Author

Zhang, Lechao, Xue, Jianxia, Xie, Yi, Huang, Danfei, Xie, Zhonghao, Zhu, Libin, Chen, Xiaoqing, Cui, Guihua, Ali, Shujat, Huang, Guangzao, and Chen, Xiaojing

Abstract

Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter‐patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single‐sample modelling to detect necrotic sites in small intestinal tissue. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

50. Research on Optimization of Sintering Mixture with Low-Grade Complex Ore

Author

Ding, Yuchuan, Zhu, Zizong, Zhou, Zhiqiang, Xiong, Hao, Zhu, Libin, Zhang, Lei, editor, Drelich, Jaroslaw W., editor, Neelameggham, Neale R., editor, Guillen, Donna Post, editor, Haque, Nawshad, editor, Zhu, Jingxi, editor, Sun, Ziqi, editor, Wang, Tao, editor, Howarter, John A, editor, Tesfaye, Fiseha, editor, Ikhmayies, Shadia, editor, Olivetti, Elsa, editor, and Kennedy, Mark William, editor

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results