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1. Coastlines violate the Schramm-Loewner Evolution

Author

Abril, Leidy M. L., Oliveira, Erneson A., Moreira, André A., Andrade Jr., José S., and Herrmann, Hans J.

Subjects
Condensed Matter - Statistical Mechanics
Abstract

Mandelbrot's empirical observation that the coast of Britain is fractal has been confirmed by many authors, but it can be described by the Schramm--Loewner Evolution? Since the self-affine surface of our planet has a positive Hurst exponent, one would not expect a priori any critical behavior. Here, we investigate numerically the roughness and fractal dimension of the isoheight lines of real and artificial landscapes. Using a novel algorithm to take into account overhangs, we find that the roughness exponent of isoheight lines is consistent with unity regardless of the Hurst exponent of the rough surface. Moreover, the effective fractal dimension of the iso-height lines decays linearly with the Hurst exponent of the surface. We perform several tests to verify if the complete and accessible perimeters would follow the Schramm--Loewner Evolution and find that the left passage probability test is clearly violated, implying that coastlines violate SLE., Comment: 17 pages, 11 figures

Published
2024
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3. Random networks with q-exponential degree distribution

Author

Filho, Cesar I. N. Sampaio, Bastos, Marcio M., Herrmann, Hans J., Moreira, André A., and Andrade Jr, José S.

Subjects
Condensed Matter - Statistical Mechanics
Abstract

We use the configuration model to generate networks having a degree distribution that follows a $q$-exponential, $P_q(k)=(2-q)\lambda[1-(1-q)\lambda k]^{1/(q-1)}$, for arbitrary values of the parameters $q$ and $\lambda$. We study the assortativity and the shortest path of these networks finding that the more the distribution resembles a pure power law, the less well connected are the corresponding nodes. In fact, the average degree of a nearest neighbor grows monotonically with $\lambda^{-1}$. Moreover, our results show that $q$-exponential networks are more robust against random failures and against malicious attacks than standard scale-free networks. Indeed, the critical fraction of removed nodes grows logarithmically with $\lambda^{-1}$ for malicious attacks. An analysis of the $k_s$-core decomposition shows that $q$-exponential networks have a highest $k_s$-core, that is bigger and has a larger $k_s$ than pure scale-free networks. Being at the same time well connected and robust, networks with $q$-exponential degree distribution exhibit scale-free and small-world properties, making them a particularly suitable model for application in several systems., Comment: 6 pages, 8 figures

Published
2022

4. Application of Artificial Intelligence to Improve Skills in Electropneumatic Control Systems to Engineering Students

Author

da Silva, António Ferreira, Santos, Adriano A., Pereira, Filipe, Felgueiras, Carlos, Moreira, André Pimentel, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Machado, Jose, editor, Soares, Filomena, editor, Yildirim, Sahin, editor, Vojtěšek, Jiří, editor, Rea, Pierluigi, editor, Gramescu, Bogdan, editor, and Hrybiuk, Olena O., editor

Published
2024
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6. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides

Author

Quiros, Adalberto Claudio, Coudray, Nicolas, Yeaton, Anna, Yang, Xinyu, Liu, Bojing, Le, Hortense, Chiriboga, Luis, Karimkhan, Afreen, Narula, Navneet, Moore, David A., Park, Christopher Y., Pass, Harvey, Moreira, Andre L., Quesne, John Le, Tsirigos, Aristotelis, and Yuan, Ke

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists. These images contain complex information requiring time-consuming expert human interpretation that is prone to human bias. Supervised deep learning approaches have proven powerful for classification tasks, but they are inherently limited by the cost and quality of annotations used for training these models. To address this limitation of supervised methods, we developed Histomorphological Phenotype Learning (HPL), a fully blue{self-}supervised methodology that requires no expert labels or annotations and operates via the automatic discovery of discriminatory image features in small image tiles. Tiles are grouped into morphologically similar clusters which constitute a library of histomorphological phenotypes, revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer tissues, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. We then demonstrate that these properties are maintained in a multi-cancer study. These results show the clusters represent recurrent host responses and modes of tumor growth emerging under natural selection. Code, pre-trained models, learned embeddings, and documentation are available to the community at https://github.com/AdalbertoCq/Histomorphological-Phenotype-Learning

Published
2022

15. Profile analysis of emerging respiratory virus in children

Author

Moreira, André Luís Elias, da Silva, Paulo Alex Neves, Assunção, Leandro do Prado, Santos, Mônica de Oliveira, Ito, Célia Regina Malveste, de Araújo, Kelliane Martins, Cunha, Marcos de Oliveira, Rabelo, Vívian da Cunha, de Souza, Paula Pires, Maia, Sibely Braga Santos, Peixoto, Fernanda Aparecida de Oliveira, Wastowski, Isabela Jubé, Carneiro, Lilian Carla, and Avelino, Melissa Ameloti Gomes

Published
2023
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18. Adeno-to-squamous transition drives resistance to KRAS inhibition in LKB1 mutant lung cancer

Author

Tong, Xinyuan, Patel, Ayushi S., Kim, Eejung, Li, Hongjun, Chen, Yueqing, Li, Shuai, Liu, Shengwu, Dilly, Julien, Kapner, Kevin S., Zhang, Ningxia, Xue, Yun, Hover, Laura, Mukhopadhyay, Suman, Sherman, Fiona, Myndzar, Khrystyna, Sahu, Priyanka, Gao, Yijun, Li, Fei, Li, Fuming, Fang, Zhaoyuan, Jin, Yujuan, Gao, Juntao, Shi, Minglei, Sinha, Satrajit, Chen, Luonan, Chen, Yang, Kheoh, Thian, Yang, Wenjing, Yanai, Itai, Moreira, Andre L., Velcheti, Vamsidhar, Neel, Benjamin G., Hu, Liang, Christensen, James G., Olson, Peter, Gao, Dong, Zhang, Michael Q., Aguirre, Andrew J., Wong, Kwok-Kin, and Ji, Hongbin

Published
2024
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24. Concepts for the Development of Person-Centered, Digitally Enabled, Artificial Intelligence–Assisted ARIA Care Pathways (ARIA 2024)

Author

Bousquet, Jean, Schünemann, Holger J., Sousa-Pinto, Bernardo, Zuberbier, Torsten, Togias, Alkis, Samolinski, Boleslaw, Bedbrook, Anna, Czarlewski, Wienczyslawa, Hofmann-Apitius, Martin, Litynska, Justyna, Vieira, Rafael José, Anto, Josep M., Fonseca, Joao A., Brozek, Jan, Bognanni, Antonio, Brussino, Luisa, Canonica, G. Walter, Cherrez-Ojeda, Ivan, Cruz, Alvaro A., Vecillas, Leticia de las, Dykewicz, Mark, Gemicioglu, Bilun, Giovannini, Mattia, Haahtela, Tari, Jacobs, Marc, Jacomelli, Cristina, Klimek, Ludger, Kvedariene, Violeta, Larenas-Linnemann, Desiree E., Louis, Gilles, Lourenço, Olga, Leemann, Lucas, Morais-Almeida, Mario, Neves, Ana Luisa, Nadeau, Kari C., Nowak, Artur, Palamarchuk, Yuliia, Palkonen, Susanna, Papadopoulos, Nikolaos G., Parmelli, Elena, Pereira, Ana Margarida, Pfaar, Oliver, Regateiro, Frederico S., Savouré, Marine, Taborda-Barata, Luis, Toppila-Salmi, Sanna K., Torres, Maria J., Valiulis, Arunas, Ventura, Maria Teresa, Williams, Sian, Yepes-Nuñez, Juan J., Yorgancioglu, Arzu, Zhang, Luo, Zuberbier, Jaron, Abdul Latiff, Amir Hamzah, Abdullah, Baharudin, Agache, Ioana, Al-Ahmad, Mona, Al-Nesf, Maryam Ali, Al Shaikh, Nada A., Amaral, Rita, Ansotegui, Ignacio J., Asllani, Julijana, Balotro-Torres, Maria Cristina, Bergmann, Karl-Christian, Bernstein, Jonathan A., Bindslev-Jensen, Carsten, Blaiss, Michael S., Bonaglia, Cristina, Bonini, Matteo, Bossé, Isabelle, Braido, Fulvio, Caballero-Fonseca, Fernan, Camargos, Paulo, Carreiro-Martins, Pedro, Casale, Thomas, Castillo-Vizuete, José-Antonio, Cecchi, Lorenzo, Teixeira, Maria do Ceu, Chang, Yoon-Seok, Loureiro, Claudia Chaves, Christoff, George, Ciprandi, Giorgio, Cirule, Ieva, Correia-de-Sousa, Jaime, Costa, Elisio M., Cvetkovski, Biljana, de Vries, Govert, Del Giacco, Stefano, Devillier, Philippe, Dokic, Dejan, Douagui, Habib, Durham, Stephen R., Enecilla, Maria Lourdes, Fiocchi, Alessandro, Fokkens, Wytske J., Fontaine, Jean-François, Gawlik, Radoslaw, Gereda, Jose E., Gil-Mata, Sara, Giuliano, Antonio F.M., Gotua, Maia, Gradauskiene, Brigita, Guzman, Maria Antonieta, Hossny, Elham, Hrubiško, Martin, Iinuma, Tomohisa, Irani, Carla, Ispayeva, Zhanat, Ivancevich, Juan Carlos, Jartti, Tuomas, Jeseňák, Miloš, Julge, Kaja, Jutel, Marek, Kaidashev, Igor, Bennoor, Kazi S., Khaltaev, Nicolai, Kirenga, Bruce, Kraxner, Helga, Kull, Inger, Kulus, Marek, Kuna, Piotr, Kupczyk, Maciej, Kurchenko, Andriy, La Grutta, Stefania, Lane, Stephen, Miculinic, Neven, Lee, Sang Min, Le Thi Tuyet, Lan, Lkhagvaa, Battur, Louis, Renaud, Mahboub, Bassam, Makela, Mika, Makris, Michael, Maurer, Marcus, Melén, Eric, Milenkovic, Branislava, Mohammad, Yousser, Moniuszko, Marcin, Montefort, Stephen, Moreira, Andre, Moreno, Pablo, Mullol, Joaquim, Nadif, Rachel, Nakonechna, Alla, Navarro-Locsin, Cecilia Gretchen, Neffen, Hugo E., Nekam, Kristof, Niedoszytko, Marek, Nunes, Elizabete, Nyembue, Dieudonné, O’Hehir, Robyn, Ollert, Markus, Ohta, Ken, Okamoto, Yoshitaka, Okubo, Kimihiro, Olze, Heidi, Padukudru, Mahesh Anand, Palomares, Oscar, Pali-Schöll, Isabella, Panzner, Petr, Palosuo, Kati, Park, Hae S., Passalacqua, Giovanni, Patella, Vincenzo, Pawankar, Ruby, Pétré, Benoît, Pitsios, Constantinos, Plavec, Davor, Popov, Todor A., Puggioni, Francesca, Quirce, Santiago, Raciborski, Filip, Ramonaité, Agné, Recto, Marysia, Repka-Ramirez, Susana, Roberts, Graham, Robles-Velasco, Karla, Roche, Nicolas, Rodriguez-Gonzalez, Monica, Romualdez, Joel A., Rottem, Menachem, Rouadi, Philip W., Salapatas, Marianella, Sastre, Joaquin, Serpa, Faradiba S., Sayah, Zineb, Scichilone, Nicola, Senna, Gianenrico, Sisul, Juan Carlos, Solé, Dirceu, Soto-Martinez, Manuel E., Sova, Milan, Sozinova, Olga, Stevanovic, Katarina, Ulrik, Charlotte Suppli, Szylling, Anna, Tan, Frances M., Tantilipikorn, Pongsakorn, Todo-Bom, Ana, Tomic-Spiric, Vesna, Tsaryk, Vladyslav, Tsiligianni, Ioanna, Urrutia-Pereira, Marilyn, Rostan, Marylin Valentin, Sofiev, Mikhail, Valovirta, Erkka, Van Eerd, Michiel, Van Ganse, Eric, Vasankari, Tuula, Vichyanond, Pakit, Viegi, Giovanni, Wallace, Dana, Wang, De Yun, Waserman, Susan, Wong, Gary, Worm, Margitta, Yusuf, Osman M., Zaitoun, Fares, and Zidarn, Mihaela

Published
2024
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25. Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Author

Chen, Pingjun, Rojas, Frank R., Hu, Xin, Serrano, Alejandra, Zhu, Bo, Chen, Hong, Hong, Lingzhi, Bandyoyadhyay, Rukhmini, Aminu, Muhammad, Kalhor, Neda, Lee, J. Jack, El Hussein, Siba, Khoury, Joseph D., Pass, Harvey I., Moreira, Andre L., Velcheti, Vamsidhar, Sterman, Daniel H., Fukuoka, Junya, Tabata, Kazuhiro, Su, Dan, Ying, Lisha, Gibbons, Don L., Heymach, John V., Wistuba, Ignacio I., Fujimoto, Junya, Solis Soto, Luisa M., Zhang, Jianjun, and Wu, Jia

Published
2023
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26. KEAP1 mutation in lung adenocarcinoma promotes immune evasion and immunotherapy resistance

Author

Zavitsanou, Anastasia-Maria, Pillai, Ray, Hao, Yuan, Wu, Warren L., Bartnicki, Eric, Karakousi, Triantafyllia, Rajalingam, Sahith, Herrera, Alberto, Karatza, Angeliki, Rashidfarrokhi, Ali, Solis, Sabrina, Ciampricotti, Metamia, Yeaton, Anna H., Ivanova, Ellie, Wohlhieter, Corrin A., Buus, Terkild B., Hayashi, Makiko, Karadal-Ferrena, Burcu, Pass, Harvey I., Poirier, John T., Rudin, Charles M., Wong, Kwok-Kin, Moreira, Andre L., Khanna, Kamal M., Tsirigos, Aristotelis, Papagiannakopoulos, Thales, and Koralov, Sergei B.

Published
2023
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27. Deep learning integrates histopathology and proteogenomics at a pan-cancer level

Author

Aguet, François, Akiyama, Yo, Anand, Shankara, Anurag, Meenakshi, Babur, Özgün, Bavarva, Jasmin, Birger, Chet, Birrer, Michael J., Cantley, Lewis C., Cao, Song, Carr, Steven A., Ceccarelli, Michele, Chan, Daniel W., Chinnaiyan, Arul M., Cho, Hanbyul, Chowdhury, Shrabanti, Cieslik, Marcin P., Clauser, Karl R., Colaprico, Antonio, Zhou, Daniel Cui, da Veiga Leprevost, Felipe, Day, Corbin, Dhanasekaran, Saravana M., Domagalski, Marcin J., Dou, Yongchao, Druker, Brian J., Edwards, Nathan, Ellis, Matthew J., Selvan, Myvizhi Esai, Foltz, Steven M., Francis, Alicia, Geffen, Yifat, Getz, Gad, Gonzalez Robles, Tania J., Gosline, Sara J.C., Gümüş, Zeynep H., Heiman, David I., Hiltke, Tara, Hostetter, Galen, Hu, Yingwei, Huang, Chen, Huntsman, Emily, Iavarone, Antonio, Jaehnig, Eric J., Jewell, Scott D., Ji, Jiayi, Jiang, Wen, Johnson, Jared L., Katsnelson, Lizabeth, Ketchum, Karen A., Kolodziejczak, Iga, Krug, Karsten, Kumar-Sinha, Chandan, Lei, Jonathan T., Liang, Wen-Wei, Liao, Yuxing, Lindgren, Caleb M., Liu, Tao, Ma, Weiping, Rodrigues, Fernanda Martins, McKerrow, Wilson, Mesri, Mehdi, Nesvizhskii, Alexey I., Newton, Chelsea J., Oldroyd, Robert, Paulovich, Amanda G., Payne, Samuel H., Petralia, Francesca, Pugliese, Pietro, Reva, Boris, Rykunov, Dmitry, Satpathy, Shankha, Savage, Sara R., Schadt, Eric E., Schnaubelt, Michael, Schürer, Stephan, Shi, Zhiao, Smith, Richard D., Song, Xiaoyu, Song, Yizhe, Stathias, Vasileios, Storrs, Erik P., Terekhanova, Nadezhda V., Thangudu, Ratna R., Thiagarajan, Mathangi, Tignor, Nicole, Wang, Liang-Bo, Wang, Pei, Wang, Ying, Wen, Bo, Wiznerowicz, Maciej, Wu, Yige, Wyczalkowski, Matthew A., Yao, Lijun, Yaron, Tomer M., Yi, Xinpei, Zhang, Bing, Zhang, Hui, Zhang, Qing, Zhang, Xu, Zhang, Zhen, Wang, Joshua M., Hong, Runyu, Demicco, Elizabeth G., Tan, Jimin, Lazcano, Rossana, Moreira, Andre L., Li, Yize, Calinawan, Anna, Razavian, Narges, Schraink, Tobias, Gillette, Michael A., Omenn, Gilbert S., An, Eunkyung, Rodriguez, Henry, Tsirigos, Aristotelis, Ruggles, Kelly V., Ding, Li, Robles, Ana I., Mani, D.R., Rodland, Karin D., Lazar, Alexander J., Liu, Wenke, and Fenyö, David

Published
2023
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32. Dynamics of racial segregation and gentrification in New York City

Author

Operti, Felipe G., Moreira, André A., Gabrielli, Andrea, Makse, Hernan, and Andrade Jr, José S.

Subjects
Physics - Physics and Society, Condensed Matter - Statistical Mechanics, Physics - Data Analysis, Statistics and Probability
Abstract

Racial residential segregation is interconnected with several other phenomena such as income inequalities, property values inequalities, and racial disparities in health and in education. Furthermore, recent literature suggests the phenomena of gentrification as a cause of perpetuation or increase of racial residential segregation in some American cities. In this paper, we analyze the dynamics of racial residential segregation for white, black, Asian, and Hispanic citizens in New York City in the years of 1990, 2000, and 2010. It was possible to observe that segregation between white and Hispanic citizens, and discrimination between white and Asian ones has grown, while segregation between white and black is quite stable. Furthermore, we analyzed the per capita income and the Gini coefficient in each segregated zone, showing that the highest inequalities occur in the zones where there is overlap of high-density zones of pair of races. Focusing on census tracts that have changed density of population during these twenty years, and, particularly, by analyzing white and black people's segregation, our analysis reveals that a positive flux of white (black) people is associated to a substantial increase (decrease) of the property values, as compared with the city mean. Furthermore, by clustering the region of high density of black citizens, we measured the variation of area and displacement of the four biggest clusters in the period from 1990 to 2010. The large displacements (~ 1.6 Km) observed for two of these clusters, namely, one in the neighborhood of Harlem and the other inside the borough of Brooklyn, led to the emergence of typically gentrified regions., Comment: 19 pages, 12 figures

Published
2019

46. Percolation on Isotropically Directed Lattice

Author

de Noronha, Aurelio W. T., Moreira, André A., Vieira, André P., Herrmann, Hans J., Andrade, José S., and Carmona, Humberto A.

Subjects
Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics
Abstract

We investigate percolation on a randomly directed lattice, an intermediate between standard percolation and directed percolation, focusing on the isotropic case in which bonds on opposite directions occur with the same probability. We derive exact results for the percolation threshold on planar lattices, and present a conjecture for the value the percolation-threshold for in any lattice. We also identify presumably universal critical exponents, including a fractal dimension, associated with the strongly-connected components both for planar and cubic lattices. These critical exponents are different from those associated either with standard percolation or with directed percolation., Comment: 10 pages, 11 figures

Published
2018
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47. Overdamped dynamics of particles with repulsive power-law interactions

Author

Moreira, André A., Vieira, César M., Carmona, Humberto A., Andrade Jr., José S., and Tsallis, Constantino

Subjects
Condensed Matter - Statistical Mechanics
Abstract

We investigate the dynamics of overdamped $D$-dimensional systems of particles repulsively interacting through short-ranged power-law potentials, $V(r)\sim r^{-\lambda}\;(\lambda/D>1)$. We show that such systems obey a non-linear diffusion equation, and that their stationary state extremizes a $q$-generalized nonadditive entropy. Here we focus on the dynamical evolution of these systems. Our first-principle $D=1,2$ many-body numerical simulations (based on Newton's law) confirm the predictions obtained from the time-dependent solution of the non-linear diffusion equation, and show that the one-particle space-distribution $P(x,t)$ appears to follow a compact-support $q$-Gaussian form, with $q=1-\lambda/D$. We also calculate the velocity distributions $P(v_x,t)$ and, interestingly enough, they follow the same $q$-Gaussian form (apparently precisely for $D=1$, and nearly so for $D=2$). The satisfactory match between the continuum description and the molecular dynamics simulations in a more general, time-dependent, framework neatly confirms the idea that the present dissipative systems indeed represent suitable applications of the $q$-generalized thermostatistical theory., Comment: 5 pages, 5 figures

Published
2018
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48. Elastic backbone defines a new transition in the percolation model

Author

Filho, Cesar I. N. Sampaio, Andrade Jr., José S., Herrmann, Hans J., and Moreira, André A.

Subjects
Condensed Matter - Statistical Mechanics
Abstract

The elastic backbone is the set of all shortest paths. We found a new phase transition at $p_{eb}$ above the classical percolation threshold at which the elastic backbone becomes dense. At this transition in $2d$ its fractal dimension is $1.750\pm 0.003$, and one obtains a novel set of critical exponents $\beta_{eb} = 0.50\pm 0.02$, $\gamma_{eb} = 1.97\pm 0.05$, and $\nu_{eb} = 2.00\pm 0.02$ fulfilling consistent critical scaling laws. Interestingly, however, the hyperscaling relation is violated. Using Binder's cumulant, we determine, with high precision, the critical probabilities $p_{eb}$ for the triangular and tilted square lattice for site and bond percolation. This transition describes a sudden rigidification as a function of density when stretching a damaged tissue., Comment: 5 pages, 7 figures

Published
2018

49. The price of a vote: diseconomy in proportional elections

Author

Melo, Hygor Piaget M., Reis, Saulo D. S., Moreira, André A., Makse, Hernán A., and Andrade Jr, José S.

Subjects
Physics - Physics and Society, Condensed Matter - Statistical Mechanics
Abstract

The increasing cost of electoral campaigns raises the need for effective campaign planning and a precise understanding of the return of such investment. Interestingly, despite the strong impact of elections on our daily lives, how this investment is translated into votes is still unknown. By performing data analysis and modeling, we show that top candidates spend more money \emph{per vote} than the less successful and poorer candidates, a sublinearity that discloses a diseconomy of scale. We demonstrate that such electoral diseconomy arises from the competition between candidates due to inefficient campaign expenditure. Our approach succeeds in two important tests. First, it reveals that the statistical pattern in the vote distribution of candidates can be explained in terms of the independently conceived, but similarly skewed distribution of money campaign. Second, using a heuristic argument, we are able to predict a turnout percentage for a given election of approximately 63\%. This result is in good agreement with the average turnout rate obtained from real data. Due to its generality, we expect that our approach can be applied to a wide range of problems concerning the adoption process in marketing campaigns., Comment: 8 pages, 4 figures

Published
2017
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