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3,066 results on '"Ramina, A"'

4. Emerging roles of non-coding RNAs in modulating the PI3K/Akt pathway in cancer

Author

Mehrdad Hashemi, Elaheh Mohandesi Khosroshahi, Saba Asadi, Mahsa Tanha, Forough Ghatei Mohseni, Ramina Abdolmohammad Sagha, Elham Taheri, Paria Vazayefi, Helya Shekarriz, Fatemeh Habibi, Shaghayegh Mortazi, Ramin Khorrami, Noushin Nabavi, Mohsen Rashidi, Afshin Taheriazam, Payman Rahimzadeh, and Maliheh Entezari

Subjects
Non-coding RNAs, Cancer therapy, Cancer progression, PTEN, Chemotherapy, Radiotherapy, Genetics, QH426-470
Abstract

Cancer progression results from the dysregulation of molecular pathways, each with unique features that can either promote or inhibit tumor growth. The complexity of carcinogenesis makes it challenging for researchers to target all pathways in cancer therapy, emphasizing the importance of focusing on specific pathways for targeted treatment. One such pathway is the PI3K/Akt pathway, which is often overexpressed in cancer. As tumor cells progress, the expression of PI3K/Akt increases, further driving cancer advancement. This study aims to explore how ncRNAs regulate the expression of PI3K/Akt. NcRNAs are found in both the cytoplasm and nucleus, and their functions vary depending on their location. They can bind to the promoters of PI3K or Akt, either reducing or increasing their expression, thus influencing tumorigenesis. The ncRNA/PI3K/Akt axis plays a crucial role in determining cell proliferation, metastasis, epithelial-mesenchymal transition (EMT), and even chemoresistance and radioresistance in human cancers. Anti-tumor compounds can target ncRNAs to modulate the PI3K/Akt axis. Moreover, ncRNAs can regulate the PI3K/Akt pathway both directly and indirectly.

Published
2025
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6. Measurement of the background in the CMS muon detector in $${p}{p}$$ pp -collisions at $$\sqrt{s} = 13$$ s = 13 $$\,\text {Te}\hspace{-.08em}\text {V}$$ Te V

Author

CMS Muon Group, M. Tytgat, A. Muhammad, G. De Lentdecker, J. Jaramillo, L. Moureaux, L. Pétré, Y. Yang, C. Rendón, G. Gokbulut, Y. Hong, A. Samalan, G. A. Alves, F. Marujo da Silva, E. Alves Coelho, M. Barroso Ferreira Filho, E. M. Da Costa, D. De Jesus Damiao, B. C. Ferreira, S. Fonseca De Souza, K. Mota Amarilo, H. Nogima, A. Santoro, M. Thiel, A. Aleksandrov, L. Dimitrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, G. Mitev, L. Ratchev, G. Rashevski, M. Shopova, G. Sultanov, A. Dimitrov, L. Litov, B. Pavlov, P. Petkov, A. Petrov, E. Shumka, S. Keshri, S. Thakur, M. Chen, X. Dong, W. Gong, Q. Hou, C. Jiang, H. Kou, Z.-A. Liu, W. Luo, J. Song, L. Sun, N. Wang, Y. Wang, Z. Wang, C. Zhang, Y. Zhang, H. Zhang, J. Zhao, A. Agapitos, Y. Ban, A. Levin, Q. Li, S. J. Qian, D. Wang, K. Wang, Z. You, C. Avila, D. A. Barbosa Trujillo, A. Cabrera, C. A. Florez, J. Fraga, J. A. Reyes Vega, F. Ramirez, M. Rodriguez, J. D. Ruiz, N. Vanegas, H. Abdalla, A. A. Abdelalim, Y. Assran, A. Radi, I. Crotty, M. A. Mahmoud, L. Balleyguier, X. Chen, C. Combaret, G. Galbit, M. Gouzevitch, G. Grenier, I. B. Laktineh, A. Luciol, L. Mirabito, W. Tromeur, I. Bagaturia, I. Lomidze, O. Kemularia, Z. Tsamalaidze, U. Böttger, D. Eliseev, T. Hebbeker, K. Hoepfner, M. Merschmeyer, F. Ivone, S. Mukherjee, F. Nowotny, B. Philipps, H. Reithler, A. Sharma, F. Torres Da Silva De Araujo, S. Wiedenbeck, S. Zaleski, F. P. Zantis, M. Abbas, S. Mallows, G. Bencze, N. Beni, J. Molnar, Z. Szillasi, D. Teyssier, B. Ujvari, G. Zilizi, J. Babbar, S. Bansal, V. Bhatnagar, S. Chauhan, A. Kaur, H. Kaur, A. Kaur Sahota, S. Kumar, T. Sheokand, J. Singh, B. C. Choudhary, A. Kumar, M. Kumar Saini, M. Naimuddin, N. Majumdar, S. Mukhopadhyay, P. Rout, V. Amoozegar, B. Boghrati, M. Ebraimi, M. Mohammadi Najafabadi, E. Zareian, M. Abbrescia, R. Aly, M. Buonsante, A. Colaleo, N. De Filippis, D. Dell’Olio, G. De Robertis, W. Elmetenawee, N. Ferrara, M. Franco, G. Iaselli, N. Lacalamita, F. Licciulli, F. Loddo, M. Maggi, S. Martiradonna, S. Nuzzo, L. Longo, A. Pellecchia, G. Pugliese, R. Radogna, D. Ramos, A. Ranieri, F. M. Simone, A. Stamerra, D. Troiano, R. Venditti, P. Verwilligen, A. Zaza, G. Abbiendi, C. Baldanza, C. Battilana, A. Benvenuti, L. Borgonovi, V. Cafaro, F. R. Cavallo, A. Crupano, M. Cuffiani, G. M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, V. Giordano, C. Guandalini, L. Guiducci, S. Lo Meo, L. Lunerti, S. Marcellini, G. Masetti, F. L. Navarria, G. Paggi, A. Perrotta, F. Primavera, A. M. Rossi, T. Rovelli, G. Torromeo, L. Benussi, S. Bianco, R. Campagnola, M. Caponero, S. Colafranceschi, S. Meola, L. Passamonti, D. Piccolo, D. Pierluigi, G. Raffone, A. Russo, G. Saviano, S. Buontempo, A. Cagnotta, F. Carnevali, F. Cassese, N. Cavallo, A. De Iorio, F. Fabozzi, A. O. M. Iorio, L. Lista, P. Paolucci, G. Passeggio, B. Rossi, L. Barcellan, M. Bellato, M. Benettoni, A. Bergnoli, A. Bragagnolo, R. Carlin, L. Castellani, P. Checchia, L. Ciano, A. Colombo, D. Corti, F. Gasparini, U. Gasparini, F. Gonella, A. Gozzelino, A. Griggio, G. Grosso, M. Gulmini, R. Isocrate, E. Lusiani, G. Maron, M. Margoni, A. T. Meneguzzo, M. Migliorini, L. Modenese, F. Montecassiano, M. Negrello, M. Passaseo, J. Pazzini, L. Ramina, M. Rampazzo, M. Rebeschini, P. Ronchese, R. Rossin, F. Simonetto, M. Toffano, N. Toniolo, A. Triossi, S. Ventura, M. Zanetti, P. G. Zatti, P. Zotto, A. Zucchetta, S. AbuZeid, C. Aimè, A. Braghieri, S. Calzaferri, D. Fiorina, S. Gigli, P. Montagna, C. Riccardi, P. Salvini, I. Vai, P. Vitulo, N. Amapane, G. Cotto, D. Dattola, P. De Remigis, B. Kiani, C. Mariotti, S. Maselli, M. Pelliccioni, F. Rotondo, A. Staiano, D. Trocino, G. Umoret, E. Asilar, T. J. Kim, J. A. Merlin, S. Choi, B. Hong, K. S. Lee, J. Goh, J. Choi, J. Kim, U. Yang, I. Yoon, W. Jang, J. Heo, D. Kang, Y. Kang, D. Kim, S. Kim, B. Ko, J. S. H. Lee, I. C. Park, I. J. Watson, S. Yang, Y. Jeong, Y. Lee, I. Yu, G. Alasfour, T. Beyrouthy, Y. Gharbia, Y. Maghrbi, M. Otkur, H. Castilla-Valdez, H. Crotte Ledesma, R. Lopez-Fernandez, A. Sánchez Hernández, M. Ramírez García, E. Vazquez, M. A. Shah, N. Zaganidis, I. Pedraza, C. Uribe Estrada, A. Ahmad, W. Ahmed, M. I. Asghar, H. R. Hoorani, S. Muhammad, A. Wajid, J. Alcaraz Maestre, A. Álvarez Fernández, Cristina F. Fernandez Bedoya, L. C. Blanco Ramos, E. Calvo, C. A. Carrillo Montoya, J. M. Cela Ruiz, M. Cepeda, M. Cerrada, N. Colino, S. Cuadrado Calzada, J. Cuchillo Ortega, B. De La Cruz, C. I. de Lara Rodríguez, D. Fernández Del Val, J. P. Fernández Ramos, M. C. Fouz, D. Francia Ferrero, J. García Romero, O. Gonzalez Lopez, S. Goy Lopez, M. I. Josa, J. León Holgado, O. Manzanilla Carretero, I. Martín Martín, J. J. Martínez Morales, E. Martín Viscasillas, D. Moran, Á. Navarro Tobar, R. Paz Herrera, J. C. Puras Sánchez, J. Puerta Pelayo, S. Pulido Ferrero, I. Redondo, D. D. Redondo Ferrero, V. Salto Parra, S. Sánchez Navas, J. Sastre, L. Urda Gómez, J. Vazquez Escobar, J. F. de Trocóniz, F. Frias Garcia-Lago, R. Reyes-Almanza, B. Alvarez Gonzalez, J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, P. Leguina López, E. Palencia Cortezon, C. Ramón Álvarez, J. Prado Pico, V. Rodríguez Bouza, A. Soto Rodríguez, A. Trapote, C. Vico Villalba, B. Kailasapathy, K. Malagalage, D. U. J. Sonnadara, D. D. C. Wickramarathna, W. G. D. Dharmaratna, K. Liyanage, N. Perera, N. Wickramage, P. Aspell, M. Bianco, D. Bozzato, S. Brachet, A. Conde Garcia, A. Dabrowski, R. De Oliveira, F. Fallavollita, P. Kicsiny, E. Hazen, S. May, A. Peck, K. Salyer, I. Suarez, S. Abbott, J. Bonilla, R. Breedon, H. Cai, P. T. Cox, R. Erbacher, O. Kukral, C. McLean, G. Mocellin, M. Mulhearn, B. Regnery, M. Tripathi, G. Waegel, Y. Yao, J. Carlson, R. Cousins, A. Dasgupta, A. Datta, J. Hauser, M. Ignatenko, M. A. Iqbal, C. Lo, D. Saltzberg, C. Schnaible, V. Valuev, R. Clare, M. Gordon, G. Hanson, N. Amin, J. Bradmiller-Feld, C. Campagnari, T. Danielson, A. Dishaw, A. Dorsett, B. Marsh, H. Mei, M. Oshiro, J. Richman, F. Setti, M. F. Sevilla, P. Siddireddy, S. Wang, C. Aruta, V. Barashko, V. Cherepanov, M. Dittrich, A. Korytov, E. Kuznetsova, A. Madorsky, G. Mitselmakher, A. Muthirakalayil Madhu, N. Rawal, N. Terentyev, J. Wang, B. Alsufyani, S. Butalla, T. Elkafrawy, M. Hohlmann, E. Yanes, J. Eysermans, E. Barberis, Y. Haddad, Y. Han, G. Madigan, D. M. Morse, V. Nguyen, D. Wood, S. Bhattacharya, J. Bueghly, Z. Chen, K. A. Hahn, Y. Liu, Y. Miao, D. G. Monk, M. H. Schmitt, A. Taliercio, M. Velasco, B. Bylsma, M. Carrigan, R. De Los Santos, L. S. Durkin, C. Hill, K. Banicz, J. Liu, M. Matveev, B. P. Padley, D. Aebi, M. Ahmad, T. Akhter, A. Bolshov, O. Bouhali, R. Eusebi, J. Gilmore, T. Huang, E. Juska, T. Kamon, H. Kim, M. Kizlov, S. Malhotra, R. Mueller, R. Rabadan, D. Rathjens, A. Safonov, P. E. Karchin, A. Aravind, K. Black, I. De Bruyn, P. Everaerts, C. Galloni, M. Herndon, A. Lanaro, R. Loveless, J. Madhusudanan Sreekala, S. Mondal, D. Teague, W. Vetens, A. Warden, I. Azhgirey, V. Borshch, L. Chtchipunov, A. Egorov, G. Gavrilov, V. Golovtcov, M. Ivanov, V. Ivantchenko, Y. Ivanov, V. Karjavine, A. Khodinov, V. Kim, I. A. Kurochkin, P. Levchenko, V. Murzin, S. Nasybulin, V. Oreshkin, V. Palichik, V. Perelygin, A. Riabchikova, D. Sosnov, V. Sulimov, L. Uvarov, S. Vavilov, and A. Vorobyev

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The CMS detector, including its muon system, has been operating at the CERN LHC in increasingly challenging conditions for about 15 years. The muon detector was designed to provide excellent triggering and track reconstruction for muons produced in proton–proton collisons at an instantaneous luminosity ( $$\mathcal {L}$$ L ) of $$1 \times 10^{34}$$ 1 × 10 34 cm $$^{-2}$$ - 2 s $$^{-1}$$ - 1 . During the Run 2 data-taking period (2015–2018), the LHC achieved an instantaneous luminosity of twice its design value, resulting in larger background rates and making the efficient detection of muons more difficult. While some backgrounds result from natural radioactivity, cosmic rays, and interactions of the circulating protons with residual gas in the beam pipe, the dominant source of background hits in the muon system arises from proton–proton interactions themselves. Charged hadrons leaving the calorimeters produce energy deposits in the muon chambers. In addition, high-energy particles interacting in the hadron calorimeter and forward shielding elements generate thermal neutrons, which leak out of the calorimeter and shielding structures, filling the CMS cavern. We describe the method used to measure the background rates in the various muon subsystems. These rates, in conjunction with simulations, can be used to estimate the expected backgrounds in the High-Luminosity LHC. This machine will run for at least 10 years starting in 2029 reaching an instantaneous luminosity of $$\mathcal {L} = 5 \times \text {10}^\text {34}\,\text {cm}^\text {-2}\,\text {s}^\text {-1}$$ L = 5 × 10 34 cm -2 s -1 and increasing ultimately to $$\mathcal {L} = 7.5 \times \text {10}^\text {34}\,\text {cm}^\text {-2}\,\text {s}^\text {-1}$$ L = 7.5 × 10 34 cm -2 s -1 . These background estimates have been a key ingredient for the planning and design of the muon detector upgrade.

Published
2024
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7. GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search

Author

Bakshi, Nikhil Angad, Gupta, Tejus, Ghods, Ramina, and Schneider, Jeff

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OOIs rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75,000 sq. m. Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline., Comment: 7 pages, 5 figures, 1 table, for associated video see: https://youtu.be/K0jkzdQ_j2E , published in International Conference on Robotics and Automation (ICRA) 2023. Outstanding Deployed Systems Paper Winner

Published
2023
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8. Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

Author

Triozzi, Riccardo, Serafini, Andrea, Bellato, Marco, Bergnoli, Antonio, Bolognesi, Matteo, Brugnera, Riccardo, Cerrone, Vanessa, Chen, Chao, Clerbaux, Barbara, Coppi, Alberto, Corti, Daniele, Corso, Flavio dal, Dong, Jianmeng, Dou, Wei, Fan, Lei, Garfagnini, Alberto, Gavrikov, Arsenii, Gong, Guanghua, Grassi, Marco, Guizzetti, Rosa Maria, Hang, Shuang, He, Cong, Hu, Jun, Isocrate, Roberto, Jelmini, Beatrice, Ji, Xiaolu, Jiang, Xiaoshan, Li, Fei, Liang, Zehong, Lippi, Ivano, Liu, Hongbang, Liu, Hongbin, Liu, Shenghui, Liu, Xuewei, Luo, Daibin, Luo, Ronghua, Marini, Filippo, Mazzaro, Daniele, Modenese, Luciano, Molla, Marta Colomer, Ning, Zhe, Peng, Yu, Petitjean, Pierre-Alexandre, Pitacco, Alberto, Qi, Mengyao, Ramina, Loris, Rampazzo, Mirco, Rebeschini, Massimo, Redchuk, Mariia, Sun, Yunhua, Triossi, Andrea, Veronese, Fabio, von Sturm, Katharina, Wang, Peiliang, Wang, Peng, Wang, Yangfu, Wang, Yusheng, Wang, Yuyi, Wang, Zheng, Wei, Ping, Weng, Jun, Xian, Shishen, Xie, Xiaochuan, Xu, Benda, Xu, Chuang, Xu, Donglian, Xu, Hai, Yan, Xiongbo, Yan, Ziyue, Yang, Fengfan, Yang, Yan, Yang, Yifan, Ye, Mei, Zeng, Tingxuan, Zhang, Shuihan, Zhang, Wei, Zhang, Aiqiang, Zhang, Bin, Zhao, Siyao, Zi, Changge, Aiello, Sebastiano, Andronico, Giuseppe, Antonelli, Vito, Barresi, Andrea, Basilico, Davide, Beretta, Marco, Brigatti, Augusto, Bruno, Riccardo, Budano, Antonio, Caccianiga, Barbara, Cammi, Antonio, Campese, Stefano, Chiesa, Davide, Clementi, Catia, Cordelli, Marco, Dusini, Stefano, Fabbri, Andrea, Felici, Giulietto, Ferraro, Federico, Giammarchi, Marco Giulio, Landini, Cecilia, Lombardi, Paolo, Lombardo, Claudio, Maino, Andrea, Mantovani, Fabio, Mari, Stefano Maria, Martini, Agnese, Meroni, Emanuela, Miramonti, Lino, Montuschi, Michele, Nastasi, Massimiliano, Orestano, Domizia, Ortica, Fausto, Paoloni, Alessandro, Parmeggiano, Sergio, Petrucci, Fabrizio, Previtali, Ezio, Ranucci, Gioacchino, Re, Alessandra Carlotta, Ricci, Barbara, Romani, Aldo, Saggese, Paolo, Sanfilippo, Simone, Sirignano, Chiara, Sisti, Monica, Stanco, Luca, Strati, Virginia, Tortorici, Francesco, Tuvé, Cristina, Venettacci, Carlo, Verde, Giuseppe, and Votano, Lucia

Subjects
Physics - Instrumentation and Detectors
Abstract

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics.

Published
2023
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10. Mass testing of the JUNO experiment 20-inch PMTs readout electronics

Author

Coppi, Alberto, Jelmini, Beatrice, Bellato, Marco, Bergnoli, Antonio, Bolognesi, Matteo, Brugnera, Riccardo, Cerrone, Vanessa, Chen, Chao, Clerbaux, Barbara, Corti, Daniele, Corso, Flavio dal, Dong, Jianmeng, Dou, Wei, Fan, Lei, Garfagnini, Alberto, Gavrikov, Arsenii, Gong, Guanghua, Grassi, Marco, Guizzetti, Rosa Maria, Hang, Shuang, He, Cong, Hu, Jun, Isocrate, Roberto, Ji, Xiaolu, Jiang, Xiaoshan, Li, Fei, Liang, Zehong, Lippi, Ivano, Liu, Hongbang, Liu, Hongbin, Liu, Shenghui, Liu, Xuewei, Luo, Daibin, Luo, Ronghua, Marini, Filippo, Mazzaro, Daniele, Modenese, Luciano, Molla, Marta Colomer, Ning, Zhe, Peng, Yu, Petitjean, Pierre-Alexandre, Pitacco, Alberto, Qi, Mengyao, Ramina, Loris, Rampazzo, Mirco, Rebeschini, Massimo, Redchuk, Mariia, Serafini, Andrea, Sun, Yunhua, Triossi, Andrea, Triozzi, Riccardo, Veronese, Fabio, von Sturm, Katharina, Wang, Peiliang, Wang, Peng, Wang, Yangfu, Wang, Yusheng, Wang, Yuyi, Wang, Zheng, Wei, Ping, Weng, Jun, Xian, Shishen, Xie, Xiaochuan, Xu, Benda, Xu, Chuang, Xu, Donglian, Xu, Hai, Yan, Xiongbo, Yan, Ziyue, Yang, Fengfan, Yang, Yan, Yang, Yifan, Ye, Mei, Zeng, Tingxuan, Zhang, Shuihan, Zhang, Wei, Zhang, Aiqiang, Zhang, Bin, Zhao, Siyao, Zi, Changge, Aiello, Sebastiano, Andronico, Giuseppe, Antonelli, Vito, Barresi, Andrea, Basilico, Davide, Beretta, Marco, Brigatti, Augusto, Bruno, Riccardo, Budano, Antonio, Caccianiga, Barbara, Cammi, Antonio, Campese, Stefano, Chiesa, Davide, Clementi, Catia, Cordelli, Marco, Dusini, Stefano, Fabbri, Andrea, Felici, Giulietto, Ferraro, Federico, Giammarchi, Marco Giulio, Landini, Cecilia, Lombardi, Paolo, Lombardo, Claudio, Maino, Andrea, Mantovani, Fabio, Mari, Stefano Maria, Martini, Agnese, Meroni, Emanuela, Miramonti, Lino, Montuschi, Michele, Nastasi, Massimiliano, Orestano, Domizia, Ortica, Fausto, Paoloni, Alessandro, Parmeggiano, Sergio, Petrucci, Fabrizio, Previtali, Ezio, Ranucci, Gioacchino, Re, Alessandra Carlotta, Ricci, Barbara, Romani, Aldo, Saggese, Paolo, Sanfilippo, Simone, Sirignano, Chiara, Sisti, Monica, Stanco, Luca, Strati, Virginia, Tortorici, Francesco, Tuvé, Cristina, Venettacci, Carlo, Verde, Giuseppe, and Votano, Lucia

Subjects
Physics - Instrumentation and Detectors
Abstract

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%.

Published
2023
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11. Validation and integration tests of the JUNO 20-inch PMTs readout electronics

Author

Cerrone, Vanessa, von Sturm, Katharina, Bellato, Marco, Bergnoli, Antonio, Bolognesi, Matteo, Brugnera, Riccardo, Chen, Chao, Clerbaux, Barbara, Coppi, Alberto, Corso, Flavio dal, Corti, Daniele, Dong, Jianmeng, Dou, Wei, Fan, Lei, Garfagnini, Alberto, Gong, Guanghua, Grassi, Marco, Hang, Shuang, Guizzetti, Rosa Maria, He, Cong, Hu, Jun, Isocrate, Roberto, Jelmini, Beatrice, Ji, Xiaolu, Jiang, Xiaoshan, Li, Fei, Liang, Zehong, Lippi, Ivano, Liu, Hongbang, Liu, Hongbin, Liu, Shenghui, Liu, Xuewei, Luo, Daibin, Luo, Ronghua, Marini, Filippo, Mazzaro, Daniele, Modenese, Luciano, Ning, Zhe, Peng, Yu, Petitjean, Pierre-Alexandre, Pitacco, Alberto, Qi, Mengyao, Ramina, Loris, Rampazzo, Mirco, Rebeschini, Massimo, Redchuk, Mariia, Serafini, Andrea, Sun, Yunhua, Triossi, Andrea, Triozzi, Riccardo, Veronese, Fabio, Wang, Peiliang, Wang, Peng, Wang, Yangfu, Wang, Yusheng, Wang, Yuyi, Wang, Zheng, Wei, Ping, Weng, Jun, Xian, Shishen, Xie, Xiaochuan, Xu, Benda, Xu, Chuang, Xu, Donglian, Xu, Hai, Yan, Xiongbo, Yan, Ziyue, Yang, Fengfan, Yang, Yan, Yang, Yifan, Ye, Mei, Zeng, Tingxuan, Zhang, Shuihan, Zhang, Wei, Zhang, Aiqiang, Zhang, Bin, Zhao, Siyao, Zi, Changge, Aiello, Sebastiano, Andronico, Giuseppe, Antonelli, Vito, Barresi, Andrea, Basilico, Davide, Beretta, Marco, Brigatti, Augusto, Bruno, Riccardo, Budano, Antonio, Caccianiga, Barbara, Cammi, Antonio, Campese, Stefano, Chiesa, Davide, Clementi, Catia, Cordelli, Marco, Dusini, Stefano, Fabbri, Andrea, Felici, Giulietto, Ferraro, Federico, Giammarchi, Marco G., Landini, Cecilia, Lombardi, Paolo, Lombardo, Claudio, Maino, Andrea, Mantovani, Fabio, Mari, Stefano Maria, Martini, Agnese, Meroni, Emanuela, Miramonti, Lino, Montuschi, Michele, Nastasi, Massimiliano, Orestano, Domizia, Ortica, Fausto, Paoloni, Alessandro, Parmeggiano, Sergio, Petrucci, Fabrizio, Previtali, Ezio, Ranucci, Gioacchino, Re, Alessandra Carlotta, Ricci, Barbara, Romani, Aldo, Saggese, Paolo, Sanfilippo, Simone, Sirignano, Chiara, Sisti, Monica, Stanco, Luca, Strati, Virginia, Tortorici, Francesco, Tuvé, Cristina, Venettacci, Carlo, Verde, Giuseppe, and Votano, Lucia

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances., Comment: 20 pages, 13 figures

Published
2022
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12. 'Russians’ Favorite Pastime Is Standing in Line': Frank Whitson Fetter about Trade and Food Situation in Kazan (1930)

Author

Ramina O. Abilova

Subjects
frank whitson fetter, kazan, atssr, foreign tourists, soviet economy, trade, food situation, american economists, first five-year plan, History of Civilization, CB3-482, History (General) and history of Europe
Abstract

The article examines trade and food situation in Kazan during the first five-year plan based on the analysis of written and visual sources created by American economist Frank Whitson Fetter (1899–1991) during his six-week visit in 1930. As a professional economist, Fetter used a wide variety of tools to study the economic situation in the Soviet Union: participant observation, interviews, photography, and filming. He visited Kazan cooperatives and markets daily and recorded field data in his notebooks. Based on them, Fetter made detailed records in his journal. Currently, a significant part of the collected materials, including more than 300 photographs, is kept at David M. Rubenstein Rare Book & Manuscript Library, Duke University (USA). Particular attention in Fetter’s materials is paid to the Kazan trade situation (the behavior of sellers and buyers, queues, range of goods, price dynamics, interruptions in government supply, sanitary condition of private markets and establishments of the state cooperative trading network) and consumers’ status (their salaries, rationing system, food, availability and quality of clothing and footwear). Fetter witnessed the growing shortage of goods and the growing discontent of Kazan citizens during a radical change in the economic system of the USSR. The article reveals his view of the situation and provides photographs taken by him.

Published
2024
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13. HUBUNGAN PROGRAM PENGEMBANGAN USAHA AGRIBISNIS PERDESAAN (PUAP) TERHADAP KINERJA KELOMPOK TANI DI KECAMATAN SERAWAIKABUPATEN SINTANG

Author

Ramina, Abdul Hamid A. Yusra, Jajat Sudrajat

Subjects
Agriculture
Abstract

This research aim to analyse correlation program of Development Effort Agribisnis Countryside (PUAP) to Productivity Group Farmer in District Serawai and to know and analyse correlation program Development Effort Agribisnis Countryside (PUAP) to make-up of Productivity, Production, Revenue, Earnings and Cost of Farming in District Serawai. Hypothesis the raised is Anticipated farmer which accept fund of PUAP, productivity, production, revenue, earnings and cost of farmer bigger than a farmer which do not accept fund of PUAP in District Serawai. Anticipated thecorrelation program of Development Effort Agribisnis Countryside (PUAP) to make-up of farming production and earnings of farmer in District Serawai. This Research use descriptive method with quantitative approach. Data collected by dividing kuesioner to responder and with interview to elite figure and responder. Research executed in Bedaha countryside and Tunas Harapan countryside in January to March 2013. Population which is taken in this research four farmer group in Bedaha countryside and four farmer group in Tunas Harapan countryside. Taken Sampel by purposive counted 20 responder people each lot farmer. Amount of entirety sampel is 160 people. Conclusion, this research prove that farmer which accept fund of PUAP, productivity, production of farming and earnings of farmer bigger than a farmer which do not accept fund of PUAP in District Serawai. Raised by Suggestion is adjacent and tuition on an ongoing basis very expected by local farmer. Or courses training which must be passed to program receiver farmer, this is meant farmer to be able to exploit PUAP fund according to order. Keyword: Productivity, Production, Revenue, Earnings, Cost

Published
2015
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14. Cost Aware Asynchronous Multi-Agent Active Search

Author

Banerjee, Arundhati, Ghods, Ramina, and Schneider, Jeff

Subjects
Computer Science - Machine Learning, Computer Science - Multiagent Systems, Computer Science - Robotics
Abstract

Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets. In a realistic setting, agents also must consider the costs that their decisions incur. Previously proposed active search algorithms simplify the problem by ignoring uncertainty in the agent's environment, using myopic decision making, and/or overlooking costs. In this paper, we introduce an online active search algorithm to detect targets in an unknown environment by making adaptive cost-aware decisions regarding the agent's actions. Our algorithm combines principles from Thompson Sampling (for search space exploration and decentralized multi-agent decision making), Monte Carlo Tree Search (for long horizon planning) and pareto-optimal confidence bounds (for multi-objective optimization in an unknown environment) to propose an online lookahead planner that removes all the simplifications. We analyze the algorithm's performance in simulation to show its efficacy in cost aware active search.

Published
2022

15. Tranexamic acid versus placebo to prevent bleeding in patients with haematological malignancies and severe thrombocytopenia (TREATT): a randomised, double-blind, parallel, phase 3 superiority trial

Author

Estcourt, Lise J, McQuilten, Zoe K, Bardy, Peter, Cole-Sinclair, Merrole, Collins, Graham P., Crispin, Philip J., Curnow, Elinor, Curnow, Jennifer, Degelia, Amber, Dyer, Claire, Friebe, Adam, Floro, Lajos, Grand, Effie, Hudson, Cara, Jones, Gail, Joseph, Joanne, Kallmeyer, Charlotte, Karakantza, Marina, Kerr, Paul, Last, Sara, Lobo-Clarke, Maria, Lumley, Matthew, McMullin, Mary F, Medd, Patrick G., Morton, Suzy M., Mumford, Andrew D., Mushkbar, Maria, Parsons, Joseph, Powter, Gillian, Sekhar, Mallika, Smith, Laura, Soutar, Richard, Stevenson, William S., Subramoniapillai, Elango, Szer, Jeff, Thomas, Helen, Waters, Neil A., Wei, Andrew H., Westerman, David A., Wexler, Sarah A., Wood, Erica M., Stanworth, Simon J., Abioye, Adrienne, Afghan, Rabia, Ai, Sylvia Ai, Akanni, Magbor, Alajangi, Rajesh, Alam, Usmaan, Al-Bubseeree, Bahaa, Alderson, Sophie, Alderson, Craig, Ali, Sayed, Ali, Kabir, Alighan, Rookmeen, Allam, Rebecca Allam, Allen, Tania, Al-Sakkaf, Wesam, Ames, Kate, Anderson, Jacqueline, Andrews, Colin, Angel, Ann-Marie, Anlya, Manuela Anlya, Ansari, Farah, Appleby, Rowan, Arnold, Claire, Asbjornsdottir, Hulda, Asfaw, Biruk, Atkins, Elissa, Atkinson, Leela, Aubrey, Clare, Ayesha, Noor, Babbola, Lola, Badcock, David, Badcock, Samuel, Baggio, Diva, Bailiff, Ben, Baines, Kizzy, Baker, Holly, Baker, Victoria, Ball, Lindsay, Ball, Martin, Balquin, Irwin, Banks, Emma, Banos, George, Barnett, Jaytee, Barrie, Claire, Barron, Claire, Barton, Rebecca, Bason, Nina, Batta, Bindu, Bautista, Dianne, Bayley, Angela, Bayly, Emma, Baynes, Fionnuala, Bazargan, Ali, Bazeley, Rachel, Beadle, Yvonne, Beardsmore, Claire, Beattie, Kate, Bedford, Caroline, Behal, Rachna, Behan, Daniel, Bejan, Lilihna, Bell, Sarah, Bell, Karen, Bell, Louise, Bell, Kaitlyn, Benjamin, Reuben, Bennett, Sam, Benson, Gary, Benson, Warwick, Bent, Cameron, Bergin, Krystal, Berry, Alex, Besenyei, Stephanie, Besley, Caroline, Betteridge, Scott, Beveridge, Leigh, Bhattacharyya, Abir, Billen, Annelies, Bilmon, Ian, Binns, Emma, Birt, Mark, Bishop, David, Blanco, Andrea, Bleby, Lisa, Blemnerhet, Richard, Blombery, Piers, Blyth, Emily, Blythe, Nicola, Boal, Lauren, Boden, Ali, Bokhari, Syed W.I., Bongetti, Elisa, Booth, Stephen, Borley, Jayne, Bowen, David, Bowers, Dawn, Boyd, Stephen, Bradley, Sarah, Bradman, Helen, Bretag, Peta, Brillante, Maria, Brockbank, Rachel, Brough, Yasmin, Brown, Ellen, Brown, Jo, Brown, Eleanor, Brown, Claire, Brown, Jenny, Brown, Susan, Browning, Joe, Brownsdon, Alex, Bruce, David, Brydon-Hill, Ruth, Buckwell, Andrea, Burgess, Dannielle, Burke, Glenda, Burley, Kate, Burney, Claire, Burns, David, Burrows, Samuel, burton, Kieran, Butler, Jason, Cambalova, Lenka, Camozzi, Maria C., Campbell, Philip, Campfield, Karen, Campion, Victoria, Cargo, Catherine, Carmona, Julia, Carney, Dennis, Casan, Joshua, Cashman, Helen, Catt, Lorraine, Cattell, Michael, Cavill, Megan, Chadbone, Rachel, Chaganti, Sridhar, Chai, Yee, Chai, Khai Li, Chang, Joshua, Chapman, Judith, Chapman, Oliver G., Chapter, Tamika, Charlton, Andrew, Chau, Celina, Chauhan, Saleena, Chavda, Nikesh, Chen, Frederick, Chen, Melody, Chen, Meng Xi, Chen, Melanie, Chen, Melissa, Cheok, Kathleen, Cheung, Mai, Chidgey, Luke, Chmielokliec, Karolina, Choi, Philip, Choi, Jae, Chok, Anne, Chopra, Ruchika, Christopherson, Louise, Chu, Vicky, Chua, Chong Chyn, Chudakou, Pavel, Chugh, Vidushi, Chung, Chi, Clark, Erin, Clarke, Peter, Clarke, Kathleen, Clay, Jennifer, Clayton, Laura, Clements, Mitch, Clemmens, Jonathan, Clifford, Ruth, Collett, Dave, Collins, Maia, Collyer, Emily, Connolly, Maureen, Cook, Mark, Coombs, Sarah, Coppell, Jason, Cornwell, Sophie, Corrigan, Claire, Coughlin, Elizabeth, Couling, Jennifer, Cousins, Tony, Cowan, Catriona, Cox, Christine, Cox, Catherine, Coyle, Luke, Craig, Emily, Creasey, Thomas, Croan, Laura, Croft, Jane, Crosbie, Nicola, Crowe, Josephine, Crowther, Helen, Crozier, Jane, Culleton, Naomi, Cullis, Jonathan, Cumming, Anita, Cummins, Michelle, Cunningham, Adam, Curley, Cameron, Curtis, Samantha, Cuthbert, Robert, Cuthill, Kirsty, Dahahayake, Dinusha A, Dang, Amy, Davies, Marc, Davies, Ceri, Dawson, Emily, Day, Tom, De Abrew, Kanchana, De Lavallade, Hugues, De Silva, Neelaskshi, Dean, Georgina, Deane, Christopher, Demosthenous, Lisa, Desai, Amisha, Desborough, Michael, Devanny, Ian, Dhanapal, Jay, Dhani, Sundip, Di Martino, Vicky, Dickens, Emmy, DiCorleto, Carmen, Dinnett, Louise, Dirisan, Divya, Dixon, Karen, Dixon, Kiri, Doal, Inderjit, Dobivh, J, Docanto, Maria, Doecke, Helve, Donaldson, David, Donaldson, Kylee, Donohoe, Carrie, Douglas, Ashley, Doung, Stephen, Downer, Susan, D'Rozario, James, Drummond, Malcolm, Drummond, Mark, Drummond, Samantha, Drysdale, Elizabeth, D'Souza, Ross, D'Souza, Eugene, Dunn, Alex, Dutton, David, Dyson, Martin, Ediriwicurena, Kushani, Edleston, Sharon, Edwards, Dawn, Edwards, Morgan, Edwards, Anita, Eise, Nicole, Ellis, Steven, Ellis, Hayley, Elmonley, Shareef, Enstone, Rosemarie, Eordogh, Agnes, Erb, Sharon, Evans, Shannon, Evans, Megan, Ewing, Joanne, Eyre, Toby, Facey, Adam, Fammy, Mina, Farman, Jon, Farnell, Rachel, Favero, Laura, Fay, Keith, Ferguson, Karen, Fernon, Laura, Filshie, Robin, Finnegan, Damian, Fisher, Lisa, Flanagan, Asia, Fleck, Emma, Fletcher, Simon, Flora, Harpreet, Flower, Catherine, Fodor, Ioana, Foley, Heather, Folland, Emma, Folorunso, Comfort, Forbes, Molly, Fordwor, Katrina, Foster, Polly, Fox, Vanessa, Fox, Thomas, Francis, Olesya, Fryearson, Louise, Fuery, Madonna, Fung, Jiin, Furtado, Michelle, Galloway-Browne, Leanne, Gamble, Louise, Gamgee, Jeanette, Ganapathy, Arundathi, Gardner, Hayley, Gardner, Clare, Gasmelsheed, Noha, Gately, Amy, Gaynor, Lynda, Gebreid, Alex, Geffens, Ruth, George, Rachel, Gertner, Aniko, Ghebeh, Manar, Ghirardini, Emanuela, Giddings, Melainie, Gillett, Sandra, Gillett, Karen, Giri, Pratyush, Glass, Chris, Glewis, Sarah, Gooding, Sarah, Gordon, Olivia, Gordon, Joanne, Gottlieb, David, Gowda, Koushik, Gower, Elysie, Gray, Nicola, Grayer, Jo, Greaves, Elaine, Greenaway, Sally Anne, Greenfield, Graeme, Greenwood, Matthew, Gregory, Gareth, Griffin, James, Griffith, Julia, Griffith, James, Griffiths, Lindsey, Grzegrzolka, Paulina, Gu, Yisu, Guest, Jo, Guinai, Rosanna, Gullapalli, Veena, Gunolr, A., Guo, Lina, H, Wayne, Hagua, Sophia, Haile, Senait, Hall, Richard, Hamdollah-Zadeh, Maryam, Hanif, Zahra, Hanlon, Kathleen, Hann, Nicholas, Hanna, Ramez, Hannah, Guy, Hapuarachchi, Sameera, Hardman, Jacinta, Hardy, Alison, Harris, Anthony, Harris, Kylie, Harrison, Beth, Harrison, Simon, Harrison, Lea-Anne, Harrop, Sean, Harvey, Caroline, Hatcliffe, Faye, Hawking, Jo, Hawkins, Matthew, Hayden, Janet, Hayman, Michelle, Haynes, Elizabeth, Heaney, Nicholas, Hebbard, Andrew, Hempton, Jenny, Hendunneti, Sasanka, Henry, Maeve, Heywood, Jonathan, Hildyard, Catherine, Hill, Lydia, Hilldrith, Annette, Hitev, Petar, Hiwase, Smita, Hiwase, Devendra, Hoare, Chris, Hodge, Renate, Holloway, Amy, Holt, Chloe, Holton, Kelly, Homer, Lauren, Horne, Gillian, Horvath, Noemi, Hotong, Linda, Houdyk, Kristen, Houseman, Katy, Hoxhallari, Ilda, Hsu, Hannah, Hsu, Nina, Huang, Gillian, Hudson, Kerryn, Hufton, Melanie, Hughes, Timothy, Hughes, Siobhan, Hurley, Kate, Huxley, Rosie, Ibitoye, Temitope, Ibrouf, Abubaken, Inam, Farha, Indran, Tishya, Ingham, Karen, Innes, Calum, Irvine, David, Jaafar, Sarah, Jain, Manish, Jameson, Laura, Janjua, Pardeep, Jarvis, Rebecca, Jatheendran, Abirami, Javed, Abbie, Jen, Sheila, Jobanpura, Shailesh, Jobson, Irene, John, Deborah, Johns, Sophie, Johnston, Amanda, Jones, Hollie, Jones, Francesca, Joniak, Karolina, Jovanovic, Michael, Jovic, Anita, Joyce, Lauren, Judd, Andrew, Kakarlamudi, Sudhakar, Kakaroubas, Nick, Kalita, Maggie, Kam, Shirly, Kan, Julie, Kandle, P, Kanellopoulos, Alex, Kao, Chien, Kaparou, Maria, Kartsios, Charamlampos, Katsioulas, Vicki, Kaye, Russell, Keen, Katie, Kelly, Richard, Kelly, Pauline, Kelly, Donna, Kelly, Melanie, Kennedy, Glen, Kennedy, Nola, Kenny, Angela, Kenworthy, Zoe, Kerridge, Ian, Kesavan, Murali, Khafizi, Angelika, Khakwani, Muhammad, Khalid, Amna, Khamly, Kate, Khan, Anjum, Khan, Dalia, Khan, Mojid, Khan, Lubna, Khoo, Mona, Khwaja, Asim, Kim, Grace, King, Andrew, King, Vicky, King, Donna, Kinsella, Francesca, Kipp, David, Kirandeep, Pachoo, Kirui, Laura C., Kishore, Bhuvan, Knectlhi, Christopher, Knot, Amy, Knot, Armit, Ko, Cathy, Kolaric, Caitlin, Koo, Ray, Kotadia, Mary, Kothari, Jaimal, Kottaridis, Panagiotis D., Kuiluinathan, Gajan, Kulasekararaj, Austin, Kwan, John, Kwok, Marwan, Kwok, Phillip, Kwok, Fiona, Laane, Kristiina, Lad, Deena, Laird, Jennifer, Lam, Ada, Lane, Mary, Lanenco, Monica, Lang, Susan, Langridge, Alex, Langton, Catherine, Lannon, Michelle, Latif, Annie, Latimer, Maya, Latter, Ruth, Lau, I-Jun, Lawless, Sarah, Lawless, Theresa, Leach, Mike, Leaney, Sarah, Leary, Heather, Leavy, James, LeBlanc, Abbey, Lee, Vivienne, Lee, Edwin, Lee, Jenny, Lee, Tamara, Leischkie, Marian, Leitinger, Emma, Leon, Christopher, Leonard, Jayne, Lewis, David, Lewis, Ian, Lewis, Tania, Lim, Daniel, Littlewood, Kelly, Liu, Dara, Loh, Joanna, Lokare, Anand, Lomas, Oliver, Lovell, Richard, Lowe, Theresa, Lowry, Lisa, Lubowiecki, Marcin, Lumb, Rebecca, Lynch, Gail, Macaulay, Amanda, MacDonald, Lyndsey, MacDonald-Burn, Jill, Macmillan, Margaret, Maddock, Karen, Mahaliyana, Tomas, Mahon, Cassandra, Maidment, Alison, Maier, Susie, Mairos, Michelle, Majid, Mahseeman, Mak, Ka L, Mak, Anne, Malendrayogau, Arunthrthy, Malham, Hana, Malyon, Felicity, Mandadapu, Vineela, Mandel, Laura, Mant, Sarah, Manton, Ruth, Maouche, Nadjoua, Maqbool, Muhammad G., Marchant, Gregory, Marinho, Mariana, Marks, David, Marner, Mike, Marr, Helen, Marshall, Gillian, Martin, Siobhan, Martin, Abigail, Marzolini, Maria, Mason, Kiara, Massie, Jonathan, Masson, Rebecca, Mathavan, Vidya, Mathew, Siju, Mathie, Judith, Mattocks, Lehenta, Maybury, Bernard, Mayer, Georgina, McAlister, Chyrelle, McAllister, Jo, McConnell, Stewart, McCracken, James, McCullagh, Liz, McCulloch, Rory, Mcdermott, Christopher, Mcdonald, Kerian, McGinniss, Laura, McGurk, Fiona, McIlwain, Jessica, McIver, Kirsten, Mckay, Pam, McKenna, Lorraine, Mclornan, Donal, McMahon, Coalon, McNeice, Linda, McNeill, Susan, McNickle, Molly, McQueen, Fiona, McRae, Simon, McTaggart, Bobby, Mehew, Jenny, Mehra, Varnn, Melly, Michelle, Menichelli, Tara, Micklethwatte, Ken, Mihailescue, Loredana, Mijovic, Aleksander, Millband, Hannah, Miller, Lucy, Millien, Samuel T., Milnthorpe, James, Minson, Adrian, Molnar, Eva, Monsour, Marc, Moody, Mary, Moon, Rebecca, Moore, Sally, Moore, Katy, Morgan, Kelly, Morralley, Rebecca, Morris, Denise, Morris, Kirk, Morrison, Nicole, Moss, Merinda, Mughal, Muhammad, Muir, Paul, Mukkath, David, Mulla, Aasiyu, Mulligan, Stephen, Mullings, Joanne, Mulqueen, Angela, Muluey, Caitlin, Murdoch, Sarah, Murrani, Sura, Murthy, Vidhya, Musngi, Jimmy, Mustafa, Nadreen, Mynes, Tracey, Nalpantidis, Anastasios, Nandurkar, Harshal, Nardone, Linda, Nasari, Latifa, Nash, Monica, Naylor, Georgina, Ngu, Loretta, Nguethina, Melissa, Nguyen, John, Nguyen, Joseph, Nichol, Wendy, Nicholls, Emma, Nicole, Catherine S., Nicolson, Phillip, Nielson, David, Nikolousis, Emmanouil, Nix, Georgina, Njoku, Rita, Norman, Jane, Norman, Amy, Norris, Phoebe, North, Daniel, Norwood, Megan, Notcheva, Gaynor, Novitzky-Basso, Igor, Nyaboko, Joseph, Nygren, Maria, Obu, Ingrid, O'Connell, Siobhan, O'Connor, Jody, O'Kelly, Deanna, O'Niell, Aideen, Ony, Jeremy, Oo, Kathy, Oo, April, Oppermann, Anne, Orr, Ruth, O'Sullivan, Mary, Page, Jennifer, Palfreyman, Emma, Paneesha, Shankaranarayana, Panicker, Shyam, Parbutt, Catherine, Parigi, Elesha, Paris, Gemma, Parker, Tracey, Parnell, Caroline, Parrish, Christopher, Parsons, Alex, Pasat, Mioara, Patel, Natasha, Patel, Vijay, Patel, Pooja, Patel, Chaya, Pati, Nalini, Patterson, Andrea, Paul, Lauren, Payet, Danielle, Payne, Elspeth, Peachey, Victoria, Pearson, Amanda, Peniket, Andy, Percy, Laura, Pereyra, Millicent, Pervaiz, Omer, Phalod, Gunjan D, Pham, Anh, Pho, Jason, Pickard, Keir, Pidcock, Michael, Piggin, Anna, Pishyar, Yalda, Pocock, Abigail, Pol, Ranjendres, Polzella, Paolo, Poolan, Sonia, Portingale, Vicki, Posnett, Claire, Potluri, Sandeep, Potter, Victoria, Pratt, Guy, Prodger, Catherine, Pueblo, Andres, Puliyayil, Anish, Puvanakumar, Pratheepan, Qadri, Abdul, Quach, Hang, Quinn, Michael, Rafferty, Mark, Rahman, Marzia, Raj, Kavita, Raj, Sonia, Rajendran, Ramina, Ramanan, Radha, Ramasamy, Karthik, Rampotas, Alexandros, Ranchhod, Natasha, Rashid, Sabia, Ratanjee, Sunita, Rathore, Gurpreet, Ratnasingam, Sumita, Rayat, Manjit, Rayner, Michael, Reddell-Denton, Rebecca, Redding, Nicola, Reddy, Udaya, Rehman, Atique, Rice, Carol, Riches, Iwona, Rider, Thomas, Riley, John, Rinaldi, Ciro, Roberts, Kayleigh, Roberts, Andrew, Robertson, Bryony, Robertson, Peter, Robinson, Dan, Robinson, Rebecca, Robjohns, Emma, Robledo, Laura, Rodrigues, Ana, Rofe, Chris, Roff, Bridie, Rogers, Rachel, Rolt, Jill, Rooney, Carmela, Rose, Kathy, Rose, Hannah, Ross, David, Rouf, Shahara, Rourke, Claire, Routledge, David, Ruggiero, Janet, Rule, Simon, Rumsey, Richard, Sagge, Cherry, Saldhana, Helen, Salisbury, Richard, Salisbury, Sarah, Salvaris, Ross, Sanders, Kay, Sangombe, Mirriam, Sanigorska, Anna, Santos, Kristine, Sarkis, Taylah, Sarma, Anita, Saunders, Natalie, Schmidt, Kara, Schmidtmann, Anja, Schumacher, Ann, Scorer, Tom Scorer, Scott, Asleigh, Seath, Ingrid, Sejman, Frances, Selim, Adrian, Shamim, Nadia, Shan, Jocelyn, Shanmuganathan, Naranie, Shanmugaranjan, Shaminie, Sharpe, Michelle, Sharpley, Faye, Shaw, Emma, Sheath, Cara, Sheehy, Oonagh, Shen, Vivian, Sherbide, Solomon, Sheridan, Mathew, Sheridan, Jane, Sheridon, Matthew, Shields, Tracy, Sim, Hau V, Sim, Shirlene, Sims, Matt, Singaraveloo, Lydia, Singh, Gurcharan, Singh, Jasmine, Sladesal, Shree, Sloan, Andrew, Slobodian, Peter, Smith, Sophie, Smith, Sarit, Smith, Claire, Smith, Alastair, Smith, Neil, Snowden, Katherine, Solis, Joel, Somios, Denise, Soo, Jade, Spanevello, Michelle, Spaulding, Madeleie, Spence, Laura, Spillane, Liz, Spiteri, Alisha, Sprigg, Naomi, Springett, Sally, Stafford, Lynn, Stainthorp, Katherine, Stark, Kate, Steeden, Louise, Stephen, Ella, Stephenson, Aisling, Stewart, Andrew, Stewart, Orla, Stobie, Emma, Stokes, Chelsea, Streater, Jacqui, Suddens, Charlie-Marie, Suntharalingam, Surenthini, Surana, Narinder, Sutherland, Robyn, Sutherland, Antony, Sutton, David, Sweeney, Connor, Sweet, Reilly, Szucs, Aniko P, Taheri, Leila E., Tailor, Hinesh, Tam, Constantine, Tambakis, George, Tamplin, Mary, Tan, Chee, Tan, Sui, Tan, Joanne, Tan, Zhi, Taran, Tatiana, Tarpey, Fiona, Taseka, Angela, Tasker, Suzy, Tatarczuch, Maciej, Tayabali, Sarrah, Taylor, Hannah, Taylor, Robert, Taylor, Melaine, Taylor-Moore, Ella, Teasdale, Lesley, Tebbet, Elizabeth, Tedjasepstra, Aditya, Tedjaseputra, Aditya, Tepkumkun, Oummy, Terpstra, Andrew, Thomas, Wayne, Thomas, Shanice, Thompson, Rachel, Thornton, Thomas, Thorp, Bronwyn, Thrift, Moi Yap, Thwaites, Phillipa, Timbres, Jasmine, Tindall, Lauren, Tiong, Ing Soo, Tippler, Nicole, Todd, Tony, Todd, Shirley, Toghill, Neil, Tomlinson, Eve, Tooth, Jacinta, Topp, M., Trail, Nicola, Tran, Nguyen, Tran, Elizabeth, Tran, Vi, Treder, Bona, Tribbeck, Michelle, Trochowski, Siobhan, Truslove, Maria, Tse, Tsun, Tseu, Bing, Tucker, David, Turner, Kelly, Turner, Dianne, Turner, Herleen, Turner, Gillian, Twohig, Julie, Tylee, Thomas, Uhe, Micheleine, Underhill, Lauren, V, Joanne, Van der Vliet, Georgina, Van Tonder, Tina, VanderWeyden, Carrie, Varghese, Jerry, Vaughan, Lachlan, Veale, David, Vickaryyous, Nicky, Vince, Kathryn, Von Welligh, Jacoba, Vora, Sona, Wadehra, Karan, Walker, Rebecca, Walker, Stephen, Wallace, Roslyn, Wallniosve, Stephanie, Wallwork, S., Walmsley, Zoe, Walters, Fiona, Wang, Joyce, Wang, Angela, Wang, Chen, Wanyika, Mercy, Warcel, Dana, Wardrobe, Katrina, Warnes, Kristian, Waterhouse, Christopher, Waterworth, Adam, Watson, Caroline, Watson, Edmund, Watts, Emily, Weaver, Emma, Weber, Nicholas, Webley, Kaytie, Welford, Anna, Wells, Matt, Westbury, Sarah, Westcott, Jackie, Western, Robyn, Weston, Julia, White, Jessica, White, Phillipa, Whitehead, Anna, Whitehouse, James, Wieringa, Samantha, Willan, John, Williams, Sandra, Williams, Bethany, Williamson, Stephanie, Willoughby, Brett, Wilmot, Gail, Wilmott, Rosalind, Wilson, Joanna, Wilson, Emma, Wilson, Suzy, Wilson, Heather, Wilson, Caroline, Wilson, Tanya, Wilton, Margaret, Wiltshire, Paula, Wincup, Joanne, Wolf, Julia, Wong, Henna, Wong, Cyndi, Wong, Daniel, Wong, Jonathan, Wong, Shi Qin, Wood, Sarah, Wood, Henry, Wooding, Jackie, Woolley, Kelly, Wright, Myles, Wynn-Williams, Roland, Yannakou, Costas, Yeoh, Zhi Han, Yeung, David, Young, Agnes, Yuen, Flora, Yuen, Agnes, Zaja, Oliver, Zhang, Xiao-Yin, and Zhang, Mei

Published
2025
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17. Multi-Agent Active Search using Detection and Location Uncertainty

Author

Banerjee, Arundhati, Ghods, Ramina, and Schneider, Jeff

Subjects
Computer Science - Robotics, Computer Science - Machine Learning, Computer Science - Multiagent Systems, I.2.9, I.2.11
Abstract

Active search, in applications like environment monitoring or disaster response missions, involves autonomous agents detecting targets in a search space using decision making algorithms that adapt to the history of their observations. Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty. The more common approach in robotics is to focus on location uncertainty and remove detection uncertainty by thresholding the detection probability to zero or one. In contrast, it is common in the sparse signal processing literature to assume the target location is accurate and instead focus on the uncertainty of its detection. In this work, we first propose an inference method to jointly handle both target detection and location uncertainty. We then build a decision making algorithm on this inference method that uses Thompson sampling to enable decentralized multi-agent active search. We perform simulation experiments to show that our algorithms outperform competing baselines that only account for either target detection or location uncertainty. We finally demonstrate the real world transferability of our algorithms using a realistic simulation environment we created on the Unreal Engine 4 platform with an AirSim plugin., Comment: Accepted to ICRA 2023

Published
2022

18. Common Chemical Plasticizer Di(2-Ethhylhexyl) Phthalate Exposure Exacerbates Coxsackievirus B3 Infection

Author

Ramina Kordbacheh, Madelyn Ashley, William D. Cutts, Taryn E. Keyzer, Shruti Chatterjee, Tyler J. Altman, Natalie G. Alexander, Timothy E. Sparer, Brandon J. Kim, and Jon Sin

Subjects
coxsackievirus B3 (CVB), Di(2-ethhylhexyl) phthalate (DEHP), proviral, environmental factors, Microbiology, QR1-502
Abstract

Di(2-ethhylhexyl) phthalate (DEHP) is a common plastic rubberizer. DEHP leaches from plastic matrices and is under increasing scrutiny as numerous studies have linked it to negative human health manifestations. Coxsackievirus B3 (CVB) is a human pathogen that typically causes subclinical infections but can sometimes cause severe diseases such as pancreatitis, myocarditis, and meningoencephalitis. Though CVB infections are common, severe illness is relatively rare, and it is unclear what factors mediate disease severity. In this study, we sought to determine the effects that DEHP has on CVB infection in a variety of human cell types to evaluate whether this plastic-derived pollutant could represent a proviral environmental factor. Methods: HeLa cervical cancer cells, human induced pluripotent stem cell-derived brain-like endothelial cells (iBECs), and Caco-2 colon carcinoma cells were exposed to 40 µg/mL DEHP for 24 h prior to infecting with enhanced green fluorescent protein (EGFP)-expressing CVB. The severity of the infection was evaluated via fluorescence microscopy and flow cytometry-based viral EGFP detection, viral plaque assay on tissue culture media, and Western blotting to detect VP1 viral capsid protein. Interferon-associated proteins such as interferon regulatory factor (IRF) 3, IRF7, interferon-induced transmembrane (IFITM) 2, and IFITM3 were measured by Western blotting. The roles of IFITM2 and IFITM3 in the context of CVB infection were evaluated via siRNA silencing. Results: We found that DEHP drastically increased CVB infection in each of the cell types we tested, and, while the cellular processes underlying DEHP’s proviral properties were not entirely clear, we observed that DEHP may subvert CVB-induced interferon signaling and elevate levels of IFITMs, which appeared to bolster CVB infection. Conclusions: DEHP may represent a major environmental factor associated with the severity of CVB infection. Further understanding of how DEHP exacerbates infection may better elucidate its potential role as a proviral environmental factor.

Published
2024
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20. Coping with Plenitude: A Computational Approach to Selecting the Right Algorithm

Author

Sotoudeh, Ramina and DiMaggio, Paul

Abstract

Sociologists increasingly face choices among competing algorithms that represent reasonable approaches to the same task, with little guidance in choosing among them. We develop a strategy that uses simulated data to identify the conditions under which different methods perform well and applies what is learned from the simulations to predict which method will perform best on never-before-seen empirical data sets. We apply this strategy to a class of methods that group respondents to attitude surveys according to whether they share construals of a given domain. This allows us to identify the relative strengths and weaknesses of the methods we consider, including relational class analysis, correlational class analysis, and eight other such variants. Results support the "no free lunch" view that researchers should abandon the quest for one best algorithm in favor of matching algorithms to kinds of data for which each is most appropriate and provide direction on how to do so.

Published
2023
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21. A comprehensive review of the neurological effects of anethole

Author

Ramina Khodadadian and Shima Balali- Dehkordi

Subjects
Anethole, Anti-oxidant properties, Anti-inflammatory effects, Nervous system, Therapeutic use, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Since ancient times many countries have employed medicinal plants as part of traditional medicine. Anethole is a substance found in various plants and has two isomers, cis-anethole (CA) and trans-anethole (TA). Currently, the food industry extensively use anethole as an aromatic and flavoring component. Extensive scientific research are warranted to provide scientific proof for the usage of anethole, given its widespread use and affordable price. Preclinical studies have suggested several pharmacological effects for anethole including neuroprotective properties. It has been determined that anethole through modulation of monoamines, gamma-aminobutyric acid (GABA)ergic and glutamatergic neurotransmissions as well as its possible anti-inflammatory and antioxidative stress properties affected central nervous system (CNS). In this concept previous studies have demonstrated anxiolytic, antidepressant, antinociceptive, anticonvulsant, and memory improvement effects for anethole. To fully understand its therapeutic potentials, more research are required to elucidate the precise mechanisms by which TA and CA affected CNS. This review summarizes the current knowledge on pharmacological activities of the anethole concentrating its neurological properties, and the possible mechanisms underlying these effects. Various pharmacological effects which have been reported suggesting that anethole could be considered as a potential agent for management of neurological disorders.

Published
2025
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22. Development and evaluation of a Bayesian network model for preventing distracted driving

Author

Ramina Javid, Eazaz Sadeghvaziri, and Mansoureh Jeihani

Subjects
Distracted driving prevention, Driving behaviors, Bayesian network, Transportation and communications, HE1-9990
Abstract

Distracted driving is one of the most significant factors leading to fatal car crashes. Using a cell phone while driving is one of the riskiest behaviors while driving and is the cause of death for hundreds of drivers in the United States. Distraction prevention technologies, such as cell phone blocking apps that limit the functioning of cell phones while the car is moving, are one strategy for combating distracted driving. The main goal of this study is to investigate the effect of cell phone blocking apps on driving behaviors and crashes caused by distracted driving using a machine learning algorithm. Some 158 participants were recruited from the state of Maryland to investigate their driving behavior using a state-specific survey. The results of the survey revealed that most people have cell phone blocking apps (62.6%); however, they do not use them on a daily basis (86.7%). A Bayesian network model was then deployed, and the results showed that if all drivers use cell phone blocking apps, crashes occurring due to distraction from cell phone use will decrease by 5 %, and self-reported distraction will decrease by 9 %. The results of this study can be used to detect distracted driving and find the best strategies to overcome this problem. The results also suggest that there should be a greater degree of awareness of distraction prevention technologies and education on the use of these technologies among different groups to reduce the number of fatalities, injuries, and crashes due to distraction.

Published
2023
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29. Multi-Agent Active Search using Realistic Depth-Aware Noise Model

Author

Ghods, Ramina, Durkin, William J., and Schneider, Jeff

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

The active search for objects of interest in an unknown environment has many robotics applications including search and rescue, detecting gas leaks or locating animal poachers. Existing algorithms often prioritize the location accuracy of objects of interest while other practical issues such as the reliability of object detection as a function of distance and lines of sight remain largely ignored. Additionally, in many active search scenarios, communication infrastructure may be unreliable or unestablished, making centralized control of multiple agents impractical. We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robots performing active search considering two sources of sensory information from monocular optical imagery and depth maps. By utilizing Thompson Sampling, NATS allows for decentralized coordination among multiple agents. NATS also considers object detection uncertainty from depth as well as environmental occlusions and operates while remaining agnostic of the number of objects of interest. Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search. We demonstrate the real-world viability of NATS using a pseudo-realistic environment created in the Unreal Engine 4 game development platform with the AirSim plugin., Comment: To appear at the 2021 IEEE International Conference on Robotics and Automation (ICRA), extended version

Published
2020

30. Optimal Data Detection and Signal Estimation in Systems with Input Noise

Author

Ghods, Ramina, Jeon, Charles, Maleki, Arian, and Studer, Christoph

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Practical systems often suffer from hardware impairments that already appear during signal generation. Despite the limiting effect of such input-noise impairments on signal processing systems, they are routinely ignored in the literature. In this paper, we propose an algorithm for data detection and signal estimation, referred to as Approximate Message Passing with Input noise (AMPI), which takes into account input-noise impairments. To demonstrate the efficacy of AMPI, we investigate two applications: Data detection in large multiple-input multiple output (MIMO) wireless systems and sparse signal recovery in compressive sensing. For both applications, we provide precise conditions in the large-system limit for which AMPI achieves optimal performance. We furthermore use simulations to demonstrate that AMPI achieves near-optimal performance at low complexity in realistic, finite-dimensional systems.

Published
2020
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31. Asynchronous Multi Agent Active Search

Author

Ghods, Ramina, Banerjee, Arundhati, and Schneider, Jeff

Subjects
Computer Science - Machine Learning, Computer Science - Robotics, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions, and has many applications including detecting gas leaks, radiation sources or human survivors of disasters using aerial and/or ground robots (agents). Existing active search methods are in general only amenable to a single agent, or if they extend to multi agent they require a central control system to coordinate the actions of all agents. However, such control systems are often impractical in robotics applications. In this paper, we propose two distinct active search algorithms called SPATS (Sparse Parallel Asynchronous Thompson Sampling) and LATSI (LAplace Thompson Sampling with Information gain) that allow for multiple agents to independently make data-collection decisions without a central coordinator. Throughout we consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions and its applicability in real world scenarios. Additionally, while most common search algorithms assume that agents can sense the entire environment (e.g. compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at all times, we make a realistic assumption that each agent can only sense a contiguous region of space at a time. We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms., Comment: Preprint under review

Published
2020

32. Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

Author

Gallyas-Sanhueza, Alexandra, Mirfarshbafan, Seyed Hadi, Ghods, Ramina, and Studer, Christoph

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

We propose sparsity-adaptive beamspace channel estimation algorithms that improve accuracy for 1-bit data converters in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. Our algorithms include a tuning stage based on Stein's unbiased risk estimate (SURE) that automatically selects optimal denoising parameters depending on the instantaneous channel conditions. Simulation results with line-of-sight (LoS) and non-LoS mmWave massive MIMO channel models show that our algorithms improve channel estimation accuracy with 1-bit measurements in a computationally-efficient manner., Comment: Presented at IEEE SPAWC 2020

Published
2020

37. MSE-Optimal Neural Network Initialization via Layer Fusion

Author

Ghods, Ramina, Lan, Andrew S., Goldstein, Tom, and Studer, Christoph

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. However, the use of stochastic gradient descent combined with the nonconvexity of the underlying optimization problems renders parameter learning susceptible to initialization. To address this issue, a variety of methods that rely on random parameter initialization or knowledge distillation have been proposed in the past. In this paper, we propose FuseInit, a novel method to initialize shallower networks by fusing neighboring layers of deeper networks that are trained with random initialization. We develop theoretical results and efficient algorithms for mean-square error (MSE)-optimal fusion of neighboring dense-dense, convolutional-dense, and convolutional-convolutional layers. We show experiments for a range of classification and regression datasets, which suggest that deeper neural networks are less sensitive to initialization and shallower networks can perform better (sometimes as well as their deeper counterparts) if initialized with FuseInit., Comment: Extended version of the CISS 2020 paper containing the proof for convolutional layers

Published
2020

39. Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing

Author

Tang, Larry, Ghods, Ramina, and Studer, Christoph

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Localization of wireless transmitters based on channel state information (CSI) fingerprinting finds widespread use in indoor as well as outdoor scenarios. Fingerprinting localization first builds a database containing CSI with measured location information. One then searches for the most similar CSI in this database to approximate the position of wireless transmitters. In this paper, we investigate the efficacy of locality-sensitive hashing (LSH) to reduce the complexity of the nearest neighbor-search (NNS) required by conventional fingerprinting localization systems. More specifically, we propose a low-complexity and memory efficient LSH function based on the sum-to-one (STOne) transform and use approximate hash matches. We evaluate the accuracy and complexity (in terms of the number of searches and storage requirements) of our approach for line-of-sight (LoS) and non-LoS channels, and we show that LSH enables low-complexity fingerprinting localization with comparable accuracy to methods relying on exact NNS or deep neural networks.

Published
2019

40. Beamspace Channel Estimation for Massive MIMO mmWave Systems: Algorithm and VLSI Design

Author

Mirfarshbafan, Seyed Hadi, Gallyas-Sanhueza, Alexandra, Ghods, Ramina, and Studer, Christoph

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

Millimeter-wave (mmWave) communication in combination with massive multiuser multiple-input multiple-output (MU-MIMO) enables high-bandwidth data transmission to multiple users in the same time-frequency resource. The strong path loss of wave propagation at such high frequencies necessitates accurate channel state information to ensure reliable data transmission. We propose a novel channel estimation algorithm called BEAmspace CHannel EStimation (BEACHES), which leverages the fact that wave propagation at mmWave frequencies is predominantly directional. BEACHES adaptively denoises the channel vectors in the beamspace domain using an adaptive shrinkage procedure that relies on Stein's unbiased risk estimator (SURE). Simulation results for line-of-sight (LoS) and non-LoS mmWave channels reveal that BEACHES performs on par with state-of-the-art channel estimation methods while requiring orders-of-magnitude lower complexity. To demonstrate the effectiveness of BEACHES in practice, we develop a very large-scale integration (VLSI) architecture and provide field-programmable gate array (FPGA) implementation results. Our results show that adaptive channel denoising can be performed at high throughput and in a hardware-friendly manner for massive MU-MIMO mmWave systems with hundreds of antennas., Comment: To appear in the IEEE Transactions on Circuits and Systems 1

Published
2019

41. BEACHES: Beamspace Channel Estimation for Multi-Antenna mmWave Systems and Beyond

Author

Ghods, Ramina, Gallyas-Sanhueza, Alexandra, Mirfarshbafan, Seyed Hadi, and Studer, Christoph

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Information Theory
Abstract

Massive multi-antenna millimeter wave (mmWave) and terahertz wireless systems promise high-bandwidth communication to multiple user equipments in the same time-frequency resource. The high path loss of wave propagation at such frequencies and the fine-grained nature of beamforming with massive antenna arrays necessitates accurate channel estimation to fully exploit the advantages of such systems. In this paper, we propose BEAmspace CHannel EStimation (BEACHES), a low-complexity channel estimation algorithm for multi-antenna mmWave systems and beyond. BEACHES leverages the fact that wave propagation at high frequencies is directional, which enables us to denoise the (approximately) sparse channel state information in the beamspace domain. To avoid tedious parameter selection, BEACHES includes a computationally-efficient tuning stage that provably minimizes the mean-square error of the channel estimate in the large-antenna limit. To demonstrate the efficacy of BEACHES, we provide simulation results for line-of-sight (LoS) and non-LoS mmWave channel models., Comment: Presented at SPAWC 2019

Published
2019

42. Profiling Bacillus subtilis response in the environment of Pseudomonas aeruginosa

Author

Amino, Ramina

Subjects
Microbiology, Molecular biology, Biology, Bacillus, Bacillus subtilis, microbial interactions, Pseudomonas, Pseudomonas aeruginosa
Abstract

The gram-positive rod-shaped bacterial species Bacillus subtilis exists in two morphological states: the vegetative cell and the spore. Which state B. subtilis exists is dictated by its environment. While this bacterial species is well studied, most research focuses on only one of its two morphological states at a time. Furthermore, bacteria live in diverse microbial environments that place different species in close proximity to each other. Yet, B. subtilis interactions with other microbial species is poorly understood. Using low magnification time-lapse imaging, we first characterized microbial interactions with respect to the spore entity of B. subtilis. We find that B. subtilis wild-type spores do not outgrow in the presence of P. aeruginosa wild-type cells. Next, we used high magnification time-lapse phase microscopy; we find that B. subtilis spores germinate but do not outgrow on P. aeruginosa acellular conditioned media pad. Then we investigated interspecies cellular interactions between B. subtilis wild-type vegetative cells and P. aeruginosa wild-type cells at the single cell level; we find that B. subtilis cells die in the presence of P. aeruginosa cells. This is the first study in microbial interactions to identify a molecular mechanism using P. aeruginosa acellular conditioned media to study the response of B. subtilis spores. This study demonstrates that the response of B. subtilis spores to P. aeruginosa is conserved in both the P. aeruginosa cellular condition and acellular conditioned media. We also demonstrate that B. subtilis cells die in the presence of P. aeruginosa cells; however, time did not permit for us to explore if this mechanism is dependent on the physical presence of P. aeruginosa cells or is also induced by P. aeruginosa acellular conditioned media. Mapping interactions of microbial species and deciphering the molecular mechanisms of these interactions adds depth to our understanding of microbial ecology.

Published
2024

43. Development of a Public Diplomacy Model for Kazakhstan through the Analysis of the International Political Activity of the USA and China

Author

Ramina Alipkyzy, Assel Utegenova, Klara Makasheva, Yermek Chukubayev, and Bakyt Byuzheyeva

Subjects
political war, propaganda, state body, information policy, diplomatic doctrine, Political science (General), JA1-92, Economics as a science, HB71-74
Abstract

Investigating the practices of the United States of America and China as pillars of two opposing models of diplomatic activity is useful and relevant in the light of the development of new strategies for public activity in the Republic of Kazakhstan and the implementation of best practices in the political sector of the state. The purpose of this study was to determine the best way to develop the model of public diplomacy for Kazakhstan by investigating the features and establishing the specifics of democratic and authoritarian methods of conducting international relations in the 21st century using evidence from the USA and China. The main method of scientific cognition used in this study was the method of system analysis, using which the following tasks were completed: the stages of public diplomacy of the United States and China were investigated, their specifics and orientation were determined; the effectiveness of two models of political activity was summed up, proceeding from the results of which relevant solutions for updating the diplomatic doctrine of Kazakhstan were presented. The study results included the coverage of the specifics and methods of American and Chinese diplomatic activities; summary of the strengths and weaknesses of the democratic and authoritarian model of international political activity of the two countries; finding the best mechanisms, tools, and methods for introducing them into the diplomatic practice of Kazakhstan.

Published
2023

45. Conceptual design of the AGATA 2[formula omitted] array at LNL

Author

Valiente-Dobón, J.J., Menegazzo, R., Goasduff, A., Agguiaro, D., Aguilera, P., Angelini, F., Balogh, M., Bazzacco, D., Benito, J., Benzoni, G., Bez, N., Bolognesi, M., Bottoni, S., Brugnara, D., Carollo, S., Cocconi, P., Cogo, A., Collado, J., Crespi, F.C.L., Ertoprak, A., Escudeiro, R., Galtarossa, F., Gamba, E.R., Gambalonga, A., Servín, B. Góngora, Gottardo, A., Gozzelino, A., Gulmini, M., Huang, Z., Marchi, T., Mengoni, D., Modanese, P., Napoli, D.R., Pellumaj, J., Pérez-Vidal, R.M., Pigliapoco, S., Pilotto, E., Ramina, L., Rampazzo, M., Raniero, W., Rebeschini, M., Rezynkina, K., Rosso, D., Scarcioffolo, M., Scarpa, D., Sedlák, M., Smith, R., Toniolo, N., Veronese, F., Volpe, V., Zago, L., Zanon, I., Zhang, G., Abels, R., Allegrini, M.L., Aufranc, C., Baulieu, G., Belkhiria, C., Benettoni, M., Benini, D., Bentley, M., Biasotto, M., Blaizot, M., Miquel, J. Blasco, Boiano, C., Boston, A., Boston, H., Boujrad, A., Bourgault, P., Bracco, A., Brambilla, S., Burrows, I., Camera, F., Capra, S., Capsoni, A., Cash, R., Civera, J.V., Clément, E., Coelli, S., Cordwell, M., Corradi, L., Coudert, S., De Angelis, G., De Ruvo, L., Debras, G., Del Fabbro, M., Diklić, J., Dosme, N., Duchene, G., Duclos, B., Dudouet, J., Eberth, J., Elloumi, S., Everett, C., Fantinel, S., Fillinger, M., Fioretto, E., Fransen, C., Gadea, A., Gibelin, L., González, V., Goupil, J., Görgen, C., Grant, A., Green, K., Ha, J., Hartnett, T., Henseler, K., Hess, H., Hirsch, R., Houarner, C., Jacob, J., Joannem, T., Judson, D.S., Karkour, N., Karolak, M., Kebbiri, M., Kieffer, J., Labiche, M., Lafay, X., Le Jeannic, P., Lefevre, A., Legay, E., Legruel, F., Lenzi, S., Leoni, S., Linget, D., Liptrot, M., López-Martens, A., Lotodé, A., Manara, L., Ménager, L., Mijatović, T., Million, B., Minarello, A., Montagnoli, G., Morrall, P., Mullacrane, I., Nyberg, J., Philippon, G., Polettini, M., Popieul, F., Pullia, A., Recchia, F., Reiter, P., Richardt, G., Rocchini, M., Roger, A., Saillant, F., Sanchis, E., Laskar, Md.S.R., Secci, G., Sigward, M.-H., Simpson, J., Solenne, N., Spee, F., Stefanini, A.M., Stézowski, O., Szilner, S., Templeton, N., Theisen, Ch., Thiel, S., Tomasi, F., Tzvetkov, S., Vigano, D., Viscione, E., Wieland, O., Wimmer, K., Wittwer, G., and Zielińska, M.

Published
2023
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46. AGATA: mechanics and infrastructures

Author

Smith, R., Menegazzo, R., Aufranc, C., Bez, N., Burrows, I., Cahoreau, M., Debras, G., Gibelin, L., Goasduff, A., Grant, A., Joannem, T., Karkour, N., Karolak, M., Kieffer, J., Lotodé, A., Million, B., Morrall, P. S., Ramina, L., Rampazzo, M., Roger, A., Simpson, J., Solenne, N., Stézowski, O., Tzvetkov, S., Zago, L., and Zielińska, M.

Published
2023
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48. Optimal Data Detection in Large MIMO

Author

Jeon, Charles, Ghods, Ramina, Maleki, Arian, and Studer, Christoph

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

Large multiple-input multiple-output (MIMO) appears in massive multi-user MIMO and randomly-spread code-division multiple access (CDMA)-based wireless systems. In order to cope with the excessively high complexity of optimal data detection in such systems, a variety of efficient yet sub-optimal algorithms have been proposed in the past. In this paper, we propose a data detection algorithm that is computationally efficient and optimal in a sense that it is able to achieve the same error-rate performance as the individually optimal (IO) data detector under certain assumptions on the MIMO system matrix and constellation alphabet. Our algorithm, which we refer to as LAMA (short for large MIMO AMP), builds on complex-valued Bayesian approximate message passing (AMP), which enables an exact analytical characterization of the performance and complexity in the large-system limit via the state-evolution framework. We derive optimality conditions for LAMA and investigate performance/complexity trade-offs. As a byproduct of our analysis, we recover classical results of IO data detection for randomly-spread CDMA. We furthermore provide practical ways for LAMA to approach the theoretical performance limits in realistic, finite-dimensional systems at low computational complexity.

Published
2018

49. Linear Spectral Estimators and an Application to Phase Retrieval

Author

Ghods, Ramina, Lan, Andrew S., Goldstein, Tom, and Studer, Christoph

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements. The best-known algorithms for this problem are iterative in nature and rely on so-called spectral initializers that provide accurate initialization vectors. We propose a novel class of estimators suitable for general nonlinear measurement systems, called linear spectral estimators (LSPEs), which can be used to compute accurate initialization vectors for phase retrieval problems. The proposed LSPEs not only provide accurate initialization vectors for noisy phase retrieval systems with structured or random measurement matrices, but also enable the derivation of sharp and nonasymptotic mean-squared error bounds. We demonstrate the efficacy of LSPEs on synthetic and real-world phase retrieval problems, and show that our estimators significantly outperform existing methods for structured measurement systems that arise in practice., Comment: To appear at ICML 2018, extended version with supplementary material

Published
2018

50. PhaseLin: Linear Phase Retrieval

Author

Ghods, Ramina, Lan, Andrew S., Goldstein, Tom, and Studer, Christoph

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

Phase retrieval deals with the recovery of complex- or real-valued signals from magnitude measurements. As shown recently, the method PhaseMax enables phase retrieval via convex optimization and without lifting the problem to a higher dimension. To succeed, PhaseMax requires an initial guess of the solution, which can be calculated via spectral initializers. In this paper, we show that with the availability of an initial guess, phase retrieval can be carried out with an ever simpler, linear procedure. Our algorithm, called PhaseLin, is the linear estimator that minimizes the mean squared error (MSE) when applied to the magnitude measurements. The linear nature of PhaseLin enables an exact and nonasymptotic MSE analysis for arbitrary measurement matrices. We furthermore demonstrate that by iteratively using PhaseLin, one arrives at an efficient phase retrieval algorithm that performs on par with existing convex and nonconvex methods on synthetic and real-world data., Comment: To be presented at CISS 2018 (http://ee-ciss.princeton.edu/)

Published
2018

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