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396 results on '"Gil, Thierry"'

1. Oscillatory Neural Network as Hetero-Associative Memory for Image Edge Detection

Author

Abernot, Madeleine, Gil, Thierry, and Todri-Sanial, Aida

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical image processing methods performed at the edge, such as feature extraction or edge detection, use convolutional filters that are energy, computation, and memory hungry algorithms. But edge devices and cameras have scarce computational resources, bandwidth, and power and are limited due to privacy constraints to send data over to the cloud. Thus, there is a need to process image data at the edge. Over the years, this need has incited a lot of interest in implementing neuromorphic computing at the edge. Neuromorphic systems aim to emulate the biological neural functions to achieve energy-efficient computing. Recently, Oscillatory Neural Networks (ONN) present a novel brain-inspired computing approach by emulating brain oscillations to perform autoassociative memory types of applications. To speed up image edge detection and reduce its power consumption, we perform an in-depth investigation with ONNs. We propose a novel image processing method by using ONNs as a hetero-associative memory (HAM) for image edge detection. We simulate our ONN-HAM solution using first, a Matlab emulator, and then a fully digital ONN design. We show results on gray scale square evaluation maps, also on black and white and gray scale 28x28 MNIST images and finally on black and white 512x512 standard test images. We compare our solution with standard edge detection filters such as Sobel and Canny. Finally, using the fully digital design simulation results, we report on timing and resource characteristics, and evaluate its feasibility for real-time image processing applications. Our digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz system frequency) respecting real-time camera constraints. This work is the first to explore ONNs as hetero-associative memory for image processing applications.

Published
2022

2. Energy Autonomous Wearable Sensors for Smart Healthcare: A Review

Author

Dahiya, Abhishek Singh, Thireau, Jerome, Boudaden, Jamila, Lal, Swatchith, Gulzar, Umair, Zhang, Yan, Gil, Thierry, Azemard, Nadine, Ramm, Peter, Kiessling, Tim, O'Murchu, Cian, Sebelius, Fredrik, Tilly, Jonas, Glynn, Colm, Geary, Shane, O'Dwyer, Colm, Razeeb, Kafil, Lacampagne, Alain, Charlot, Benoit, and Todri-Sanial, Aida

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Human-Computer Interaction
Abstract

Energy Autonomous Wearable Sensors (EAWS) have attracted a large interest due to their potential to provide reliable measurements and continuous bioelectric signals, which help to reduce health risk factors early on, ongoing assessment for disease prevention, and maintaining optimum, lifelong health quality. This review paper presents recent developments and state-of-the-art research related to three critical elements that enable an EAWS. The first element is wearable sensors, which monitor human body physiological signals and activities. Emphasis is given on explaining different types of transduction mechanisms presented, and emerging materials and fabrication techniques. The second element is the flexible and wearable energy storage device to drive low-power electronics and the software needed for automatic detection of unstable physiological parameters. The third is the flexible and stretchable energy harvesting module to recharge batteries for continuous operation of wearable sensors. We conclude by discussing some of the technical challenges in realizing energy-autonomous wearable sensing technologies and possible solutions for overcoming them.

Published
2019

5. Niraparib in patients with metastatic castration-resistant prostate cancer and DNA repair gene defects (GALAHAD): a multicentre, open-label, phase 2 trial

Author

Parnis, Francis, Joshua, Anthony M., Horvath, Lisa G., Steer, Christopher, Marx, Gavin, Sandhu, Shahneen, Gurney, Howard, Ferguson, Thomas, Van Bruwaene, Siska, Luyten, Daisy, Schatteman, Peter, Lumen, Nicolaas, Dirix, Luc, Goeminne, Jean-Charles, Gil, Thierry, Seront, Emmanuel, Vulsteke, Christof, Kussumoto, Celio, Franke, Fabio A., Martinelli de Oliveira, Fabricio Augusto, Pereira de Santana Gomes, Andrea Juliana, Pinczowski, Hélio, Preto, Daniel D'Almeida, Zucca, Luis Eduardo, Borges, Giuliano Santos, Murad, Andre M., Saad, Fred, Chi, Kim N., Fradet, Yves, Fleshner, Neil E., Emmenegger, Urban, Brasso, Klaus, Fizazi, Karim, Culine, Stephane, Thiery-Vuillemin, Antoine, Joly, Florence, Fléchon, Aude, Hilgers, Werner, Eymard, Jean-Christophe, Borchiellini, Delphine, Barthélémy, Philippe, Berger, Raanan, Leibowitz-Amit, Raya, Mermershtain, Wilmosh, Rouvinov, Keren, Peer, Avivit, Kovel, Svetlana, Sella, Avishay, Lolkema, Martijn P., van den Eertwegh, Alfonsus J.M., Voortman, Johannes, Aarts, Maureen J., Gietema, Jourik A., Kim, Choung-Soo, Choi, Young-Deuk, Chung, Byung-Ha, Gafanov, Rustem A., Kopyltsov, Evgeniy, Usynin, Evgeny A., Carles, Joan, Mellado, Begoña, Maroto, José Pablo, García-Donás, Jesús, Rodríguez Moreno, Juan Francisco, Durán, Ignacio, Pérez-Valderrama, Begoña, Castro, Elena, Olmos, David, Méndez-Vidal, María José, Estellés, David Lorente, Sarrió, Regina Gironés, Muñoz-Langa, José, Herranz, Urbano Anido, Puente Vázquez, Javier, Castellanos, Enrique, Hellström, Martin, Widmark, Anders, Lissbrant, Ingela Franck, Jellvert, Åsa, Külich, Cecilia, Blom, René, Ståhl, Olof, Chiang, Po-Hui, Kang, Chih-Hsiung, Ou, Yen-Chuan, Wang, Shian-Shiang, Wu, Hsi-Chin, Lu, Yu-Chuan, Attard, Gerhardt, Khoo, Vincent, Bahl, Amit, Kellati, Prasad, Parikh, Omi, Srinivasan, Rajaguru, Lester, Jason F., Staffurth, John N., Cheng, Heather H., Efstathiou, Eleni, Pilié, Patrick G., George, Daniel J., Karsh, Lawrence I., Kelly, W. Kevin, Danila, Daniel C., Sieber, Paul R., Smith, Matthew R., Heath, Elisabeth I., Vaishampayan, Ulka N., Flaig, Thomas W., Emamekhoo, Hamid, Pinski, Jacek K., Kalebasty, Arash Rezazadeh, Maly, Joseph J., Moon, Helen, Smith, Matthew R, Scher, Howard I, Lara, Primo N, Jr, Yu, Evan Y, George, Daniel J, Chi, Kim N, Danila, Daniel C, Mason, Gary E, Espina, Byron M, Zhao, Xin, Urtishak, Karen A, Francis, Peter, and Lopez-Gitlitz, Angela

Published
2022
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6. Durvalumab alone and durvalumab plus tremelimumab versus chemotherapy in previously untreated patients with unresectable, locally advanced or metastatic urothelial carcinoma (DANUBE): a randomised, open-label, multicentre, phase 3 trial

Author

Park, Se Hoon, van der Heijden, Michiel S., Necchi, Andrea, Castellano, Daniel, Bamias, Aristotelis, Lee, Jae Lyun, De Giorgi, Ugo, Bögemann, Martin, Eigl, Bernhard J., Tsiatas, Marinos, Powles, Thomas, Novikov, Andrey, Skoneczna, Iwona, Mukherjee, Som D., Suarez, Cristina, Westgeest, Hans, Fradet, Yves, Flechon, Aude, Ou, Yen-Chuan, Park, Inkeun, Matveev, Vsevolod, Pérez-Valderrama, Begoña, Cheng, Susanna, Frank, Stephen, Gurney, Howard, Anido, Urbano, Hamzaj, Alketa, Retz, Margitta, Sridhar, Srikala, Scagliotti, Giorgio Vittorio, Voortman, Jens, Alekseev, Boris, Alyasova, Anna, Komyakov, Boris, Dumez, Herlinde, Pavic, Michel, Kimura, Go, Mizokami, Atsushi, Osanto, Susanne, Arranz, Jose Angel, Piersma, Djura, Shin, Sang Joon, Karyakin, Oleg, Delgado, Ignacio, Gonzalez, Jose Luis, Pang, See-Tong, Tran, Anna, Lipatov, Oleg, Su, Wen-Pin, Flaig, Thomas, Alva, Ajjai, Park Kyong, Hwa, Kopyltsov, Evgeny, Almagro, Elena, Domenech, Monserrat, Chang, Yen-Hwa, Sautois, Brieuc, Ravaux, Andre, Aravantinos, Gerasimos, Georgoulias, Vasileios, Mulder, Sasja, Kim, Yu Jung, Kater, Fabio, Chevreau, Christine, Tagawa, Scott, Zalewski, Pawel, Joly, Florence, Loriot, Yohann, Hatiboglu, Gencay, Gianni, Luca, Morelli, Franco, Tambaro, Rosa, Hashimoto, Yasuhiro, Nosov, Alexander, Font, Albert, Rodriguez-Vida, Alejo M., Jones, Robert, Vasudev, Naveen, Srinivas, Sandhya, Zhang, Jingsong, Gil, Thierry, Finch, Daygen, Grimm, Marc-Oliver, Su, Yu-Li, Chowdhury, Simon, Hocking, Christopher, Plas, Eugen, North, Scott, Jensen, Niels Viggo, Theodore, Christine, Imkamp, Florian, Peer, Avivit, Kobayashi, Takashi, Sakai, Hideki, Sassa, Naoto, Yoshimura, Kazuhiro, Aarts, Maureen, Ferreira Castro, Ana, Topuzov, Marlen, Rodriguez, Juan Francisco, Vazquez, Federico Jose, Tsai, Yu-Chieh, Crabb, Simon, Hussain, Syed, Bendell, Johanna, Gross-Goupil, Marine, Gwenaelle, Gravis, Berger, Raanan, Statsenko, Galina, Evans, Linda, Drakaki, Alexandra, Somer, Bradley, Davis, Ian, Lynam, James, Borges, Giuliano, Dettino, Aldo, Fay, André P., Martins, Graziella, Zucca, Luis Eduardo, Agerbaek, Mads, Kalofonos, Haralambos, Rosenbaum, Eli, Enokida, Hideki, Kikukawa, Hiroaki, Nishimura, Kazuo, Tamada, Satoshi, Uemura, Motohide, Lopez, Yamil, Gietema, Jourik, Slojewski, Marcin, Fernandes, Isabel, Smolin, Alexey, Mazhar, Danish, Kalebasty, Arash Rezazadeh, Carthon, Bradley, Loidl, Wolfgang, Franke, Fabio, Girotto, Gustavo, Alimohamed, Nimira, Macfarlane, Robyn, Pappot, Helle, Niegisch, Guenter, Mavroudis, Dimitrios, Sella, Avishay, Porta, Camillo, Ebara, Shin, Nakamura, Motonobu, Obara, Wataru, Okuno, Norihiko, Shinohara, Nobuo, Sugimoto, Mikio, Suzuki, Akitaka, Tokuda, Noriaki, Uemura, Hiroji, Yamaguchi, Akito, Ramirez, Francisco, Rozanowski, Pawel, Wiechno, Pawel, Keam, Bhumsuk, Kislov, Nikolay, Plaksin, Denis, Cicin, Irfan, Kumar, Satish, Galsky, Matthew D., Petrylak, Daniel P., Rosales, Joseph, Vaishampayan, Ulka, Culine, Stephane, Papandreou, Christos, Nara, Taketoshi, Erman, Mustafa, Kreiger, Laurence, Janoski, Juliana, Rosa, Diogo, Siqueira, Mariana, Canil, Christina, Sengelov, Lisa, Tourani, Jean-Marc, Arai, Gaku, Hashine, Katsuyoshi, Kawakita, Mutsushi, Nakaigawa, Noboru, Nomi, Hayahito, Shiina, Hiroaki, Suzuki, Hiroyoshi, Yonese, Junji, Kuri, Roberto, Macedo, Eleazar, Rivera, Samuel, Villalobos Prieto, Alberto, Polakiewicz-Gilowska, Anna, Zaucha, Renata, Lopes, Fabio, Ponomarev, Roman, Pomerantz, Mark, Shariat, Shahrokh, Luk, Cynthia, Lesniewski-Kmak, Krzysztof, van der Heijden, Michiel S, Galsky, Matthew D, Petrylak, Daniel P, Ogawa, Osamu, Lee, Jae-Lyun, Eigl, Bernhard J, Mukherjee, Som D, Suárez, Cristina, Fay, André P, Duran, Ignacio, Wildsmith, Sophie, He, Philip, Angra, Natasha, Gupta, Ashok K, Levin, Wendy, and Bellmunt, Joaquim

Published
2020
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7. Avelumab as neoadjuvant therapy in patients with urothelial non-metastatic muscle invasive bladder cancer: a multicenter, randomized, non-comparative, phase II study (Oncodistinct 004 - AURA trial)

Author

Martinez Chanza, Nieves, Soukane, Louisa, Barthelemy, Philippe, Carnot, Aurélien, Gil, Thierry, Casert, Vinciane, Vanhaudenarde, Vincent, Sautois, Brieuc, Staudacher, Lionel, Van den Brande, Jan, Culine, Stephane, Seront, Emmanuel, Gizzi, Marco, Albisinni, Simone, Tricard, Thibault, Fantoni, Jean Christophe, Paesmans, Marianne, Caparica, Rafael, Roumeguere, Thierry, and Awada, Ahmad

Published
2021
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11. Which Factors Predict Overall Survival in Patients With Metastatic Castration-Resistant Prostate Cancer Treated With Abiraterone Acetate Post-Docetaxel?

Author

Van Praet, Charles, Rottey, Sylvie, Van Hende, Fransien, Pelgrims, Gino, Demey, Wim, Van Aelst, Filip, Wynendaele, Wim, Gil, Thierry, Schatteman, Peter, Filleul, Bertrand, Schallier, Denis, Machiels, Jean-Pascal, Schrijvers, Dirk, Everaert, Els, D'Hondt, Lionel, Werbrouck, Patrick, Vermeij, Joanna, Mebis, Jeroen, Clausse, Marylene, Rasschaert, Marika, Van Erps, Joanna, Verheezen, Jolanda, Van Haverbeke, Jan, Goeminne, Jean-Charles, and Lumen, Nicolaas

Published
2017
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12. A Mixed-Signal Oscillatory Neural Network for Scalable Analog Computations in Phase Domain

Author

Delacour, Corentin, Carapezzi, Stefania, Boschetto, Gabriele, Abernot, Madeleine, Gil, Thierry, Todri-Sanial, Aida, Delacour, Corentin, Carapezzi, Stefania, Boschetto, Gabriele, Abernot, Madeleine, Gil, Thierry, and Todri-Sanial, Aida

Abstract

Digital electronics based on von Neumann's architecture are reaching their limits to solve large scale problems essentially due to the memory fetching. Instead, recent efforts to bring the memory near the computation have enabled highly parallel computations at low energy cost. Oscillatory Neural Network (ONN) is one example of in-memory analog computing paradigm consisting of coupled oscillating neurons. When implemented in hardware, ONNs naturally perform gradient descent of an energy landscape that makes them particularly suited for solving optimization problems. Although the ONN computational capability and its link with the Ising model are known for decades, implementing a large-scale ONN remains difficult. Beyond the oscillators' variations, there are still design challenges such as having compact, programmable synapses and a modular architecture for solving large problem instances. In this paper, we propose a mixed-signal architecture named Saturated Kuramoto ONN (SKONN) that leverages both analog and digital domains for efficient ONN hardware implementation. SKONN computes in the analog phase domain while propagating the information digitally to facilitate scaling up the ONN size. SKONN's separation between computation and propagation enhances the robustness and enables a feed-forward phase propagation that is showcased for the first time. Moreover, the SKONN architecture leads to unique binarizing dynamics that are particularly suitable for solving NP-hard combinatorial optimization problems such as finding the Weighted Max-cut of a graph. We find that SKONN's accuracy is as good as the Goemans-Williamson 0.878-approximation algorithm for Max-cut; whereas SKONN's computation time only grows logarithmically. We report on Weighted Max-cut experiments using a 9-neuron SKONN proof-of-concept on PCB. Finally, we present a low-power 16-neuron SKONN integrated circuit and illustrate SKONN's feed-forward ability while computing the XOR function.

Published
2023

16. Efficacy and safety of abiraterone acetate plus prednisone and androgen deprivation therapy +/- docetaxel in older patients (≥70 years), with de novo metastatic-castration sensitive prostate cancer, compared to younger patients (<70 years): The PEACE-1 trial.

Author

Mourey, Loic, primary, Boyle, Helen Jane, additional, Roubaud, Guilhem, additional, McDermott, Raymond S., additional, Supiot, Stephane, additional, Tombal, Bertrand F., additional, Flechon, Aude, additional, Berthold, Dominik R., additional, Ronchin, Philippe, additional, Kacso, Gabriel, additional, Berdah, Jean Francois, additional, Calabro, Fabio, additional, Gravis, Gwenaelle, additional, Palumbo, Samuel, additional, Gil, Thierry, additional, Vie, Brigitte, additional, Ribault, Hélène, additional, Fizazi, Karim, additional, Foulon, Stéphanie, additional, and Carles, Joan, additional

Published
2023
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17. ONN-Based On-chip Learning for Obstacle Avoidance on Mobile Robot

Author

Abernot, Madeleine, Gil, Thierry, Todri-Sanial, Aida, Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Eindhoven University of Technology [Eindhoven] (TU/e), and European Project: 871501,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),H2020-ICT-2019-2,NeurONN(2020)

Subjects
[SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; The amount of data available to process at the edge has increased drastically in the last decades with the emergence of edge devices in numerous domains. Moreover, the increasing amount of data requires powerful artificial intelligence (AI) algorithms, such as deep learning algorithms to process the data, and they are very power-hungry. Thus, there is a lot of ongoing research on how to train and compute on edge devices. Current hardware architectures based on von Neumann are not adapted for AI algorithms due to the processing to memory transfer bottleneck [1]. Recently, neuromorphic computing, which proposes brain-inspired hardware-based paradigms, has emerged as a suitable solution to edge computing, removing the von Neumann bottleneck [1].Oscillatory Neural Network (ONN) is a promising neuromorphic computing paradigm for AI at the edge. ONNs [2,3,4] are networks of coupled oscillators using the natural synchronization behavior of oscillators to compute. Information is encoded in the phase relationship among oscillators to reduce the voltage amplitude and limit power consumption [5]. In state-of-the-art, ONN is configured with a fully- connected recurrent architecture, and trained with unsupervised learning to solve auto-associative memory tasks, like pattern recognition. In the framework of the EU H2020 NEURONN [6] project, we explore ONN architectures, learning algorithms, and applications to showcase ONN advantages for edge computing. We developed a fully-connected recurrent ONN in digital to implement it on FPGA to explore ONN edge applications and on-chip learning capabilities [7]. For example, using the ONN auto- associative memory properties, we demonstrated it can efficiently solve real-time obstacle avoidance application on mobile robots equipped with proximity sensors [8,9]. In this case, we cascade two ONNs, one trained to detect obstacles and the second trained to define the novel robot direction. Later, we updated the system with an all-in-one architecture based on a Xilinx Zynq processor to have an ONN- based System-on-Chip (SoC) capable of reading, conditioning, and treating sensory data [9]. In parallel, we also explore ONN on-chip learning capabilities with the digital design. Recently, we proposed a novel architecture, also based on the Zynq processor, to allow ONN on-chip learning for auto- associative memory tasks [10].In this work, we combine both the obstacle avoidance application with the on-chip learning capability. We propose an architecture that incorporates the on-chip learning in the ONN-based SoC architecture configured for robot obstacle avoidance, see Fig. 1. In particular, we propose to have a pre-trained ONN to detect obstacles, and a non-trained or half-trained ONN to define direction. The training of the second ONN is done by using an additional post-processing from the first ONN output to define a direction. This computed direction is then compared with the direction given by the second ONN output. A difference between the two activates the training of a novel pattern. After numerous cycles without any difference, we consider the second ONN trained, and we remove the post-processing algorithm. Results from [9] and [10] are encouraging to perform real-time on-chip learning with ONN for mobile robot obstacle avoidance application.

Published
2023

20. Réseaux de neurones oscillants pour des calculs économes en énergie

Author

Abernot, Madeleine, Boschetto, Gabriele, Carapezzi, Stefania, Delacour, Corentin, Gil, Thierry, Todri-Sanial, Aida, Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Eindhoven University of Technology [Eindhoven] (TU/e), and European Project: 871501,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),H2020-ICT-2019-2,NeurONN(2020)

Subjects
Réseau de neurones oscillatoires, mémoire associative, associative memory, intelligence artificielle embarquée, edge computing, économe en énergie, résolution de problèmes d'optimisation, energy efficient, solving optimisation problems, oscillatory Neural network, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

National audience; Artificial intelligence allows us to solve increasingly complex problems, but algorithms require a lot of computational resources that consume a lot of power, especially during the learning phase to train neural networks with a large amount of data. Today, the need to process the data where they are generated requires low power architectures.This article presents a novel neural network based on oscillatory neural networks (ONN) which are inspired by the oscillations observed in the brain. ONNs present an alternative low power computing paradigm that enables online learning and inference on edge IoT devices.; L’intelligence artificielle permet aujourd’hui de résoudre des problèmes de plus en plus complexes mais les algorithmes utilisés nécessitent des ressources de calcul qui consomment beaucoup d’énergie notamment pendant la phase d’apprentissage pour entrainer des réseaux de neurones avec une quantité importante de données. Aujourd’hui, la nécessité de traiter les données là où elles sont générées nécessite des architectures à faible consommation d'énergie.Cet article présente un nouveau réseau de neurones basé sur des neurones oscillatoires (ONN pour Oscillatory Neural Network) qui s’inspirent des oscillations observées dans le cerveau. Les ONN présentent une alternative de calcul à faible consommation pour permettre l'apprentissage et l'inférence en ligne sur les dispositifs IoT (Internet of Things).

Published
2022

21. Efficacy of Cabozantinib in Metastatic MiT Family Translocation Renal Cell Carcinomas

Author

Thouvenin, Jonathan, primary, Alhalabi, Omar, additional, Carlo, Maria, additional, Carril-Ajuria, Lucia, additional, Hirsch, Laure, additional, Martinez-Chanza, Nieves, additional, Négrier, Sylvie, additional, Campedel, Luca, additional, Martini, Dylan, additional, Borchiellini, Delphine, additional, Chahoud, Jad, additional, Lodi, Massimo, additional, Barthélémy, Philippe, additional, Hasanov, Elshad, additional, Hahn, Andrew W, additional, Gil, Thierry, additional, Viswanathan, Srinivas R, additional, Bakouny, Ziad, additional, Msaouel, Pavlos, additional, Asim Bilen, Mehmet, additional, Choueiri, Toni K, additional, Albiges, Laurence, additional, Tannir, Nizar M, additional, and Malouf, Gabriel G, additional

Published
2022
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24. The Perplexus Programming Framework: Combining Bio-inspiration and Agent-Oriented Programming for the Simulation of Large Scale Complex Systems

Author

Brousse, Olivier, Sassatelli, Gilles, Gil, Thierry, Robert, Michel, Grize, François, Sanchez, Eduardo, Upegui, Andrés, Thoma, Yann, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hornby, Gregory S., editor, Sekanina, Lukáš, editor, and Haddow, Pauline C., editor

Published
2008
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25. Run-Time Resources Management on Coarse Grained, Packet-Switching Reconfigurable Architecture: A Case Study Through the APACHES’ Platform

Author

Ngouanga, Alex, Sassatelli, Gilles, Torres, Lionel, Gil, Thierry, Suarez, André Borin, Susin, Altamiro Amadeu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bertels, Koen, editor, Cardoso, João M. P., editor, and Vassiliadis, Stamatis, editor

Published
2006
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26. How Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase

Author

Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo, European Union (UE). H2020, Todri Sanial, Aida, Carapezzi, Stefania, Delacour, Corentin, Abernot, Madeleine, Gil, Thierry, Corti, Elisabetta, Karg, Siegfried F., Núñez Martínez, Juan, Jiménez, Manuel, Avedillo de Juan, María José, Linares Barranco, Bernabé, Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo, European Union (UE). H2020, Todri Sanial, Aida, Carapezzi, Stefania, Delacour, Corentin, Abernot, Madeleine, Gil, Thierry, Corti, Elisabetta, Karg, Siegfried F., Núñez Martínez, Juan, Jiménez, Manuel, Avedillo de Juan, María José, and Linares Barranco, Bernabé

Abstract

Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human brain and are suitable for solving large and complex associative problems. In this work, we investigate the dynamics of coupled oscillators to implement such ONNs. By harnessing the complex dynamics of coupled oscillatory systems, we forge a novel computation model—information is encoded in the phase of oscillations. Coupled interconnected oscillators can exhibit various behaviors due to the strength of the coupling. In this article, we present a novel method based on subharmonic injection locking (SHIL) for controlling the oscillatory states of coupled oscillators that allow them to lock in frequency with distinct phase differences. Circuit-level simulation results indicate SHIL effectiveness and its applicability to large-scale oscillatory networks for pattern recognition.

Published
2022

27. How frequency injection locking can train oscillatory neural networks to compute in phase

Author

European Commission, Todri-Sanial, Aída, Carapezzi, Stefania, Delacour, Corentin, Abernot, Madeleine, Gil, Thierry, Corti, Elisabetta, Karg, Siegfried, Núñez, Juan, Jiménez Través, Manuel, Avedillo, María J., Linares-Barranco, Bernabé, European Commission, Todri-Sanial, Aída, Carapezzi, Stefania, Delacour, Corentin, Abernot, Madeleine, Gil, Thierry, Corti, Elisabetta, Karg, Siegfried, Núñez, Juan, Jiménez Través, Manuel, Avedillo, María J., and Linares-Barranco, Bernabé

Abstract

Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human brain and are suitable for solving large and complex associative problems. In this work, we investigate the dynamics of coupled oscillators to implement such ONNs. By harnessing the complex dynamics of coupled oscillatory systems, we forge a novel computation model—information is encoded in the phase of oscillations. Coupled interconnected oscillators can exhibit various behaviors due to the strength of the coupling. In this article, we present a novel method based on subharmonic injection locking (SHIL) for controlling the oscillatory states of coupled oscillators that allow them to lock in frequency with distinct phase differences. Circuit-level simulation results indicate SHIL effectiveness and its applicability to large-scale oscillatory networks for pattern recognition.

Published
2022

28. Avelumab as the basis of neoadjuvant regimen in platinum-eligible and -ineligible patients with nonmetastatic muscle-invasive bladder cancer: AURA (Oncodistinct-004) trial.

Author

Martinez Chanza, Nieves, primary, Carnot, Aurelien, additional, Barthélémy, Philippe, additional, Casert, Vinciane, additional, Staudacher, Lionel, additional, Van Den Brande, Jan, additional, Sautois, Brieuc, additional, Vanhaudenarde, Vincent, additional, Seront, Emmanuel, additional, Debien, Veronique, additional, Gil, Thierry, additional, Paesmans, Marianne, additional, Kotecki, Nuria, additional, Ignatiadis, Michail, additional, Albisinni, Simone, additional, Fantoni, Jean Christophe, additional, Tricard, Thibault, additional, Diamond, Romain, additional, Roumeguere, Thierry Andre, additional, and Awada, Ahmad, additional

Published
2022
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31. Aflibercept versus placebo in combination with docetaxel and prednisone for treatment of men with metastatic castration-resistant prostate cancer (VENICE): a phase 3, double-blind randomised trial

Author

Tannock, Ian F, Fizazi, Karim, Ivanov, Sergey, Karlsson, Camilla Thellenberg, Fléchon, Aude, Skoneczna, Iwona, Orlandi, Francisco, Gravis, Gwenaelle, Matveev, Vsevolod, Bavbek, Sevil, Gil, Thierry, Viana, Luciano, Arén, Osvaldo, Karyakin, Oleg, Elliott, Tony, Birtle, Alison, Magherini, Emmanuelle, Hatteville, Laurence, Petrylak, Daniel, Tombal, Bertrand, and Rosenthal, Mark

Published
2013
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35. Niraparib in patients with metastatic castration-resistant prostate cancer and DNA repair gene defects (GALAHAD): a multicentre, open-label, phase 2 trial

Author

Smith, Matthew R, primary, Scher, Howard I, additional, Sandhu, Shahneen, additional, Efstathiou, Eleni, additional, Lara, Primo N, additional, Yu, Evan Y, additional, George, Daniel J, additional, Chi, Kim N, additional, Saad, Fred, additional, Ståhl, Olof, additional, Olmos, David, additional, Danila, Daniel C, additional, Mason, Gary E, additional, Espina, Byron M, additional, Zhao, Xin, additional, Urtishak, Karen A, additional, Francis, Peter, additional, Lopez-Gitlitz, Angela, additional, Fizazi, Karim, additional, Parnis, Francis, additional, Joshua, Anthony M., additional, Horvath, Lisa G., additional, Steer, Christopher, additional, Marx, Gavin, additional, Gurney, Howard, additional, Ferguson, Thomas, additional, Van Bruwaene, Siska, additional, Luyten, Daisy, additional, Schatteman, Peter, additional, Lumen, Nicolaas, additional, Dirix, Luc, additional, Goeminne, Jean-Charles, additional, Gil, Thierry, additional, Seront, Emmanuel, additional, Vulsteke, Christof, additional, Kussumoto, Celio, additional, Franke, Fabio A., additional, Martinelli de Oliveira, Fabricio Augusto, additional, Pereira de Santana Gomes, Andrea Juliana, additional, Pinczowski, Hélio, additional, Preto, Daniel D'Almeida, additional, Zucca, Luis Eduardo, additional, Borges, Giuliano Santos, additional, Murad, Andre M., additional, Chi, Kim N., additional, Fradet, Yves, additional, Fleshner, Neil E., additional, Emmenegger, Urban, additional, Brasso, Klaus, additional, Culine, Stephane, additional, Thiery-Vuillemin, Antoine, additional, Joly, Florence, additional, Fléchon, Aude, additional, Hilgers, Werner, additional, Eymard, Jean-Christophe, additional, Borchiellini, Delphine, additional, Barthélémy, Philippe, additional, Berger, Raanan, additional, Leibowitz-Amit, Raya, additional, Mermershtain, Wilmosh, additional, Rouvinov, Keren, additional, Peer, Avivit, additional, Kovel, Svetlana, additional, Sella, Avishay, additional, Lolkema, Martijn P., additional, van den Eertwegh, Alfonsus J.M., additional, Voortman, Johannes, additional, Aarts, Maureen J., additional, Gietema, Jourik A., additional, Kim, Choung-Soo, additional, Choi, Young-Deuk, additional, Chung, Byung-Ha, additional, Gafanov, Rustem A., additional, Kopyltsov, Evgeniy, additional, Usynin, Evgeny A., additional, Carles, Joan, additional, Mellado, Begoña, additional, Maroto, José Pablo, additional, García-Donás, Jesús, additional, Rodríguez Moreno, Juan Francisco, additional, Durán, Ignacio, additional, Pérez-Valderrama, Begoña, additional, Castro, Elena, additional, Méndez-Vidal, María José, additional, Estellés, David Lorente, additional, Sarrió, Regina Gironés, additional, Muñoz-Langa, José, additional, Herranz, Urbano Anido, additional, Puente Vázquez, Javier, additional, Castellanos, Enrique, additional, Hellström, Martin, additional, Widmark, Anders, additional, Lissbrant, Ingela Franck, additional, Jellvert, Åsa, additional, Külich, Cecilia, additional, Blom, René, additional, Chiang, Po-Hui, additional, Kang, Chih-Hsiung, additional, Ou, Yen-Chuan, additional, Wang, Shian-Shiang, additional, Wu, Hsi-Chin, additional, Lu, Yu-Chuan, additional, Attard, Gerhardt, additional, Khoo, Vincent, additional, Bahl, Amit, additional, Kellati, Prasad, additional, Parikh, Omi, additional, Srinivasan, Rajaguru, additional, Lester, Jason F., additional, Staffurth, John N., additional, Cheng, Heather H., additional, Pilié, Patrick G., additional, George, Daniel J., additional, Karsh, Lawrence I., additional, Kelly, W. Kevin, additional, Danila, Daniel C., additional, Sieber, Paul R., additional, Smith, Matthew R., additional, Heath, Elisabeth I., additional, Vaishampayan, Ulka N., additional, Flaig, Thomas W., additional, Emamekhoo, Hamid, additional, Pinski, Jacek K., additional, Kalebasty, Arash Rezazadeh, additional, Maly, Joseph J., additional, and Moon, Helen, additional

Published
2022
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36. A Rare Case of Hepatic Vanishing Bile Duct Syndrome Occurring after Combination Therapy with Nivolumab and Cabozantinib in a Patient with Renal Carcinoma

Author

Gourari, Karim, primary, Catherine, Julien, additional, Garaud, Soizic, additional, Kerger, Joseph, additional, Lepida, Antonia, additional, Georgala, Aspasia, additional, Lebrun, Fabienne, additional, Gomez Galdon, Maria, additional, Gil, Thierry, additional, Willard-Gallo, Karen, additional, and Langouo Fontsa, Mireille, additional

Published
2022
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37. Mobile Robot Obstacle Avoidance with Oscillatory Neural Networks on FPGA

Author

Abernot, Madeleine, Gil, Thierry, Delacour, Corentin, Boschetto, Gabriele, Carapezzi, Stefania, Azemard, Nadine, Salles, Jérémie, Gonos, Théophile, Todri-Sanial, Aida, Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and A.I.Mergence [Paris]

Subjects
[SPI]Engineering Sciences [physics], [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, ComputingMilieux_MISCELLANEOUS, [SPI.TRON]Engineering Sciences [physics]/Electronics
Abstract

International audience

Published
2021

38. Design, Fabrication, and In-Vivo Characterization of Strain Sensors Based on 1D Nanomaterials for Health Monitoring Wearable Devices

Author

Boschetto, Gabriele, Tieying, Xu, Yehya, Mohamad, Carapezzi, Stefania, Thireau, Jérôme, Lacampagne, Alain, Charlot, Benoit, Gil, Thierry, Todri-Sanial, Aida, Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut d’Electronique et des Systèmes (IES), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Matériaux, Micro et Nanodispositifs (M2N), Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] (PhyMedExp), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Capteurs et Instrumentations (CI), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), European Project: 825114,SmartVista, Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SPI.MAT]Engineering Sciences [physics]/Materials
Published
2021

39. 68Ga-PSMA PET/CT for Response Assessment and Outcome Prediction in Metastatic Prostate Cancer Patients Treated with Taxane-Based Chemotherapy

Author

Shagera, Qaid Ahmed, primary, Artigas, Carlos, additional, Karfis, Ioannis, additional, Critchi, Gabriela, additional, Chanza, Nieves Martinez, additional, Sideris, Spyridon, additional, Peltier, Alexandre, additional, Paesmans, Marianne, additional, Gil, Thierry, additional, and Flamen, Patrick, additional

Published
2021
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41. Oligometastatic Disease Detection with 68Ga-PSMA-11 PET/CT in Hormone-Sensitive Prostate Cancer Patients (HSPC) with Biochemical Recurrence after Radical Prostatectomy: Predictive Factors and Clinical Impact

Author

Artigas, Carlos, primary, Diamand, Romain, additional, Shagera, Qaid Ahmed, additional, Plouznikoff, Nicolas, additional, Fokoue, Fabrice, additional, Otte, François-Xavier, additional, Gil, Thierry, additional, Peltier, Alexandre, additional, Van Gestel, Dirk, additional, and Flamen, Patrick, additional

Published
2021
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42. FPGA Implementation of Oscillatory Neural Networks for Artificial Intelligence Edge Computing

Author

Abernot, Madeleine, Boschetto, Gabriele, Carapezzi, Stefania, Delacour, Corentin, Gil, Thierry, Todri-Sanial, Aida, Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), European Project: 871501,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),H2020-ICT-2019-2,NeurONN(2020), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI]Engineering Sciences [physics], [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2021

43. EU H2020 NEURONN: 2D Oscillatory Neural Networks For Energy Efficient Neuromorphic Computing

Author

Carapezzi, Stefania, Boschetto, Gabriele, Delacour, Corentin, Abernot, Madeleine, Gil, Thierry, Todri-Sanial, Aida, Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871501., European Project: 871501,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),H2020-ICT-2019-2,NeurONN(2020), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Molybdenum disulfide (MoS2), Neuromorphic computing, pattern recognition, Beyond-CMOS device, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], Memristor, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, vanadium dioxide (VO2), [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, FPGA, Oscillatory Neural Network (ONN), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI.MAT]Engineering Sciences [physics]/Materials
Abstract

National audience; In this paper, we showcase a leading-edge implementation of oscillatory neural networks (ONNs) using beyond Complementary-Metal-Oxide-Semiconductor devices based on vanadium dioxide to mimick neurons, and 2D molybdenum disulfide memristors to emulate synapses. We explore the ONN technology through simulations from materials to devices up to circuits. We show that ONNs naturally behave like associative memories and can be used for pattern recognition, a task to be exploited in edge devices. Finally, we develop a reconfigurable digital ONN-on-FPGA to assess ONN functionality in real world applications.

Published
2021

44. Assessment of 1D and 2D Materials for Health Monitoring Wearable Devices

Author

Boschetto, Gabriele, Xu, Tieying, Yehya, Mohamad, Thireau, Jérôme, Lacampagne, Alain, Charlot, Benoit, Gil, Thierry, Todri-Sanial, Aida, Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut d’Electronique et des Systèmes (IES), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Matériaux, Micro et Nanodispositifs (M2N), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] (PhyMedExp), Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SPI.MAT]Engineering Sciences [physics]/Materials
Abstract

National audience; Here, the performance of 1D and 2D nanomaterials (carbon nanotubes and MoS2, respectively) was assessed for the development of novel highly sensitive and flexible strain sensors to monitor patients’ respiratory rate. To do so, we followed a multi-disciplinary and multi-level ap- proach, starting from atomistic simulations, up to device fabrication and ex-vivo testing. We successfully designed, fabricated and tested a CNT-based flexible strain sensor capable of tracking the respiratory rate, and we showed 1D and 2D materials to have excellent mechanical and electronic properties for the use in such devices.

Published
2021

46. Oscillatory Neural Networks for Edge AI Computing

Author

Delacour, Corentin, primary, Carapezzi, Stefania, additional, Abernot, Madeleine, additional, Boschetto, Gabriele, additional, Azemard, Nadine, additional, Salles, Jeremie, additional, Gil, Thierry, additional, and Todri-Sanial, Aida, additional

Published
2021
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47. Digital Implementation of Oscillatory Neural Network for Image Recognition Applications

Author

Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo, European Union (UE). H2020, Abernot, Madeleine, Gil, Thierry, Jiménez, Manuel, Núñez Martínez, Juan, Avedillo de Juan, María José, Linares Barranco, Bernabé, Gonos, Théophile, Hardelin, Tanguy, Todri Sanial, Aida, Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo, European Union (UE). H2020, Abernot, Madeleine, Gil, Thierry, Jiménez, Manuel, Núñez Martínez, Juan, Avedillo de Juan, María José, Linares Barranco, Bernabé, Gonos, Théophile, Hardelin, Tanguy, and Todri Sanial, Aida

Abstract

Computing paradigm based on von Neuman architectures cannot keep up with the ever-increasing data growth (also called “data deluge gap”). This has resulted in investigating novel computing paradigms and design approaches at all levels from materials to system-level implementations and applications. An alternative computing approach based on artificial neural networks uses oscillators to compute or Oscillatory Neural Networks (ONNs). ONNs can perform computations efficiently and can be used to build a more extensive neuromorphic system. Here, we address a fundamental problem: can we efficiently perform artificial intelligence applications with ONNs? We present a digital ONN implementation to show a proof-of-concept of the ONN approach of “computing-in-phase” for pattern recognition applications. To the best of our knowledge, this is the first attempt to implement an FPGA-based fully-digital ONN. We report ONN accuracy, training, inference, memory capacity, operating frequency, hardware resources based on simulations and implementations of 5 × 3 and 10 × 6 ONNs. We present the digital ONN implementation on FPGA for pattern recognition applications such as performing digits recognition from a camera stream. We discuss practical challenges and future directions in implementing digital ONN.

Published
2021

48. Prostate Cancer and Reactive Haemophagocytic Lymphohistiocytosis

Author

Dumont, Laura, Salaroli, Adriano, Dal Lago, Lissandra, Gil, Thierry, Pepersack, Thierry, Dumont, Laura, Salaroli, Adriano, Dal Lago, Lissandra, Gil, Thierry, and Pepersack, Thierry

Abstract

We report the case of a 76-year-old man presenting with reactive haemophagocytic lymphohistiocytosis (rHLH) in the setting of disseminatedprostate cancer. This often fatal syndrome must be diagnosed early in order to maximize survival. Treatment should be initiated wheneverthe clinical diagnosis is suspected, even if the HLH-2004 criteria are not met. The HScore is a useful diagnostic tool for rHLH. In case ofneurological symptoms, an extensive assessment must be performed. The goal of this case report is to raise awareness of this rare syndromeamong oncologists., LEARNING POINTS• The association of prostate cancer and reactive haemophagocytic lymphohistiocytosis (rHLH) has rarely been described.• This often fatal syndrome must be recognized early in order to start specific treatment and maximize survival.• Specific treatment for rHLH must be accompanied by treatment of the triggering factors, SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2021

49. Digital Implementation of Oscillatory Neural Network for Image Recognition Applications

Author

European Commission, Abernot, Madeleine, Gil, Thierry, Jiménez Través, Manuel, Núñez, Juan, Avellido, María J., Linares-Barranco, Bernabé, Gonos, Théophile, Hardelin, Tanguy, Todri-Sanial, Aída, European Commission, Abernot, Madeleine, Gil, Thierry, Jiménez Través, Manuel, Núñez, Juan, Avellido, María J., Linares-Barranco, Bernabé, Gonos, Théophile, Hardelin, Tanguy, and Todri-Sanial, Aída

Abstract

Computing paradigm based on von Neuman architectures cannot keep up with the ever-increasing data growth (also called "data deluge gap"). This has resulted in investigating novel computing paradigms and design approaches at all levels from materials to system-level implementations and applications. An alternative computing approach based on artificial neural networks uses oscillators to compute or Oscillatory Neural Networks (ONNs). ONNs can perform computations efficiently and can be used to build a more extensive neuromorphic system. Here, we address a fundamental problem: can we efficiently perform artificial intelligence applications with ONNs? We present a digital ONN implementation to show a proof-of-concept of the ONN approach of "computing-in-phase" for pattern recognition applications. To the best of our knowledge, this is the first attempt to implement an FPGA-based fully-digital ONN. We report ONN accuracy, training, inference, memory capacity, operating frequency, hardware resources based on simulations and implementations of 5 × 3 and 10 × 6 ONNs. We present the digital ONN implementation on FPGA for pattern recognition applications such as performing digits recognition from a camera stream. We discuss practical challenges and future directions in implementing digital ONN.

Published
2021

50. Ab Initio Computer Simulations on Interfacial Properties of Single-Layer MoS2 and Au Contacts for Two-Dimensional Nanodevices.

Author

Boschetto, Gabriele, Carapezzi, Stefania, Delacour, Corentin, Abernot, Madeleine, Gil, Thierry, and Todri-Sanial, Aida

Abstract

The functionality and performance of devices based on atomically thin two-dimensional (2D) materials strongly depend on the quality of the employed 2D material. Although molybdenum disulfide (MoS2) is an excellent candidate for future applications in nanoelectronics, MoS2 films have not yet reached the level of purity achieved in silicon technologies. At present, the formation of small and extended defects in the material is inevitable during the growth process, and this has a non-negligible impact on the electronic properties of MoS2. Furthermore, defects are also thought to affect nontrivially the resistance at the MoS2–metal contact and the injection of carriers. In this work, we systematically and thoroughly assess the impact of some of the most commonly occurring defects in MoS2 (such as vacancies, substitutions, and grain boundaries) not only from the point of view of the material's properties but also by looking at MoS2–metal contacts. To do so, we carry out ab initio computer simulations in the density functional theory (DFT) framework coupled with surface simulations based on the Green's function formalism. Our simulation approach allows us to obtain more realistic models of MoS2 interfaces with Au. Moreover, this is the first theoretical study in which the effect of grain boundaries on the MoS2–Au contact properties is explored. Results suggest that S vacancies have a detrimental impact on the quality of the metal contacts, whereas Mo vacancies strongly improve the electron injection from the metal to MoS2. Antisite Mo defects also increase the electron injection rate by acting as "conductive bridges" between the Au electrode and the 2D material. Finally, each of the grain boundaries considered here improves the quality of the contact. We expect our study to provide the necessary theoretical foundation for the design of MoS2–metal contacts with suitable characteristics. [ABSTRACT FROM AUTHOR]

Published
2022
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