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808 results on '"Liutkus, A."'

1. Black-box language model explanation by context length probing

Author

Cífka, Ondřej and Liutkus, Antoine

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present context length probing, a novel explanation technique for causal language models, based on tracking the predictions of a model as a function of the length of available context, and allowing to assign differential importance scores to different contexts. The technique is model-agnostic and does not rely on access to model internals beyond computing token-level probabilities. We apply context length probing to large pre-trained language models and offer some initial analyses and insights, including the potential for studying long-range dependencies. The source code and an interactive demo of the method are available., Comment: 11 pages, 9 figures. ACL 2023 short paper camera-ready. Demos at https://cifkao.github.io/context-probing/ and https://huggingface.co/spaces/cifkao/context-probing ; code at https://github.com/cifkao/context-probing/

Published
2022
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2. Optimization of cerebral oxygenation based on regional cerebral oxygen saturation monitoring during carotid endarterectomy: a Phase III multicenter, double-blind randomized controlled trial

Author

Le Teurnier, Yann, Rozec, Bertrand, Degryse, Cecile, Levy, François, Miliani, Youcef, Godet, Gilles, Daccache, Georges, Truc, Cyrille, Steinmetz, Eric, Ouattara, Alexandre, Cholley, Bernard, Malinovsky, Jean-Marc, Portier, Denis, Dupont, Gregory, Liutkus, Darius, Viard, Pierre, Pere, Morgane, Daumas-Duport, Benjamin, Magras, Pierre-Aubin, and Vourc’h, Mickael

Published
2024
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4. Relative Positional Encoding for Transformers with Linear Complexity

Author

Liutkus, Antoine, Cífka, Ondřej, Wu, Shih-Lun, Şimşekli, Umut, Yang, Yi-Hsuan, and Richard, Gaël

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

Recent advances in Transformer models allow for unprecedented sequence lengths, due to linear space and time complexity. In the meantime, relative positional encoding (RPE) was proposed as beneficial for classical Transformers and consists in exploiting lags instead of absolute positions for inference. Still, RPE is not available for the recent linear-variants of the Transformer, because it requires the explicit computation of the attention matrix, which is precisely what is avoided by such methods. In this paper, we bridge this gap and present Stochastic Positional Encoding as a way to generate PE that can be used as a replacement to the classical additive (sinusoidal) PE and provably behaves like RPE. The main theoretical contribution is to make a connection between positional encoding and cross-covariance structures of correlated Gaussian processes. We illustrate the performance of our approach on the Long-Range Arena benchmark and on music generation., Comment: ICML 2021 (long talk) camera-ready. 24 pages

Published
2021

6. AI in the media and creative industries

Author

Amato, Giuseppe, Behrmann, Malte, Bimbot, Frédéric, Caramiaux, Baptiste, Falchi, Fabrizio, Garcia, Ander, Geurts, Joost, Gibert, Jaume, Gravier, Guillaume, Holken, Hadmut, Koenitz, Hartmut, Lefebvre, Sylvain, Liutkus, Antoine, Lotte, Fabien, Perkis, Andrew, Redondo, Rafael, Turrin, Enrico, Vieville, Thierry, and Vincent, Emmanuel

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Thanks to the Big Data revolution and increasing computing capacities, Artificial Intelligence (AI) has made an impressive revival over the past few years and is now omnipresent in both research and industry. The creative sectors have always been early adopters of AI technologies and this continues to be the case. As a matter of fact, recent technological developments keep pushing the boundaries of intelligent systems in creative applications: the critically acclaimed movie "Sunspring", released in 2016, was entirely written by AI technology, and the first-ever Music Album, called "Hello World", produced using AI has been released this year. Simultaneously, the exploratory nature of the creative process is raising important technical challenges for AI such as the ability for AI-powered techniques to be accurate under limited data resources, as opposed to the conventional "Big Data" approach, or the ability to process, analyse and match data from multiple modalities (text, sound, images, etc.) at the same time. The purpose of this white paper is to understand future technological advances in AI and their growing impact on creative industries. This paper addresses the following questions: Where does AI operate in creative Industries? What is its operative role? How will AI transform creative industries in the next ten years? This white paper aims to provide a realistic perspective of the scope of AI actions in creative industries, proposes a vision of how this technology could contribute to research and development works in such context, and identifies research and development challenges.

Published
2019

7. Speech enhancement with variational autoencoders and alpha-stable distributions

Author

Leglaive, Simon, Simsekli, Umut, Liutkus, Antoine, Girin, Laurent, and Horaud, Radu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders. The noise model remains unsupervised because we do not assume prior knowledge of the noisy recording environment. In this context, our contribution is to propose a noise model based on alpha-stable distributions, instead of the more conventional Gaussian non-negative matrix factorization approach found in previous studies. We develop a Monte Carlo expectation-maximization algorithm for estimating the model parameters at test time. Experimental results show the superiority of the proposed approach both in terms of perceptual quality and intelligibility of the enhanced speech signal., Comment: 5 pages, 3 figures, audio examples and code available online : https://team.inria.fr/perception/research/icassp2019-asvae/. arXiv admin note: text overlap with arXiv:1811.06713

Published
2019
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8. Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions

Author

Liutkus, Antoine, Şimşekli, Umut, Majewski, Szymon, Durmus, Alain, and Stöter, Fabian-Robert

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

By building upon the recent theory that established the connection between implicit generative modeling (IGM) and optimal transport, in this study, we propose a novel parameter-free algorithm for learning the underlying distributions of complicated datasets and sampling from them. The proposed algorithm is based on a functional optimization problem, which aims at finding a measure that is close to the data distribution as much as possible and also expressive enough for generative modeling purposes. We formulate the problem as a gradient flow in the space of probability measures. The connections between gradient flows and stochastic differential equations let us develop a computationally efficient algorithm for solving the optimization problem. We provide formal theoretical analysis where we prove finite-time error guarantees for the proposed algorithm. To the best of our knowledge, the proposed algorithm is the first nonparametric IGM algorithm with explicit theoretical guarantees. Our experimental results support our theory and show that our algorithm is able to successfully capture the structure of different types of data distributions., Comment: Published at the International Conference on Machine Learning (ICML) 2019

Published
2018

9. An Overview of Lead and Accompaniment Separation in Music

Author

Rafii, Zafar, Liutkus, Antoine, Stöter, Fabian-Robert, Mimilakis, Stylianos Ioannis, FitzGerald, Derry, and Pardo, Bryan

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Popular music is often composed of an accompaniment and a lead component, the latter typically consisting of vocals. Filtering such mixtures to extract one or both components has many applications, such as automatic karaoke and remixing. This particular case of source separation yields very specific challenges and opportunities, including the particular complexity of musical structures, but also relevant prior knowledge coming from acoustics, musicology or sound engineering. Due to both its importance in applications and its challenging difficulty, lead and accompaniment separation has been a popular topic in signal processing for decades. In this article, we provide a comprehensive review of this research topic, organizing the different approaches according to whether they are model-based or data-centered. For model-based methods, we organize them according to whether they concentrate on the lead signal, the accompaniment, or both. For data-centered approaches, we discuss the particular difficulty of obtaining data for learning lead separation systems, and then review recent approaches, notably those based on deep learning. Finally, we discuss the delicate problem of evaluating the quality of music separation through adequate metrics and present the results of the largest evaluation, to-date, of lead and accompaniment separation systems. In conjunction with the above, a comprehensive list of references is provided, along with relevant pointers to available implementations and repositories.

Published
2018

10. The 2018 Signal Separation Evaluation Campaign

Author

Stöter, Fabian-Robert, Liutkus, Antoine, and Ito, Nobutaka

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

This paper reports the organization and results for the 2018 community-based Signal Separation Evaluation Campaign (SiSEC 2018). This year's edition was focused on audio and pursued the effort towards scaling up and making it easier to prototype audio separation software in an era of machine-learning based systems. For this purpose, we prepared a new music separation database: MUSDB18, featuring close to 10h of audio. Additionally, open-source software was released to automatically load, process and report performance on MUSDB18. Furthermore, a new official Python version for the BSSEval toolbox was released, along with reference implementations for three oracle separation methods: ideal binary mask, ideal ratio mask, and multichannel Wiener filter. We finally report the results obtained by the participants., Comment: To appear in International Conference on Latent Variable Analysis and Signal Separation

Published
2018

12. Blind Source Separation Using Mixtures of Alpha-Stable Distributions

Author

Keriven, Nicolas, Deleforge, Antoine, and Liutkus, Antoine

Subjects
Statistics - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions. Complex symmetric alpha-stable distributions have been recently showed to better model audio signals in the time-frequency domain than classical Gaussian distributions thanks to their larger dynamic range. However, inference of these models is notoriously hard to perform because their probability density functions do not have a closed-form expression in general. Here, we introduce a novel method for estimating mixture of alpha-stable distributions based on characteristic function matching. We apply this to the blind estimation of binary masks in individual frequency bands from multichannel convolutive audio mixes. We show that the proposed method yields better separation performance than Gaussian-based binary-masking methods., Comment: International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, Canada

Published
2017

14. Outcomes with Finerenone in Participants with Stage 4 CKD and Type 2 Diabetes: A FIDELITY Subgroup Analysis

Author

Sarafidis, Pantelis, Agarwal, Rajiv, Pitt, Bertram, Wanner, Christoph, Filippatos, Gerasimos, Boletis, John, Tuttle, Katherine R., Ruilope, Luis M., Rossing, Peter, Toto, Robert, Anker, Stefan D., Liu, Zhi-Hong, Joseph, Amer, Ahlers, Christiane, Brinker, Meike, Lawatscheck, Robert, Bakris, George, Aizenberg, Diego, Bartolacci, Inés, Besada, Diego, Bittar, Julio, Chahin, Mariano, Elbert, Alicia, Gelersztein, Elizabeth, Liberman, Alberto, Maffei, Laura, Manghi, Federico Pérez, Sanabria, Hugo, Vallejos, Augusto, Viñes, Gloria, Wassermann, Alfredo, Abhayaratna, Walter, Acharya, Shamasunder, Ekinci, Elif, Lee, Darren, MacIsaac, Richard, Mah, Peak Mann, Nelson, Craig, Packham, David, Pape, Alexia, Roger, Simon, Stephenson, Hugo, Suranyi, Michael, Wittert, Gary, Vale, Elizabeth, Colman, Peter, Colquhoun, David, Ellis, Chris, Joshua, Kim, Pedagogos, Eugenia, Regal, Paul, Topliss, Duncan, Vandeleur, James, Verjans, Johan, Wittert, Gary, Wynne, Katie-Jane, Clodi, Martin, Ebenbichler, Christoph, Fliesser-Görzer, Evelyn, Hanusch, Ursula, Krebs, Michael, Lhotta, Karl, Ludvik, Bernhard, Mayer, Gert, Neudorfer, Peter, Paulweber, Bernhard, Prager, Rudolf, Preiß, Wolfgang, Prischl, Friedrich, Schernthaner, Gerit-Holger, Sourij, Harald, Wiesholzer, Martin, Drexel, Heinz, Oberbauer, Rainer, Schönherr, Hans-Robert, Doubel, Peter, Engelen, Wendy, Gillard, Pieter, Hougardy, Jean-Michel, Krzesinski, Jean-Marie, Maes, Bart, Speeckaert, Marijn, Stas, Koen, van Gaal, Luc, Vanbelleghem, Hilde, Duyck, Francis, Scheen, André, Antunes, Daniela, Botelho, Roberto, Brito, Claudia, Canani, Luis, Canziani, Maria Eugenia, Cerqueira, Maria, de Paula, Rogerio, Eliaschewitz, Freddy, Figueiredo, Carlos Eduardo, Forti, Adriana, Hissa, Miguel, Leite, Emerson Lima Maurilo, Jr, Noronha, Irene, Paolino, Bruno, Paschoalin, Nathalia, Paschoalin, Raphael, Filho, Roberto Pecoits, Pereira, Marcio, Portes, Evandro, Precoma, Dalton, Rea, Rosangela, Riella, Miguel, Salles, Joao Eduardo, Vasconcellos, Eduardo, Vencio, Sergio, Bacci, Marcelo, Maia, Lilia, Villacorta, Aline, Apostolova, Emiliya, Boshnyashka, Radostina, Farah, Ghassan, Georgiev, Dimitar, Gushterova, Valentina, Klyuchkova, Neli, Lucheva, Mariya, Manova, Petya, Minkova, Dotska, Nonchev, Boyan, Pichmanova, Mariyana, Prakova, Zhulieta, Rangelov, Rangel, Rashkov, Rosen, Stanchev, Pavel, Stoyanovska-Elencheva, Bilyana, Tagarev, Zhivko, Temelkova-Kurktschieva, Theodora, Vasileva, Svetla, Yoncheva-Mihaylova, Mariana, Marinchev, Angel, Miteva, Mariya, Barre, Paul, Carlson, Brian, Conway, James, Cournoyer, Serge, Dumas, Richard, Fikry, Sameh, Goluch, Richard, Hamet, Pavel, Hart, Randolph, Henein, Sam, Liutkus, Joanne, Madore, Francois, Martinho, Valdemar, Mazza, Giuseppe, McFarlane, Philip, Keefe, Dennis O′, Peterson, Sean, Schwartz, Daniel, Shu, Daniel, Steele, Andrew, Tellier, Guy, Tennankore, Karthik, Tobe, Sheldon, Tsoukas, George, Tytus, Richard, Vitou, Louise, Walsh, Michael, Weisnagel, Stanley, Wilderman, Igor, Yale, Jean-Francois, El Boreky, Fadia, Kelly, Alan, Leiter, Lawrence, Teitelbaum, Ivor, Cobos, Jorge, Godoy, Juan, González, Fernando, Lobos, Sergio, Palma, Juan Carlos, Prieto Dominguez, Juan Carlos, Reyes, Eliana, Romero, Carmen, Saavedra, Victor, Vega, Mario, Medina, Marcelo, Varleta, Paola, Bu, Ruifang, Cai, Hanqing, Chen, Nan, Chen, Qinkai, Chen, Dejun, Cheng, Jinluo, Dong, Youping, Dong, Junwu, Guan, Tianjun, Hao, Chuanming, Huang, Wen, Jiang, Fangfang, Lei, Minxiang, Li, Ling, Li, Zhonghe, Li, Xuemei, Li, Jingmei, Li, Yan, Liang, Xinling, Liang, Bo, Liu, Fang, Liu, Yinghong, Liu, Yuantao, Liu, Zhihong, Long, Gang, Lu, Guoyuan, Lu, Weiping, Lu, Yibing, Luo, Ping, Ma, Jianhua, Mo, Zhaohui, Niu, Jianying, Peng, Ai, Shen, Jiansong, Shen, Feixia, Shi, Bingyin, Su, Qing, Sun, Zhuxing, Tang, Shuifu, Tong, Nanwei, Wang, Hao, Wang, Xinjun, Wang, Guixia, Wang, Jianqin, Wang, Yangang, Wang, Li, Wei, Jiali, Wu, Tianfeng, Wu, Chaoqing, Xing, Changying, Xiong, Fei, Xu, Xudong, Xu, Ning, Yan, Tiekun, Yang, Jinkui, Yin, Aiping, Zeng, Longyi, Zhang, Hao, Zhang, Yanlin, Zhang, Ying, Zhao, Wenjing, Zhao, Zhiquan, Zheng, Hongguang, Zhong, Ling, Zhu, Dalong, Zhuang, Yongze, Du, Yuming, Fang, Yi, Guo, Weiying, Jiang, Sheng, Kuang, Jian, Li, Dongmei, Li, Hongmei, Li, Yinan, Li, Yuxiu, Liu, Jian, Liu, Yu, Miao, Heng, Peng, Wen, Wang, Lihua, Xu, Mingtong, Zhong, Liyong, Zhu, Jun, Arango, Clara, Barrera, Sandra, López, Nelly Beltrán, Benitez, Diego, Blanco, Guillermo, Cadena, Andrés, Coronel, Julian, Cure, Carlos, Durán, Carlos, González, Alexander, Guzmán, Gustavo, Hernández, Eric, Ibarra, Jaime, Jaramillo, Carlos, Jaramillo, Nicolás, Kattah, William, Molina, Dora, Sánchez, Gregorio, Terront, Mónica, Trujillo, Freddy, Urina, Miguel, Vargas, Ruben, Villegas, Iván, Yupanqui, Hernán, Arcos, Edgar, Aroca, Gustavo, Barreto, Germán, Bermudez, Andres, Botero, Rodrigo, Cárdenas, Tatiana, Figueroa, Wilmer, Jaramillo, Mónica, Liévano, Manuel, López, Mónica, Molina, Dora, Rosero, Ricardo, Trillos, Pedro, Dino Alferi, Brada, Michal, Brezina, Jiri, Bucek, Petr, Edelsberger, Tomas, Gulakova, Drahomira, Zapletalova, Jitka Hasalova, Hola, Olga, Hornova, Lucie, Houdova, Jana, Hrmova, Helena, Karasek, David, Kopecka, Sarka, Kovar, Richard, Krcova, Eva, Kuchar, Jiri, Kutejova, Vlasta, Lubanda, Hana, Matyasek, Ivo, Mokrejsova, Magdalena, Okenka, Libor, Prazny, Martin, Pumprla, Jiri, Tomanek, Pavel, Andersen, Ulla, Andries, Alin, Bech, Jesper, Faber, Jens, Gislason, Gunnar, Hangaard, Jørgen, Pacyk, Grzegorz Jaroslaw, Juhl, Claus, Krarup, Thure, Lindhardt, Morten, Madsbad, Sten, Nielsen, Joan, Pedersen-Bjergaard, Ulrik, Poulsen, Per, Rasmussen, Ole, Rossing, Peter, Schousboe, Karoline, Gram, Jeppe, Lauridsen, Thomas, Pedersen, Erling, Thorsteinsson, Birger, Flöjt, Päivi, Honkasalo, Mikko, Honkasalo, Mikko, Humaloja, Kari, Kananen, Kristiina, Kantola, Ilkka, Koistinen, Arvo, Korsoff, Pirkko, Lahtela, Jorma, Nieminen, Sakari, Nieminen, Tuomo, Sadeharju, Karita, Strand, Jorma, Sulosaari, Sakari, Cariou, Bertrand, Chantrel, François, Clavel, Sylvaine, Combe, Christian, Fauvel, Jean-Pierre, Gallouj, Karim, Gouet, Didier, Guerci, Bruno, Guerrot, Dominique, Hourmant, Maryvonne, Klein, Alexandre, Mariat, Christophe, Marre, Michel, Mesbah, Rafik, Le Meur, Yannick, Monier, Arnaud, Moranne, Olivier, Roussel, Ronan, Serusclat, Pierre, Vendrely, Benoit, Verges, Bruno, Zaoui, Philippe, Axthelm, Christoph, Bergmann, Andreas, Birkenfeld, Andreas L., Braun, Hermann, Busch, Klaus, Contzen, Christel, Degenhardt, Stefan, Derwahl, Karl, Giebel, Thomas, Hagenow, Andreas, Haller, Hermann, Hasslacher, Christoph, Horacek, Thomas, Jungmair, Wolfgang, Kloos, Christof, Koch, Thorsten, Krüger, Thilo, Mühlfeld, Anja, Müller, Joachim, Pfützner, Andreas, Pistrosch, Frank, Rinke, Andrea, Rose, Ludger, Rump, Lars, Schettler, Volker, Schiefke, Ingolf, Schlichthaar, Heike, Schröppel, Bernd, Schöll, Norbert, Schubert, Kristin, Schürholz, Thomas, Sigal, Helena, Stemler, Lutz, Strack, Georg, Täschner, Heidrun, Toursarkissian, Nicole, Tschöpe, Diethelm, Ulmer, Achim, van der Giet, Markus, Wanner, Christoph, Winkelmann, Bernhard R., Boletis, Ioannis, Dimitriadis, George, Hatziagelaki, Erifili, Iatrou, Christos, Ioannidis, Ioannis, Kounadi, Theodora, Makriniotou, Ioanna, Papadopoulou, Dorothea, Papagianni, Aikaterini, Passadakis, Ploumis, Piaditis, George, Stefanidis, Ioannis, Tai Pang Ip, Lee, Paul, Andrea Luk, On Yan, Ma, Ronald, Chow, Wing Sun, Wang, Angela, Yeung, Vincent, Bajcsi, Dora, Danos, Peter, Harcsa, Eleonora, Kalina, Akos, Kazup, Szilvia, Keltai, Katalin, Kirschner, Robert, Kiss, Julianna, Kovacs, Laszlo, Lamboy, Beata, Literati-Nagy, Botond, Mileder, Margit, Nagy, Laszlo, Noori, Ebrahim, Nyirati, Gabor, Petro, Gizella, Schneider, Karoly, Simon, Judit, Szocs, Albert, Vasas, Szilard, Wudi, Krisztina, Zilahi, Zsolt, Zsom, Marianna, Eustace, Joe, Holian, John, Reddan, Donal, Meara, Yvonne O′, Abramof Ness, Rosane, Adawi, Faiad, Armaly, Zaher, Atar, Shaul, Bashkin, Amir, Ben Chetrit, Sydney, Yanay, Noa Berar, Chernin, Gil, Darawsha, Mahmud, Efrati, Shai, Elias, Mazen, Farber, Evgeny, Glandt, Mariela, Grossman, Ehud, Halabi, Majdi, Harman-Boehm, Ilana, Khazim, Khaled, Liberty, Idit, Minuchin, Oscar, Mosenzon, Ofri, Nakhoul, Farid, Nimer, Assy, Schwartz, Doron, Wainstein, Julio, Yagil, Yoram, Zukermann, Robert, Avogaro, Angelo, Battaglia, Giovanni Giorgio, Bevilacqua, Maurizio Tiziano, Bonora, Enzo, Bossi, Carlo Antonio, Calabrò, Paolo, Cavalot, Franco Luigi, Cimino, Roberto, Cozzolino, Mario Gennaro, David, Salvatore, Emdin, Michele, Fiaccadori, Enrico, Fiorina, Paolo, Giorda, Carlo Bruno, Gregorini, Maria Cristina, La Manna, Gaetano, Maggi, Davide Carlo, Manti, Roberta, Meregalli, Giancarla, Pani, Antonello, Parvanova, Aneliy Ilieva, Perico, Norberto, Piatti, PierMarco, Pisani, Antonio, Pontiroli, Antonio Ettore, Ponzani, Paola, Santorelli, Gennaro, Santoro, Domenico, Scanziani, Renzo, Teatini, Ugo, Tonolo, Giancarlo, Trevisan, Roberto, Veronelli, Anna Maria, Viviani, Giorgio Luciano, Araki, Hideo, Bando, Yukihiro, Ebisui, Osamu, Fujita, Naruhiro, Fukasawa, Hirotaka, Furuya, Ryuichi, Hamamoto, Yoshiyuki, Hamasaki, Akihiro, Hasegawa, Kotaro, Hatazaki, Masahiro, Hayashi, Terumasa, Higashi, Takayuki, Hirohata, Yoshihide, Horinouchi, Shuji, Hoshi, Ayumu, Imoto, Hirofumi, Inagaki, Akemi, Inagaki, Masayuki, Inaguma, Daijo, Inoue, Toshihiko, Ishii, Masao, Ishiko, Tamayo, Isono, Motohide, Jinnouchi, Hideaki, Kanai, Hidetoshi, Kanda, Daisuke, Kanehara, Hideo, Kashima, Masayuki, Kataoka, Yuko, Katayama, Shigehiro, Kato, Kiyoe, Katsuki, Takeshi, Kawamitsu, Katsunori, Kawasaki, Satsuki, Kikuchi, Fumi, Kikuchi, Hidetoshi, Kishimoto, Rui, Kobayashi, Kunihisa, Koide, Junko, Komi, Rieko, Kubota, Miyuki, Kuriya, Genpei, Kurose, Takeshi, Kusano, Yoshiro, Hajime, Maeda, Matsubayashi, Sunao, Matsumoto, Kazunari, Matsumura, Naoya, Matsuo, Yasuto, Matsuoka, Naoki, Miyaoka, Hiroaki, Miyata, Satoshi, Morita, Takeshi, Murakami, Isao, Murao, Satoshi, Nakamura, Udai, Nakayama, Mikihiro, Nakazawa, Jun, Nohara, Sakae, Nomiyama, Takashi, Noritake, Masayuki, Oda, Yoshiaki, Ogiwara, Takayuki, Ohashi, Hiroshi, Okamoto, Hideki, Okino, Shinichi, Osonoi, Takeshi, Sasaki, Nobuhiro, Sayo, Yoshitaka, Sekigami, Taiji, Shibasaki, Taro, Shibata, Hirotaka, Shimoyama, Tatsushi, Shinoda, Junji, Sobajima, Hiroshi, Sugitatsu, Kazuya, Sugiura, Toshiyuki, Sugiyama, Toru, Suzuki, Daisuke, Suzuki, Hiroyuki, Suzuki, Masaaki, Takeda, Asami, Tanaka, Asami, Tanaka, Seiichi, Tsunematsu, Izumi, Ueda, Yasuo, Uekihara, Soichi, Ujihara, Makoto, Yajima, Ken, Yamada, Daishiro, Yamada, Masayo, Yamagata, Kazuo, Yamakawa, Ken, Yamakawa, Fumiko, Yamasaki, Yoshimitsu, Yambe, Yuko, Yanagida, Taihei, Yanai, Hidekatsu, Yanase, Toshihiko, Yasuda, Tetsuyuki, Kriauciuniene, Dovile, Lasiene, Jurate, Navickas, Antanas, Radzeviciene, Lina, Urbanaviciene, Egle, Urbonas, Gediminas, Velaviciene, Audrone, Abd Ghani, Rohana, Aziz, Nor Azizah, Lee, Li Yuan, Loh, Chek Loong, Ali, Norhaliza Mohd, Noor, Nurain Mohd, Fatnoon Nik Ahmad, Nik Nur, Ratnasingam, Jeyakantha, Halimi Bin Wan Hasan, Wan Hasnul, Izani Wan Mohamed, Wan Mohd, Khir, Rizmy Najme, Mohamad, Masni, Alexander Tan, Tong Boon, Avila Pardo, Sandro, Adrian, Miriam Bastidas, Wong, Alfredo Chew, Escobedo de la Peña, Jorge, Salmón, Guillermo Fanghänel, Gálvez, Guillermo González, Ochoa, Ramiro Gutiérrez, Santana, Saúl Irizar, Rovalo, Magdalena Madero, Machado, Gustavo Méndez, Ruiz, Luis Nevarez, Ibarra, Denisse Ramos, López, Gabriel Ramos, Reyna, Leobardo Sauque, Ortiz, Gustavo Solache, Ortiz, Rafael Valdez, Mesa, Juan Villagordoa, Salazar, Melchor Alpizar, Hernández, Pedro García, González, José, Soto, José Lazcano, Mendoza, Arturo Saldaña, Santana, Sergio Irizar, Vilchis, Elvira González, Bakker, R.C., Barendregt, J.N.M., Boonstra, A.H., Bos, Willem, Brouwer, C.B., Buren, M. van, Gansevoort, Ron, Kooy, Adriaan, Krekels, Marielle, Leendert, Ruud J.M. van, Lieverse, Louis A.G., Luik, P.T., Penne, E. Lars, Gregoor, Peter Smak, Vogt, Liffert, Born, Bert-Jan van den, John Baker, Crawford, Veronica, Cutfield, Rick, Dunn, Peter, Krebs, Jeremy, Nirmalaraj, Kingsley, Scott, Russell, Smuts, Nine, Titchener, Janet, Eriksen, Erik, Finnes, Trine, Høivik, Hans, Karlsson, Thomas, Munk, Peter Scott, Radtke, Maria, Risberg, Knut, Rocke, Jan, Solnør, Leidulv, Stenehjem, Aud-Eldrid, Tafjord, Anne-Beathe, Asprusten, Emil, Hagemeier, Robert, Høye, Kjetil, Selsås, Hilde, Thorup, Frode, Wium, Cecilie, Pamugas, Glenda, Panelo, Araceli, Perez, Ronald, Tanque, Maribel, Tirador, Louie, Villa, Michael, Bautista, Albert, Catindig, Elizabeth, Manalo, Carlo, Mirasol, Roberto, Butrymowicz, Patrycja, Ciechanowski, Kazimierz, Cieslik, Grazyna, Franek, Edward, Gumprecht, Janusz, Hoffmann, Michal, Krzykowska, Jolanta, Kurnatowska, Ilona, Landa, Katarzyna, Madrzejewski, Adam, Madziarska, Katarzyna, Mazur, Stanislaw, Napora, Piotr, Nowicki, Michal, Ocicka-Kozakiewicz, Anna, Rewerska, Barbara, Rusicka, Teresa, Ruxer, Jan, Skokowska, Ewa, Stankiewicz, Andrzej, Stompor, Tomasz, Tiuryn-Petrulewicz, Agnieszka, Wasilewska, Katarzyna, Wierusz-Wysocka, Bogna, Wnetrzak-Michalska, Renata, Jedynasty, Krystyna, Anand, Izabela Sein, Almeida, Edgar, Ballesteros, Rosa, Barreto, Carlos, Beirao, Idalina, Birne, Rita, Esteves, Cesar, Guia, Jose, Heitor, Susana, Marques, Olinda, Melo, Pedro, Nolasco, Fernando, Pereira, Amalia, Roque, Cristina, Rosario, Francisco, Silva, Gil, Silva, Ana, Teixeira e Costa, Fernando, Lobos, Ana Vila, Alves, Ana Rita, Brandao, Ilidio, Carvalho, Rui, Coelho, Joao, Lourenco, Ana, Matos, Pedro, Rosario, Vanisa, Neves, Joao Sergio, Cortes-Maisonet, Gregorio, Roman-Miranda, Amaury, Brito-Peguero, Yudit, Colon-Vega, Gildred, Albota, Adrian, Bala, Cornelia, Barbonta, Hortensia, Caceaune, Elena, Catrinoui, Doina, Constantin, Ciprian, Dumitrescu, Adriana, Mindrescu, Nicoleta, Mistode, Cristina, Negrisanu, Gabriela, Onaca, Adriana, Paveliu, Silvia, Pintilei, Ella, Pop, Lavinia, Popa, Amorin, Popescu, Alexandrina, Radulian, Gabriela, Szilagyi, Iosif, Turcu, Liana, Vacaru, Georgeta, Vlad, Adrian, Filimon, Adriana, Veresiu, Ioan, Antsiferov, Mikhail, Arkhipov, Mikhail, Babkin, Andrey, Barbarash, Olga, Baranov, Vitaliy, Chernyavskaya, Elena, Demko, Arkadiy, Dreval, Alexander, Edin, Anton, Ermakova, Polina, Fadeev, Valentin, Galyavich, Albert, Gaysina, Leyla, Gordeev, Ivan, Ipatko, Irina, Kalashnikova, Marina, Khalimov, Yuriy, Klimontov, Vadim, Kobalava, Zhanna, Kosmacheva, Elena, Koziolova, Natalya, Sergey Levashov, Lyudmila Kvitkova, Libis, Roman, Marasaev, Vyacheslav, Malykh, Natalia, Martynenko, Vladimir, Malyutina, Sofya, Merai, Imad, Mkrtumyan, Ashot, Nechaeva, Galina, Petunina, Nina, Palyutin, Shamil, Pimenov, Leonid, Rechkova, Elena, Rodionova, Tatyana, Rymar, Oksana, Sardinov, Ruslan, Semenova, Olga, Sherenkov, Alexander, Solovev, Oleg, Smolyarchuk, Elena, Strongin, Leonid, Ukhanova, Olga, Verlan, Nadezhda, Vorokhobina, Natalya, Yakhontov, Davyd, Yakushin, Sergey, Zakharova, Elena, Zalevskaya, Alsu, Zanozina, Olga, Zhdanova, Elena, Zhukova, Larisa, Zykova, Tatyana, Argunova, Yulia, Nikolaev, Konstantin, Villevalde, Svetlana, Fang Sum, Chee, Suhail, Sufi Muhummad, San Tan, Ru, Vathsala, Anantharaman, Wong, Edmund, Bee, Yong Mong, Babikova, Jana, Buganova, Ingrid, Dzupina, Andrej, Ochodnicka, Zuzana, Sosovec, Dalibor, Spodniakova, Denisa, Minarik, Peter, Fayzal Ahmed, Amod, Aslam, Bhana, Sindeep, Distiller, Larry, Jansen van Rensburg, Dirkie, Joshi, Mukesh, Joshi, Shaifali, Lakha, Deepak, Mitha, Essack, Podgorski, Gracjan, Ranjith, Naresh, Rayner, Brian, Rheeder, Paul, Sarvan, Mohamed, Seeber, Mary, Siebert, Heidi, Tayob, Mohammed, Trokis, Julien, Urbach, Dorothea, van Zyl, Louis, Jansen van Rensburg, Dirkie, Bum-Soon Choi, Choi, Moon Gi, Chung, ChoonHee, Hwang, YouCheol, Kim, ChongHwa, Kim, InJoo, Kim, JaeHyeon, Kim, SinGon, Kim, SungGyun, Kim, Tae Hee, Lee, WooJe, Lee, ByungWan, Lee, Kang Wook, Oh, Kook-Hwan, Oh, Ji Eun, Oh, Yun Kyu, Oh, Dong-Jin, Park, Junbeom, Shin, Seok Joon, Sung, Su-Ah, Yu, Jae Myung, Chung, HyeSoo, Huh, Ji Hye, Kang, JunGoo, Kim, ChulSik, Kim, HyeSoon, Kim, NamHoon, Lim, Soo, Cho, Young Min, Park, Cheol Young, Agraz, Irene, Ampudia, Francisco Javier, Bouarich, Hanane, Calero, Francesca, Castro, Cristina, Guldris, Secundino Cigarrán, Garrit, Josep Cruzado, de Álvaro, Fernando, Galcerán, Josep, Albarrán, Olga González, Jaras, Julio Hernández, Ibernón, Meritxell, Deben, Francisco Martínez, Ma, Esteban, Dolores Martínez, Pascual Izuel, José María, Martins, Judith, Mediavilla, Juan, Michán, Alfredo, Santos, Julio Pascual, Poch, Esteban, Rusillo, Manuel Polaina, Juan, Carlos Sánchez, Olmo, Rafael Santamaría, Segura de la Morena, José Julián, Soto, Alfonso, Troya, Maribel, Castro, Fernando Cereto, Fernández, Pablo Gómez, Sánchez, Laura Fuentes, Moya, Mercedes González, Marrero, Domingo Hernández, Maroto, Gonzalo Piedrola, Redón, Josep, Seron, Daniel, Bruchfeld, Annette, Curiac, Dan, Eliasson, Ken, Frank, Malin, Guron, Gregor, Hellberg, Olof, Hellgren, Margareta, Larnefeldt, Hans, Lindholm, Carl-Johan, Löndahl, Magnus, Rein-Hedin, Erik, Soveri, Inga, Spaak, Jonas, Tengmark, Bengt-Olov, Lif-Tiberg, Cornelia, Månflod, Johan, Nguyen, Han, Ackermann, Daniel, Bilz, Stefan, Burnier, Michel, Forster, Christian, Kalbermatter, Stefan, Kistler, Andreas, Pechère-Bertschi, Antoinette, Schultes, Bernd, Laimer, Markus, Rudofsky, Gottfried, Strey, Christopher, Wuerzner, Gregoire, Chiz-Tzung Chang, Hung, Cheng-Chieh, Jiang, Ju-Ying, Lee, Chien-Te, Lin, Shuei-Liong, Tarng, Der-Cherng, Tu, Shih-Te, Wu, Mai-Szu, Wu, Ming-Ju, Chuang, Lee-Ming, Deerochanawong, Chaicharn, Kitiyakara, Chagriya, Ophascharoensuk, Vuddhidej, Pongchaiyakul, Chatlert, Satirapoj, Bancha, Kosachunhanan, Natapong, Sritara, Piyamitr, Eren, Necmi, Gul, Ibrahim, Gulel, Okan, Kocyigit, Ismail, Kumbasar, Abdulbaki, Sahin, Idris, Sari, Ramazan, Sayin, Burak, Tavli, Talat, Ustundag, Sedat, Yenicerioglu, Yavuz, Badak, Ozer, Cayli, Murat, Oguz, Aytekin, Ozdogan, Oner, Sari, Ibrahim, Temizhan, Ahmet, Tigen, Mustafa, Turk, Ugur, Yilmaz, Huseyin, Yilmaz, Mehmet, Bondarets, Iryna, Botsyurko, Volodymyr, Chernikova, Viktoriia, Donets, Oleksandra, Fushtey, Ivan, Grachova, Mariia, Isayeva, Anna, Kogut, Dmytro, Komisarenko, Julia, Kravchun, Nonna, Malyar, Kateryna, Mankovsky, Borys, Martynyuk, Liliya, Maslyanko, Vitaliy, Myshanych, Halyna, Pererva, Larysa, Pertseva, Nataliia, Serhiyenko, Oleksandr, Smirnov, Ivan, Sokolova, Liubov, Stryzhak, Vasyl, Vlasenko, Maryna, Isayeva, Ganna, Larin, Oleksandr, Ahmad AbouSaleh, Barratt, Jonathan, Dang, Cuong, Kahal, Hassan, Kirk, Adam, Kilvert, Anne, Kon, Sui Phi, McCafferty, Kieran, Patel, Dipesh, Rice, Sam, Vijayaraman, Arutchelvam, Wong, Yuk-ki, Gibson, Martin, Wahba, Mona, Zaidi, Reza, Bilous, Rudy, Johnson, Andrew, Kalathil, Dhanya, Kilvert, Anne, Kyriakidou, Christina, Mathew, Amit, Mukhtar, Rasha, Munsoor, Imrozia, Poterajlo, Anton, Swift, Pauline, Idalia Acosta, Adams, Atoya, Adler, Sharon, Ajani, Dilawar, Ali, Slamat, Alicic, Radica, Al-Karadsheh, Amer, Alla, Sreedhara, Allison, D., Andrawis, Nabil, Arif, Ahmed, Awad, Ahmed, Azizad, Masoud, Bahrami, Michael, Bansal, Shweta, Barag, Steven, Barakzoy, Ahmad, Barney, Mark, Barzilay, Joshua, Bashir, Khalid, Bautista, Jose, Beddhu, Srinivasan, Belo, Diogo, Benjamin, Sabrina, Berenji, Ramin, Bhargava, Anuj, Birriel, Jose, Brietzke, Stephen, Brosius, Frank, Brusco, Osvaldo, Burgner, Anna, Busch, Robert, Canadas, Rafael, Caramori, Maria, Cardona, Jose, Case, Christopher, Cruz, Humberto, Dandillaya, Ramprasad, Dawoud, Dalia, Din, Zia, Dixon, Bradley, Doshi, Ankur, Drakakis, James, El Shahawy, Mahfouz, El-Meanawy, Ashraf, El-Shahawy, Mohammed, Evans, John, Fadda, George, Farooq, Umar, Fernando, Roland, Fink, Raymond, First, Brian, Fitz-Patrick, David, Flack, John, Fluck, Patrick, Fogelfeld, Leon, Fonseca, Vivian, Frias, Juan, Galphin, Claude, Garcia-Mayol, Luis, Goldstein, Gary, Gonzalez, Edgar, Gonzalez-Abreu, Francisco, Gore, Ashwini, Grant, David, Habwe, Violet, Hamilton, Maxine, Hammoud, Jamal, Handelsman, Stuart, Hartman, Israel, Heigerick, Glenn, Henry, Andrew, Hernandez, German, Hernandez-Cassis, Carlos, Herrera, Carlos, Hertel, Joachim, Huang, Wenyu, Iglesias, Rogelio, Iranmanesh, Ali, Jackson, Timothy, Jain, Mahendra, Jamerson, Kenneth, Johnson, Karen, Judd, Eric, Kaplan, Joshua, Kayali, Zeid, Khan, Bobby, Khan, Muhammad, Kharait, Sourabh, Kirkman, M. Sue, Kopyt, Nelson, Kotzker, Wayne, Kovesdy, Csaba, Kreit, Camil, Krishna, Arvind, Kronfli, Saeed, Lee, Keung, LeJeune, Derek, Lemus, Brenda, Leon-Forero, Carlos, Linfert, Douglas, Lora, Henry, Lurie, Alexander, Maddukuri, Geetha, Magno, Alexander, Maletz, Louis, Mandayam, Sreedhar, Markell, Mariana, Mayfield, Ronald, Mbogua, Caroline, McMullen, Dierdre, Meisner, Carl, Minton, Stephen, Mocherla, Bharat, Mohandas, Rajesh, Montero, Manuel, Moustafa, Moustafa, Nadkarni, Salil, Nakhle, Samer, Navarro, Jesus, Neyra, Nilda, Nica, Romanita, Nicol, Philip, Norwood, Paul, Numrungroad, Visal, Donovan, Richard O′, Odugbesan, A., Paoli-Bruno, Jorge, Parikh, Samir, Patel, Rakesh, Peixoto, Aldo, Pergola, Pablo, Perlman, Alan, Pettis, Karlton, Pisoni, Roberto, Ponduchi, Mirela, Posada, Jorge, Prabhakar, Sharma, Radhakrishnan, Jai, Rahman, Mahboob, Raina, Rupesh, Rastogi, Anjay, Reisin, Efrain, Rendell, Marc, Robertson, David, Rocco, Michael, Romeu, Hugo, Rosas, Sylvia, Rosenfeld, Jack, Ross, Dennis, Rothman, Jeffrey, Rudolph, Lance, Ruhullah, Yusuf, Ruoff, Gary, Ryu, Jeffrey, Sahani, Mandeep, Sam, Ramin, Samuels, Garfield, Sanchez, William, Santos, Vladimir, Satko, Scott, Saxena, Sanjeev, Scott, David, Seco, Gilberto, Seek, Melvin, Serota, Harvey, Shafi, Tariq, Shahid, Nauman, Shanik, Michael, Sharma, Santosh, Sinha, Arjun, Smelser, James, Smith, Mark, Soe, Kyaw, Solomon, Richard, Soroka, Eugene, Soufer, Joseph, Spinowitz, Bruce, Spry, Leslie, Suarez, Rosa, Subramanian, Bala, Szerlip, Harold, Tamirisa, Aparna, Thomson, Stephen, Tran, Tuan-Huy, Treger, Richard, Trullenque, Gretel, Turk, Thomas, Umpierrez, Guillermo, Urbach, Daniel, Valdes, Martin, Valika, Shujauddin, Vega, Damaris, Weissman, Peter, Whaley-Connell, Adam, Winston, Jonathan, Wise, Jonathan, Wynne, Alan, Zeig, Steven, Abdel-Rahman, Emaad, Abreu, Edel, Awad, Alaa, Bahri, Nader, Bertsch, John, Bleich, David, Bornfreund, Jonathan, Brar, Harjeet, Brian, Susan, Brinson, Cynthia, Bruschetta, Humberto, Carpio, Jose, Cohen, Steven, Cosby, John, Dhanireddy, Soni, Diaz, Jorge, Dunn, Fredrick, Eppanapally, Sabitha, Fayad, Joseph, Goel, Archana, Govindaraju, Kanakadurga, Halpern, Stephen, Jones, Audrey, Kaye, William, Knight, Herbert, Koch, Stanley, Kohli, Nandini, Lastra, Guido, Lerman, Sam, Loredo, Jorge, Lovre, Dragana, Mandviwala, Mustafa, Martin, Earl, Meyer, Jill, Murray, John, Oliver, David, Oparil, Suzanne, Penabad, Jesus, Pereira, Isabel, Popeil, Larry, Quesada, Gonzalo, Ramanathan, Kodangudi, Ramos-Gonez, Luis, Rastegar, Mandana, Rastogi, Padmashri, Rondon, Juan, Roy-Chaudhury, Prabir, Smith, David, Williamson, Don, Womack, Catherine, Yamout, Hala, Yuryev, Michael, Chu, Phuong, Van Hoang, Lam, Khanh, Tran, Phi Nga, Nguyen Thi, Son, Pham Nguyen, Tran, Lan Phuong, Le, Thuy Khuong, Nguyen, Boi Ngoc, Nguyen, Thao, Nui, Nguyen Minh, Nam, Tran Quang, and Tran, Kim Chi

Published
2023
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15. Footprint evidence of early hominin locomotor diversity at Laetoli, Tanzania

Author

McNutt, Ellison J., Hatala, Kevin G., Miller, Catherine, Adams, James, Casana, Jesse, Deane, Andrew S., Dominy, Nathaniel J., Fabian, Kallisti, Fannin, Luke D., Gaughan, Stephen, Gill, Simone V., Gurtu, Josephat, Gustafson, Ellie, Hill, Austin C., Johnson, Camille, Kallindo, Said, Kilham, Benjamin, Kilham, Phoebe, Kim, Elizabeth, Liutkus-Pierce, Cynthia, Maley, Blaine, Prabhat, Anjali, Reader, John, Rubin, Shirley, Thompson, Nathan E., Thornburg, Rebeca, Williams-Hatala, Erin Marie, Zimmer, Brian, Musiba, Charles M., and DeSilva, Jeremy M.

Published
2021
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18. L\'evy NMF for robust nonnegative source separation

Author

Magron, Paul, Badeau, Roland, and Liutkus, Antoine

Subjects
Computer Science - Sound
Abstract

Source separation, which consists in decomposing data into meaningful structured components, is an active research topic in many areas, such as music and image signal processing, applied physics and text mining. In this paper, we introduce the Positive $\alpha$-stable (P$\alpha$S) distributions to model the latent sources, which are a subclass of the stable distributions family. They notably permit us to model random variables that are both nonnegative and impulsive. Considering the L\'evy distribution, the only P$\alpha$S distribution whose density is tractable, we propose a mixture model called L\'evy Nonnegative Matrix Factorization (L\'evy NMF). This model accounts for low-rank structures in nonnegative data that possibly has high variability or is corrupted by very adverse noise. The model parameters are estimated in a maximum-likelihood sense. We also derive an estimator of the sources given the parameters, which extends the validity of the generalized Wiener filtering to the P$\alpha$S case. Experiments on synthetic data show that L\'evy NMF compares favorably with state-of-the art techniques in terms of robustness to impulsive noise. The analysis of two types of realistic signals is also considered: musical spectrograms and fluorescence spectra of chemical species. The results highlight the potential of the L\'evy NMF model for decomposing nonnegative data.

Published
2016

19. Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses

Author

Jokubas Liutkus, Arturas Kriukas, Dominyka Stragyte, Erikas Mazeika, Vidas Raudonis, Wolfgang Galetzka, Andreas Stang, and Skaidra Valiukeviciene

Subjects
melanoma, nevus, seborrheic keratosis, dermatoscopy, artificial intelligence, smartphone, Medicine (General), R5-920
Abstract

Current artificial intelligence algorithms can classify melanomas at a level equivalent to that of experienced dermatologists. The objective of this study was to assess the accuracy of a smartphone-based “You Only Look Once” neural network model for the classification of melanomas, melanocytic nevi, and seborrheic keratoses. The algorithm was trained using 59,090 dermatoscopic images. Testing was performed on histologically confirmed lesions: 32 melanomas, 35 melanocytic nevi, and 33 seborrheic keratoses. The results of the algorithm’s decisions were compared with those of two skilled dermatologists and five beginners in dermatoscopy. The algorithm’s sensitivity and specificity for melanomas were 0.88 (0.71–0.96) and 0.87 (0.76–0.94), respectively. The algorithm surpassed the beginner dermatologists, who achieved a sensitivity of 0.83 (0.77–0.87). For melanocytic nevi, the algorithm outclassed each group of dermatologists, attaining a sensitivity of 0.77 (0.60–0.90). The algorithm’s sensitivity for seborrheic keratoses was 0.52 (0.34–0.69). The smartphone-based “You Only Look Once” neural network model achieved a high sensitivity and specificity in the classification of melanomas and melanocytic nevi with an accuracy similar to that of skilled dermatologists. However, a bigger dataset is required in order to increase the algorithm’s sensitivity for seborrheic keratoses.

Published
2023
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21. Fed-batch Pre-industrial Production and Purification of a Consensus Tetratricopeptide Repeat (CTPR) Scaffold as Container for Fluorescent Proteins (FPs)

Author

Paolo Cinà, Monica Salamone, Salvatrice Rigogliuso, Riccardo Paternostro, Mantas Liutkus, Aitziber L. Cortajarena, and Giulio Ghersi

Subjects
Chemical engineering, TP155-156, Computer engineering. Computer hardware, TK7885-7895
Abstract

White light-emitting diodes (WLEDs) are big news in the field of lighting, however, current production processes are still very expensive or based on unsustainable inorganic metals such as inorganic phosphorus. The EU-funded ARTIBLED project aims to produce low-cost and high-efficiency Bio-hybrid light-emitting diodes (Bio-HLEDs). This can be achieved using artificially synthesized fluorescent proteins linked in biological scaffolds like the packaging to obtain LED for lighting applications containing a biogenic phosphor. This study aims to optimize the protein scaffold CTPR10 production process to obtain a high number of scaffolds with a good purity level for Bio-HLEDs construction. Different fed-batch fermentation procedures were investigated and it was possible to produce more than two times of biomass and intracellular proteins compared to a conventional fed-batch strategy. The improvement in production leads both to the reduction of costs related to the amount of IPTG used and the isolation of a consistent amount of CTPR10 through rapid and highly efficient purification techniques. The realization of this project represents a significant milestone for Europe which will be at the forefront of innovation in the lighting sector.

Published
2022
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22. Reference-less measurement of the transmission matrix of a highly scattering material using a DMD and phase retrieval techniques

Author

Dremeau, Angelique, Liutkus, Antoine, Martina, David, Katz, Ori, Schulke, Christophe, Krzakala, Florent, Gigan, Sylvain, and Daudet, Laurent

Subjects
Physics - Optics
Abstract

This paper investigates experimental means of measuring the transmission matrix (TM) of a highly scattering medium, with the simplest optical setup. Spatial light modulation is performed by a digital micromirror device (DMD), allowing high rates and high pixel counts but only binary amplitude modulation. We used intensity measurement only, thus avoiding the need for a reference beam. Therefore, the phase of the TM has to be estimated through signal processing techniques of phase retrieval. Here, we compare four different phase retrieval principles on noisy experimental data. We validate our estimations of the TM on three criteria : quality of prediction, distribution of singular values, and quality of focusing. Results indicate that Bayesian phase retrieval algorithms with variational approaches provide a good tradeoff between the computational complexity and the precision of the estimates.

Published
2015
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23. Listening to features

Author

Moussallam, Manuel, Liutkus, Antoine, and Daudet, Laurent

Subjects
Computer Science - Sound
Abstract

This work explores nonparametric methods which aim at synthesizing audio from low-dimensionnal acoustic features typically used in MIR frameworks. Several issues prevent this task to be straightforwardly achieved. Such features are designed for analysis and not for synthesis, thus favoring high-level description over easily inverted acoustic representation. Whereas some previous studies already considered the problem of synthesizing audio from features such as Mel-Frequency Cepstral Coefficients, they mainly relied on the explicit formula used to compute those features in order to inverse them. Here, we instead adopt a simple blind approach, where arbitrary sets of features can be used during synthesis and where reconstruction is exemplar-based. After testing the approach on a speech synthesis from well known features problem, we apply it to the more complex task of inverting songs from the Million Song Dataset. What makes this task harder is twofold. First, that features are irregularly spaced in the temporal domain according to an onset-based segmentation. Second the exact method used to compute these features is unknown, although the features for new audio can be computed using their API as a black-box. In this paper, we detail these difficulties and present a framework to nonetheless attempting such synthesis by concatenating audio samples from a training dataset, whose features have been computed beforehand. Samples are selected at the segment level, in the feature space with a simple nearest neighbor search. Additionnal constraints can then be defined to enhance the synthesis pertinence. Preliminary experiments are presented using RWC and GTZAN audio datasets to synthesize tracks from the Million Song Dataset., Comment: Technical Report

Published
2015

27. Real-world use of once-weekly semaglutide in patients with type 2 diabetes: pooled analysis of data from four SURE studies by baseline characteristic subgroups

Author

Thozhukat Sathyapalan, Andrei-Mircea Catarig, Patrick Holmes, Jean-François Yale, Ulrik Bodholdt, Sergiu Catrina, Alice Clark, Neda Rajamand Ekberg, Umut Erhan, Søren Tang Knudsen, Joanne Liutkus, Bernd Schultes, and Gottfried Rudofsky

Subjects
Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Published
2022
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30. Proceedings of the second 'international Traveling Workshop on Interactions between Sparse models and Technology' (iTWIST'14)

Author

Jacques, L., De Vleeschouwer, C., Boursier, Y., Sudhakar, P., De Mol, C., Pizurica, A., Anthoine, S., Vandergheynst, P., Frossard, P., Bilen, C., Kitic, S., Bertin, N., Gribonval, R., Boumal, N., Mishra, B., Absil, P. -A., Sepulchre, R., Bundervoet, S., Schretter, C., Dooms, A., Schelkens, P., Chabiron, O., Malgouyres, F., Tourneret, J. -Y., Dobigeon, N., Chainais, P., Richard, C., Cornelis, B., Daubechies, I., Dunson, D., Dankova, M., Rajmic, P., Degraux, K., Cambareri, V., Geelen, B., Lafruit, G., Setti, G., Determe, J. -F., Louveaux, J., Horlin, F., Drémeau, A., Heas, P., Herzet, C., Duval, V., Peyré, G., Fawzi, A., Davies, M., Gillis, N., Vavasis, S. A., Soussen, C., Magoarou, L. Le, Liang, J., Fadili, J., Liutkus, A., Martina, D., Gigan, S., Daudet, L., Maggioni, M., Minsker, S., Strawn, N., Mory, C., Ngole, F., Starck, J. -L., Loris, I., Vaiter, S., Golbabaee, M., and Vukobratovic, D.

Subjects
Computer Science - Numerical Analysis, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Theory, Computer Science - Learning, Mathematics - Optimization and Control, Mathematics - Statistics Theory
Abstract

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference., Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist14

Published
2014

31. Investigating CALL in the Classroom: Situational Variables to Consider

Author

Liutkus, Darlene

Abstract

A new paradigm in second language pedagogy has Computer Assisted Language Learning (CALL) playing a significant role. Much of the literature to-date claims that CALL can have a positive impact on students' second language acquisition (SLA). Mixed method of research produces data to investigate if CALL positively affects student language proficiency, motivation and autonomy. Classroom observation of participants in their natural environment is a qualitative technique used but has situational variables that could skew results if not structured. A questionnaire is a quantitative tool that can offer insight regarding participants' perception of performance but can contradict what the researcher has observed. This paper will take an in-depth look at variables such as: instructor's pedagogical application; blending CALL into the curriculum; types of CALL implemented; feedback received and their implications for design of the data collection tools.

Published
2012

34. Imaging With Nature: Compressive Imaging Using a Multiply Scattering Medium

Author

Liutkus, Antoine, Martina, David, Popoff, Sébastien, Chardon, Gilles, Katz, Ori, Lerosey, Geoffroy, Gigan, Sylvain, Daudet, Laurent, and Carron, Igor

Subjects
Physics - Optics
Abstract

The recent theory of compressive sensing leverages upon the structure of signals to acquire them with much fewer measurements than was previously thought necessary, and certainly well below the traditional Nyquist-Shannon sampling rate. However, most implementations developed to take advantage of this framework revolve around controlling the measurements with carefully engineered material or acquisition sequences. Instead, we use the natural randomness of wave propagation through multiply scattering media as an optimal and instantaneous compressive imaging mechanism. Waves reflected from an object are detected after propagation through a well-characterized complex medium. Each local measurement thus contains global information about the object, yielding a purely analog compressive sensing method. We experimentally demonstrate the effectiveness of the proposed approach for optical imaging by using a 300-micrometer thick layer of white paint as the compressive imaging device. Scattering media are thus promising candidates for designing efficient and compact compressive imagers., Comment: 17 pages, 8 figures

Published
2013
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42. Multichannel Audio Modeling with Elliptically Stable Tensor Decomposition

Author

Fontaine, Mathieu, Stöter, Fabian-Robert, Liutkus, Antoine, Şimşekli, Umut, Serizel, Romain, Badeau, Roland, 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, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Deville, Yannick, editor, Gannot, Sharon, editor, Mason, Russell, editor, Plumbley, Mark D., editor, and Ward, Dominic, editor

Published
2018
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43. Save the children by treating their mothers (PriVileG-M-study) - study protocol: a sequentially randomized controlled trial of individualized psychotherapy and telemedicine to reduce mental stress in pregnant women and young mothers and to improve Child’s health

Author

M. Bischoff, V. Howland, J. Klinger-König, S. Tomczyk, S. Schmidt, M. Zygmunt, M. Heckmann, N. van den Berg, B. Bethke, J. Corleis, S. Günther, K. Liutkus, U. Stentzel, A. Neumann, P. Penndorf, T. Ludwig, E. Hammer, T. Winter, and H. J. Grabe

Subjects
Maternal mental health, Pregnancy, Intervention, Ecological momentary assessment, Attachment, Cortisol, Psychiatry, RC435-571
Abstract

Abstract Background As early as pregnancy, maternal mental stress impinges on the child’s development and health. Thus, this may cause enhanced risk for premature birth, lowered fetal growth, and lower fetal birth weight as well as enhanced levels of the stress hormone cortisol and lowered levels of the bonding hormone oxytocin. Maternal stress further reduces maternal sensitivity for the child’s needs which impairs the mother-child-interaction and bonding. Therefore, prevention and intervention studies on mental stress are necessary, beginning prenatally and applying rigorous research methodology, such as randomized controlled trials, to ensure high validity. Methods A randomized controlled trial is used to assess the impact of psychotherapy and telemedicine on maternal mental stress and the child’s mental and physical health. Mentally stressed pregnant women are randomized to an intervention (IG) and a not intervened control group. The IG receives an individualized psychotherapy starting prenatal and lasting for 10 months. Afterwards, a second randomization is used to investigate whether the use of telemedicine can stabilize the therapeutic effects. Using ecological momentary assessments and video recordings, the transfer into daily life, maternal sensitivity and mother-child-bonding are assessed. Psycho-biologically, the synchronicity of cortisol and oxytocin levels between mother and child are assessed as well as the peptidome of the colostrum and breast milk, which are assumed to be essential for the adaptation to the extra-uterine environment. All assessments are compared to an additional control group of healthy women. Finally, the results of the study will lead to the development of a qualification measure for health professionals to detect mental stress, to treat it with low-level interventions and to refer those women with high stress levels to mental health professionals. Discussion The study aims to prevent the transgenerational transfer of psychiatric and somatic disorders from the mother to her child. The effects of the psychotherapy will be stabilized through telemedicine and long-term impacts on the child’s and mothers’ mental health are enhanced. The combination of psychotherapy, telemedicine and methodologies of ecological momentary assessment, video recording and bio banking are new in content-related and methodological manner. Trial registration German Clinical Trials Register: DRKS00017065. Registered 02 May 2019. World Health Organization, Universal Trial Number: U1111–1230-9826. Registered 01 April 2019.

Published
2019
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