Search

Your search keyword '"Livi, Lorenzo"' showing total 1,473 results
Start Over Author "Livi, Lorenzo" Publication Year Range Last 10 years
1,473 results on '"Livi, Lorenzo"'

3. Image-guided moderately hypofractionated radiotherapy for localized prostate cancer: a multicentric retrospective study (IPOPROMISE)

Author

Ingrosso, Gianluca, Ponti, Elisabetta, Francolini, Giulio, Caini, Saverio, Fondelli, Simona, Santini, Roberto, Valeriani, Maurizio, Rago, Luciana, Duroni, Giacomo, Bruni, Alessio, Augurio, Antonietta, Tramacere, Francesco, Trippa, Fabio, Russo, Donatella, Bottero, Marta, Tamburo, Maria, Parisi, Silvana, Borghesi, Simona, Lancia, Andrea, Gomellini, Sara, Scoccianti, Silvia, Stefanacci, Marco, Vullo, Gianluca, Statuto, Teodora, Miranda, Giulia, Santo, Bianca, Di Marzo, Alessandro, Bellavita, Rita, Vinciguerra, Annamaria, Livi, Lorenzo, Aristei, Cynthia, Bertini, Niccolò, Orsatti, Carolina, and Detti, Beatrice

Published
2024
Full Text
View/download PDF

7. Graph state-space models

Author

Zambon, Daniele, Cini, Andrea, Livi, Lorenzo, and Alippi, Cesare

Subjects
Computer Science - Machine Learning
Abstract

State-space models constitute an effective modeling tool to describe multivariate time series and operate by maintaining an updated representation of the system state from which predictions are made. Within this framework, relational inductive biases, e.g., associated with functional dependencies existing among signals, are not explicitly exploited leaving unattended great opportunities for effective modeling approaches. The manuscript aims, for the first time, at filling this gap by matching state-space modeling and spatio-temporal data where the relational information, say the functional graph capturing latent dependencies, is learned directly from data and is allowed to change over time. Within a probabilistic formulation that accounts for the uncertainty in the data-generating process, an encoder-decoder architecture is proposed to learn the state-space model end-to-end on a downstream task. The proposed methodological framework generalizes several state-of-the-art methods and demonstrates to be effective in extracting meaningful relational information while achieving optimal forecasting performance in controlled environments.

Published
2023

8. Early biochemical outcomes following PSMA guided approach for bIoCHEmical relapse after prostatectomy-PSICHE trial (NCT05022914): preliminary results

Author

Francolini, Giulio, Ganovelli, Michele, Di Cataldo, Vanessa, Detti, Beatrice, Caini, Saverio, Loi, Mauro, Simontacchi, Gabriele, Desideri, Isacco, Greto, Daniela, Valzano, Marianna, Serni, Sergio, Vaggelli, Luca, Salvestrini, Viola, Visani, Luca, Becherini, Carlotta, Olmetto, Emanuela, Franzese, Ciro, Baldaccini, Davide, Scorsetti, Marta, Sollini, Martina, Chiti, Arturo, Meattini, Icro, Valicenti, Richard K, and Livi, Lorenzo

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Biomedical Imaging, Aging, Clinical Research, Clinical Trials and Supportive Activities, Cancer, Urologic Diseases, Prostate Cancer, Patient Safety, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Male, Humans, Gallium Radioisotopes, Gallium Isotopes, Prostatic Neoplasms, Positron Emission Tomography Computed Tomography, Prospective Studies, Neoplasm Recurrence, Local, Prostatectomy, Prostate-Specific Antigen, Prostate, Radiotherapy, Bladder, Kidney, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

PSICHE (NCT05022914) is a prospective trial to test a [68Ga]Ga- PSMA-11 PET/CT imaging tailored strategy. All evaluable patients had biochemical relapse after surgery and underwent centralized [68Ga]Ga-PSMA-11 PET/CT imaging. The treatment was performed according pre-defined criteria. Observation and re-staging at further PSA progression were proposed to patients with negative PSMA and previous postoperative RT. Prostate bed SRT was proposed to all patients with a negative staging or positive imaging within prostate bed. Stereotactic body radiotherapy (SBRT) to all sites of disease was used for all patients with pelvic nodal recurrence (nodal disease

Published
2023

9. Embedding and trajectories of temporal networks

Author

Thongprayoon, Chanon, Livi, Lorenzo, and Masuda, Naoki

Subjects
Physics - Physics and Society
Abstract

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling, which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days., Comment: 25 pages, 10 figures

Published
2022
Full Text
View/download PDF

10. Stereotactic reirradiation with CyberknifeR for locally recurrent prostate cancer, long-term toxicity and clinical outcomes from a monocentric cohort

Author

Francolini, Giulio, Carnevale, Maria Grazia, Di Cataldo, Vanessa, Loi, Mauro, Detti, Beatrice, Orsatti, Carolina, Caprara, Luisa, Bertini, Niccolò, Lorenzetti, Victoria, Olmetto, Emanuela, Becherini, Carlotta, Visani, Luca, Salvestrini, Viola, Simontacchi, Gabriele, Greto, Daniela, Bonomo, Pierluigi, Doro, Raffaela, Masi, Laura, Desideri, Isacco, Meattini, Icro, Serni, Sergio, and Livi, Lorenzo

Published
2023
Full Text
View/download PDF

13. Pattern of recurrence after stereotactic body radiotherapy for para-aortic oligo-recurrent prostate cancer, a multicentric analysis

Author

Francolini, Giulio, Garlatti, Pietro, Di Cataldo, Vanessa, Triggiani, Luca, Simoni, Nicola, Detti, Beatrice, Lorenzetti, Victoria, Colombo, Federico, Morelli, Vittorio, Ganovelli, Michele, Caprara, Luisa, Orsatti, Carolina, Burchini, Luca, Frosini, Giulio, Bertini, Niccolò, Loi, Mauro, Simontacchi, Gabriele, Greto, Daniela, Desideri, Isacco, Meattini, Icro, and Livi, Lorenzo

Published
2023
Full Text
View/download PDF

14. Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning

Author

Heydari, Sajjad, Raniolo, Stefano, Livi, Lorenzo, and Limongelli, Vittorio

Subjects
Physics - Chemical Physics, Computer Science - Machine Learning, Physics - Biological Physics, Quantitative Biology - Biomolecules
Abstract

Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has use cases in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms has impacted on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, possessing a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy states. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing the potential energy of a conformation, and (ii) novel message passing and pooling layers for processing and making predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable AUC of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the very same transfer learning approach can be used to group, in an unsupervised way, various secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.

Published
2022

15. PERSIAN trial (NCT05717660): an ongoing randomized trial testing androgen deprivation therapy, apalutamide and stereotactic body radiotherapy. An alternative “triplet” for oligometastatic hormone sensitive prostate cancer patients

Author

Francolini, Giulio, Porreca, Angelo, Facchini, Gaetano, Santini, Daniele, Bruni, Alessio, Simoni, Nicola, Trovò, Marco, Osti, Mattia Falchetto, Fornarini, Giuseppe, Sisani, Michele, Di Cataldo, Vanessa, Detti, Beatrice, Garlatti, Pietro, Bertini, Niccolò, Serni, Sergio, Minervini, Andrea, and Livi, Lorenzo

Published
2024
Full Text
View/download PDF

16. Message Passing Neural Networks for Hypergraphs

Author

Heydari, Sajjad and Livi, Lorenzo

Subjects
Computer Science - Machine Learning
Abstract

Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data. We show that the proposed model defines a design space for neural network models for hypergraphs, thus generalizing existing models for hypergraphs. We report experiments on a benchmark dataset for node classification, highlighting the effectiveness of the proposed model with respect to other state-of-the-art methods for graphs and hypergraphs. We also discuss the benefits of using hypergraph representations and, at the same time, highlight the limitation of using equivalent graph representations when the underlying problem has relations among more than two objects.

Published
2022
Full Text
View/download PDF

19. Third national surgical consensus conference of the Italian Association of Breast Surgeons (ANISC) on management after neoadjuvant chemotherapy: The difficulty in reaching a consensus

Author

Zanotti, Simone, Ceccarossi, Virginia, Massaza, Lauretta, Cosentino, Luigi Marino, Amaturo, Giuseppe, Curcio, Annalisa, Bondioli, Paola, Meattini, Icro, Luca, Alessandro De, Garcia-Etienne, Carlos Alberto, Gala, Antonia, Thodas, Alexios, Lozupone, Antonia, Botticella, Maria Antonietta, Grassi, Massimo, Santoro, Valeria, Ala, Ada, Tessa, Cristina La, D'onghia, Giuliano, Carfora, Elisabetta, Cappelli, Sonia, Nunzi, Martina, Mastropietro, Tiziana, Meneghini, Graziano, Morichetti, Doriana, Sanna, Paola Angela, Pozzi, Giada, Soatti, Carlo Pietro, Pellegrini, Alba, Puccica, Ilaria, Frati, Sara, Leone, Alba Di, Giuliani, Giuliana, Conte, Anna Maria, Zamagni, Claudio, Seriau, Luca, Minischetti, Monica, Foroni, Moira, Valli, Mariacarla, Lippi, Andrea, Ravarino, Alberto, Giaccherini, Lucia, Calzolaro, Adele, Neri, Anna, Carli, Luciano, Mambelli, Laura, Prisco, Agnese, Di Marzio, Enrico, Venturini, Annalisa, Gattuso, Maria Ilaria, Sicignano, Margherita, Amabile, Maria Ida, Baldissera, Antonella, Sibilio, Andrea, Chiavilli, Stefano, Gennari, Roberto, Melina, Marica, D'aponte, Maria Luisa, Germano, Alessandro, Baldoni, Giulia, Santurro, Letizia, Busani, Massimo, Bonanni, Alessio, Tondo, Salvatore, Marchetti, Vania, Cassinotti, Michela, Serra, Margherita, Stani, Simonetta, Firpo, Emma, Grendele, Sara, Spolveri, Federico, Custodero, Olindo, Pellegrini, Francesco, Cremonini, Anna, De Tullio, Damiano, Sorbo, Grazia, Combi, Francesca, Aristei, Cynthia, Roncella, Manuela, Folli, Secondo, Nesi, Silvia, Gentile, Damiano, Burlizzi, Stefano, Galimberti, Viviana, Trapani, Anna, Curella, Luciano, Panizza, Pietro, Nanez, Jose Andres Diaz, Sgarella, Adele, Pensa, Anna, Gasperoni, Marco, Marino, Lorenza, Marzetti, Alice, De Rosa, Antonia, Borri, Filippo, Camilletti, Anna Chiara, Ruggieri, Annamaria, Andrulli, Angela Damiana, Silvia, Asioli, Terribile, Daniela Andreina, Accardo, Giuseppe, Patrone, Francesco, Raviele, Paola Rafaniello, Santini, Donatella, Costarelli, Leopoldo, Angelini, Lucia, Reitano, Renato, Polistena, Andrea, Palma, Enza, Santoni, Marcello, Magni, Carla, Cima, Simona, Foglietta, Jennifer, Minafra, Marina, Rizzo, Antonio, Milanesio, Michela Camilla, Giorgini, Sara, Grossi, Simona, Cumbo, Jacopo, Campanella, Barbara, Fante, Rossella, Pinta, Massimo La, Manna, Elena, Santini, Elena, Mascioni, Francesca, Teggi, Silvia, Galasso, Maria Grazia, Sanguinetti, Alessandro, Fenocchio, Daniela, Giacobbe, Felicia, Pellegrini, Alice, Papi, Simona, Pieroni, Marzia, Villa, Elisa, Querzoli, Patrizia, Guadagno, Elia, Perfetti, Alessandra, Balzarotti, Laura, Ciriotto, Davide, Guida, Giovanna, Canova, Elisa, Lunardi, Maria, Vignutelli, Patrizia, Apicella, Giuseppina, Natale, Maria, Ghilli, Matteo, Troilo, Vito Leopoldo, Rocco, Nicola, Schiavone, Alfonso, Ventimiglia, Fabrizio, Zarba Meli, Emanuele, Taffurelli, Mario, Caruso, Francesco, Gentilini, Oreste Davide, Del Mastro, Lucia, Livi, Lorenzo, Castellano, Isabella, Bernardi, Daniela, Minelli, Mauro, and Fortunato, Lucio

Published
2024
Full Text
View/download PDF

20. Learning Graph Cellular Automata

Author

Grattarola, Daniele, Livi, Lorenzo, and Alippi, Cesare

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we focus on a generalised version of typical CA, called graph cellular automata (GCA), in which the lattice structure is replaced by an arbitrary graph. In particular, we extend previous work that used convolutional neural networks to learn the transition rule of conventional CA and we use graph neural networks to learn a variety of transition rules for GCA. First, we present a general-purpose architecture for learning GCA, and we show that it can represent any arbitrary GCA with finite and discrete state space. Then, we test our approach on three different tasks: 1) learning the transition rule of a GCA on a Voronoi tessellation; 2) imitating the behaviour of a group of flocking agents; 3) learning a rule that converges to a desired target state., Comment: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

Published
2021

21. A New Remote Monitor and Control System Based on Sigfox IoT Network

Author

Livi, Lorenzo Francesco and Catani, Jacopo

Subjects
Computer Science - Networking and Internet Architecture, Condensed Matter - Quantum Gases
Abstract

We describe a new, low-cost system designed to provide multi-sensor remote condition monitoring of modern scientific laboratories, as well as to allow users to perform actions from remote locations in case of detection of specified events. The system is battery operated and does not require the presence of a Local Area Network (LAN) or WiFi (which are typically not available in case of, e.g. power losses), as it exploits the growing infrastructure of Internet of Things (IoT) Low Power Wide Area Networks (LPWAN). In particular our system exploits the new SigFox ultra-narrow-bandwidth (UNB) infrastructure, and provides for a bidirectional link between the instrumentation and the remote user even in case of power line outages, which are among the most critical situations that a scientific laboratory can withstand. The system can detect the occurrence of predefined events in very short times, and either autonomously react with a series of predefined actions, also allowing a remote user to timely perform additional actions on the system through an user-friendly smartphone application or via a browser interface. The system also embeds a novel power-loss detection architecture, which detects power line failures in less than 2 ms. We provide a full characterization of the prototype, including reaction times, connection latencies, sensors sensitivity, and power consumption., Comment: Accepted for publication on Review of Scientific Instruments; 8 pages, 5 figures

Published
2021
Full Text
View/download PDF

22. Re-irradiation for recurrent intracranial meningiomas: Analysis of clinical outcomes and prognostic factors

Author

Desideri, Isacco, Morelli, Ilaria, Banini, Marco, Greto, Daniela, Visani, Luca, Nozzoli, Filippo, Caini, Saverio, Della Puppa, Alessandro, Livi, Lorenzo, Perini, Zeno, Zivelonghi, Emanuele, Bulgarelli, Giorgia, Pinzi, Valentina, Navarria, Pierina, Clerici, Elena, Scorsetti, Marta, Ascolese, Anna Maria, Osti, Mattia Falchetto, Anselmo, Paola, Amelio, Dante, Minniti, Giuseppe, and Scartoni, Daniele

Published
2024
Full Text
View/download PDF

23. Cancer-specific dose and fractionation schedules in stereotactic body radiotherapy for oligometastatic disease: An interim analysis of the EORTC-ESTRO E2-RADIatE OligoCare study

Author

Christ, Sebastian M., Alongi, Filippo, Ricardi, Umberto, Scorsetti, Marta, Livi, Lorenzo, Balermpas, Panagiotis, Lievens, Yolande, Braam, Pètra, Jereczek-Fossa, Barbara Alicja, Stellamans, Karin, Ratosa, Ivica, Widder, Joachim, Peulen, Heike, Dirix, Piet, Bral, Samuel, Ramella, Sara, Hemmatazad, Hossein, Khanfir, Kaouthar, Geets, Xavier, Jeene, Paul, Zilli, Thomas, Fournier, Beatrice, Ivaldi, Giovanni Battista, Clementel, Enrico, Fortpied, Catherine, Oppong, Felix Boakye, Ost, Piet, and Guckenberger, Matthias

Published
2024
Full Text
View/download PDF

24. Pembrolizumab With or Without Lenvatinib for First-Line Metastatic NSCLC With Programmed Cell Death-Ligand 1 Tumor Proportion Score of at least 1% (LEAP-007): A Randomized, Double-Blind, Phase 3 Trial

Author

Yang, James Chih-Hsin, Han, Baohui, De La Mora Jiménez, Emmanuel, Lee, Jong-Seok, Koralewski, Piotr, Karadurmus, Nuri, Sugawara, Shunichi, Livi, Lorenzo, Basappa, Naveen S., Quantin, Xavier, Dudnik, Julia, Ortiz, Diego Moran, Mekhail, Tarek, Okpara, Chinyere E., Dutcus, Corina, Zimmer, Zachary, Samkari, Ayman, Bhagwati, Niyati, and Csőszi, Tibor

Published
2024
Full Text
View/download PDF

25. Essential requirements for reporting radiation therapy in breast cancer clinical trials: An international multi-disciplinary consensus endorsed by the European Society for Radiotherapy and Oncology (ESTRO)

Author

Kaidar-Person, Orit, Meattini, Icro, Boersma, Liesbeth J., Becherini, Carlotta, Cortes, Javier, Curigliano, Giuseppe, de Azambuja, Evandro, Harbeck, Nadia, Rugo, Hope S., Del Mastro, Lucia, Gennari, Alessandra, Isacke, Clare M., Vestmø Maraldo, Maja, Marangoni, Elisabetta, Nader Marta, Gustavo, Mjaaland, Ingvil, Salvestrini, Viola, Spanic, Tanja, Visani, Luca, Morandi, Andrea, Lambertini, Matteo, Livi, Lorenzo, Coles, Charlotte E., Poortmans, Philip, and Offersen, Birgitte V.

Published
2024
Full Text
View/download PDF

26. Pembrolizumab plus concurrent chemoradiotherapy versus placebo plus concurrent chemoradiotherapy in patients with locally advanced squamous cell carcinoma of the head and neck (KEYNOTE-412): a randomised, double-blind, phase 3 trial

Author

Rischin, Danny, Hughes, Brett GM, Gao, Bo, McGrath, Margaret, Greil, Richard, Thurnher, Dietmar, Fuereder, Thorsten, Burian, Martin, Rottey, Sylvie, Machiels, Jean-Pascal, Clement, Paul M, Henry, Stéphanie, Deheneffe, Stéphanie, Vasconcelos Alves, Gustavo, Lima, Iane Pinto Figueiredo, Mourão Dias, Josiane, De Marchi, Pedro Rafael Martins, Mak, Milena Perez, Pereira de Santana Gomes, Andrea Juliana, Oliveira de Castro Junior, Dalvaro, Motta, Tatiane Cardoso, Agostinho Padoan, Monica Luciana, Victorina, Ana Paula, de Azevedo, Sergio Jobim, Siu, Lillian L, Brule, Stephanie, Hilton, John, Wang, Chang Shu, Bouganim, Nathaniel, Webster, Marc, Walker, John, Chua, Neil, Zambrano, Angela Regina, Quiroga Echeverri, Alicia, Niño Gomez, Oscar Mauricio, Ortiz, Carlos Alberto, Rojas, Luis, Cardona Zorilla, Andrés, Urrego Meléndez, Olga Marcela, Holečková, Petra Beran, Melichar, Bohuslav, Cvek, Jakub, Prausová, Jana, Vošmik, Milan, Delord, Jean-Pierre, Zasadny, Xavier, Geoffrois, Lionnel, Tao, Yungan, Pointreau, Yoann, Fietkau, Rainer, Haderlein, Marlen, Mueller, Andreas H, Schroeder, Ursula, Wollenberg, Barbara, Laban, Simon, Ivanyi, Philipp, Gruenwald, Viktor, Schafhausen, Philippe, Gutfeld, Orit, Gluck, Iris, Popovtzer, Aron, Meirovitz, Amichay, Billan, Salem, Brenner, Baruch, Limon, Dror, Licitra, Lisa, Perri, Francesco, Caponigro, Francesco, Violati, Martina, Ferrari, Daris, Nole, Franco, Bertolini, Federica, Livi, Lorenzo, Ghi, Maria Grazia, Imarisio, Ilaria, Tahara, Makoto, Homma, Akihiro, Ueda, Tsutomu, Asada, Yukinori, Yamazaki, Tomoko, Matsumoto, Koji, Fujii, Takashi, Ikeda, Sadakatsu, Takahashi, Shunji, Kinoshita, Takashi, Sasaki, Keita, Tsuji, Akihito, Ahn, Myung-Ju, Cho, Byoung Chul, Lee, Keun-Wook, Lee, Ki Hyeong, Choi, Moon Ki, Yun, Hwan Jung, Hendriks, Mathijs P, Oosting, Sjoukje F, Buter, Jan, Van Meerten, Esther, Graham, Jonathan, Kawecki, Andrzej, Debicka, Izabella, Maciejczyk, Adam, Pysz, Maciej, Filarska, Dorota, Hetnał, Marcin, Koralewski, Piotr, Wygoda, Andrzej, Składowski, Krzysztof, Talerczyk, Małgorzata, Berrocal Jaime, Alfonso, Pérez Segura, Pedro, Braña García, Irene, Basté Rotllan, Neus, Mesía Nin, Ricard, Taberna Sanz, Miren, Iglesias Docampo, Lara, Soria Rivas, Ainara, Rueda Domínguez, Antonio, Trigo Pérez, José Manuel, Hong, Ruey-Long, Li, Shau-Hsuan, Wang, Hung-Ming, Yen, Chia-Jui, Yang, Muh-Hwa, Chang, Yi-Fang, Liu, Yi-Chun, Lin, Jin-Ching, Ekenel, Meltem, Harputluoğlu, Hakan, Aksoy, Sercan, Özyilkan, Özgür, Bılıcı, Ahmet, Şendur, Mehmet Alı Nahıt, Arslan, Cagatay, Harrington, Kevin, Ramkumar, Shanmugasundaram, Gujral, Dorothy, Stewart, Simon, Powell, Melanie, Sibtain, Amen, Roques, Tom, Yip, Kent, Mirza, Arafat, Sivaramalingam, Muthiah, Belman, Neil D, Agarwala, Sanjiv, Anderson, Ian, Patel, Arpan, Maggiore, Ronald, Baumgart, Megan, Burtness, Barbara, Fidler, Mary Jo, Kaur, Varinder, Gaughan, Elizabeth, Worden, Francis, Rodriguez, Cristina P, Sukari, Ammar, Wong, Deborah, Yom, Sue, Walsh, William V, Fiorillo, Joseph A, Yorio, Jeffrey T, Obara, Grzegorz S, Alves, Gustavo, Hughes, Brett G M, Mesía, Ricard, Taberna, Miren, Waldron, John N, Simon, Christian, Grégoire, Vincent, Harrington, Kevin J, Swaby, Ramona F, Zhang, Yayan, Gumuscu, Burak, and Bidadi, Behzad

Published
2024
Full Text
View/download PDF

29. Flavour-selective localization in interacting lattice fermions via SU(N) symmetry breaking

Author

Tusi, Daniele, Franchi, Lorenzo, Livi, Lorenzo Francesco, Baumann, Karla, Orenes, Daniel Benedicto, Del Re, Lorenzo, Barfknecht, Rafael Emilio, Zhou, Tianwei, Inguscio, Massimo, Cappellini, Giacomo, Capone, Massimo, Catani, Jacopo, and Fallani, Leonardo

Subjects
Condensed Matter - Quantum Gases, Condensed Matter - Strongly Correlated Electrons, Quantum Physics
Abstract

A large repulsion between particles in a quantum system can lead to their localization, as it happens for the electrons in Mott insulating materials. This paradigm has recently branched out into a new quantum state, the orbital-selective Mott insulator, where electrons in some orbitals are predicted to localize, while others remain itinerant. We provide a direct experimental realization of this phenomenon, that we extend to a more general flavour-selective localization. By using an atom-based quantum simulator, we engineer SU(3) Fermi-Hubbard models breaking their symmetry via a tunable coupling between flavours, observing an enhancement of localization and the emergence of flavour-dependent correlations. Our realization of flavour-selective Mott physics opens the path to the quantum simulation of multicomponent materials, from superconductors to topological insulators., Comment: Main Text: 5 pages, 4 figures. Supplementary Material: 6 pages, 6 figures

Published
2021
Full Text
View/download PDF

30. New quantum simulations with ultracold Ytterbium gases

Author

LIVI, LORENZO FRANCESCO

Subjects
bic Book Industry Communication::P Mathematics & science::PN Chemistry, bic Book Industry Communication::P Mathematics & science::PN Chemistry::PNK Inorganic chemistry
Abstract

In recent years, the unprecedented experimental control of the laws of quantum mechanics has led to the so-called quantum technology revolution, a terminology adopted to refer to the applications in which quantum properties play a prominent role. Also the fundamental physics has benefited from these advancements with the development of quantum simulators, controlled quantum platforms engineered in such a way to emulate the physics of complex systems difficult to investigate with classical approaches. The present work deals with the simulation of some fundamental properties of quantum-Hall systems by means of degenerate fermionic Ytterbium atoms confined in artificial periodic potentials. Extreme regimes, inaccessible to real solid-state system, have been explored, paving the way to the observation of exotic phases of matter.

Published
2019
Full Text
View/download PDF

32. Clinical utility of Next Generation Sequencing of plasma cell-free DNA for the molecular profiling of patients with NSCLC at diagnosis and disease progression

Author

Del Re, Marzia, Luculli, Giovanna Irene, Petrini, Iacopo, Sbrana, Andrea, Scotti, Vieri, Perez, Diego de Miguel, Livi, Lorenzo, Crucitta, Stefania, Iannopollo, Mauro, Mazzoni, Francesca, Ruglioni, Martina, Tibaldi, Carmelo, Olmetto, Emanuela, Stasi, Irene, Baldini, Editta, Allegrini, Giacomo, Antonuzzo, Lorenzo, Morelli, Franco, Pierini, Andrea, Panzeri, Nicola, Fogli, Stefano, Chella, Antonio, Rolfo, Christian, and Danesi, Romano

Published
2024
Full Text
View/download PDF

33. Health-related quality of life in men with oligometastatic prostate cancer following metastases-directed stereotactic body radiotherapy: Real-world data from the E2-RADIatE OligoCare cohort

Author

Bultijnck, Renée, Van Hemelrijck, Mieke, Fonteyne, Valérie, Livi, Lorenzo, Jereczek-Fossa, Barbara Alicja, Hemmatazad, Hossein, Mayinger, Michael, Peulen, Heike, Verbeke, Luc, Ramella, Sara, Castro, Pablo, Tsoutsou, Pelagia, Stellamans, Karin, Shaukat, Adnan, Orazem, Miha, Jeene, Paul, Braam, Pètra, Verkooijen, Helena, Simek, Inga-Malin, Alongi, Filippo, Clementel, Enrico, Fortpied, Catherine, Machingura, Abigirl, Boakye Oppong, Felix, Guckenberger, Matthias, and Ost, Piet

Published
2024
Full Text
View/download PDF

34. International multidisciplinary consensus on the integration of radiotherapy with new systemic treatments for breast cancer: European Society for Radiotherapy and Oncology (ESTRO)-endorsed recommendations

Author

Alkner, Sara, Bhattacharya, Indrani S., Boersma, Liesbeth, Callari, Maurizio, Clarke, Robert B., Del Mastro, Lucia, Ekholm, Maria, Gennari, Alessandra, Kirby, Anna M., Kroeze, Stephanie, Malumbres, Marcos, Vestmø Maraldo, Maja, Marta, Gustavo Nader, Mjaaland, Ingvil, Morgan, Gilberto, Pistilli, Barbara, Paluch-Shimon, Shani, Rivera, Sofia, Rottenberg, Sven, Saura, Cristina, Skyttä, Tanja, Spanic, Tanja, Meattini, Icro, Becherini, Carlotta, Caini, Saverio, Coles, Charlotte E, Cortes, Javier, Curigliano, Giuseppe, de Azambuja, Evandro, Isacke, Clare M, Harbeck, Nadia, Kaidar-Person, Orit, Marangoni, Elisabetta, Offersen, Birgitte V, Rugo, Hope S, Salvestrini, Viola, Visani, Luca, Morandi, Andrea, Lambertini, Matteo, Poortmans, Philip, and Livi, Lorenzo

Published
2024
Full Text
View/download PDF

37. Learn to Synchronize, Synchronize to Learn

Author

Verzelli, Pietro, Alippi, Cesare, and Livi, Lorenzo

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Nonlinear Sciences - Chaotic Dynamics
Abstract

In recent years, the machine learning community has seen a continuous growing interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks we have the Reservoir Computing (RC) paradigm characterized by conceptual simplicity and a fast training scheme. Yet, the guiding principles under which RC operates are only partially understood. In this work, we analyze the role played by Generalized Synchronization (GS) when training a RC to solve a generic task. In particular, we show how GS allows the reservoir to correctly encode the system generating the input signal into its dynamics. We also discuss necessary and sufficient conditions for the learning to be feasible in this approach. Moreover, we explore the role that ergodicity plays in this process, showing how its presence allows the learning outcome to apply to multiple input trajectories. Finally, we show that satisfaction of the GS can be measured by means of the Mutual False Nearest Neighbors index, which makes effective to practitioners theoretical derivations.

Published
2020
Full Text
View/download PDF

38. Input-to-State Representation in linear reservoirs dynamics

Author

Verzelli, Pietro, Alippi, Cesare, Livi, Lorenzo, and Tino, Peter

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning, Mathematics - Dynamical Systems
Abstract

Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is not trained (e.g., via gradient descent), making them appealing for analytical studies by a large community of researchers with backgrounds spanning from dynamical systems to neuroscience. However, even in the simple linear case, the working principle of these networks is not fully understood and their design is usually driven by heuristics. A novel analysis of the dynamics of such networks is proposed, which allows the investigator to express the state evolution using the controllability matrix. Such a matrix encodes salient characteristics of the network dynamics; in particular, its rank represents an input-indepedent measure of the memory capacity of the network. Using the proposed approach, it is possible to compare different reservoir architectures and explain why a cyclic topology achieves favourable results as verified by practitioners.

Published
2020

39. The Echo Index and multistability in input-driven recurrent neural networks

Author

Ceni, Andrea, Ashwin, Peter, Livi, Lorenzo, and Postlethwaite, Claire

Subjects
Mathematics - Dynamical Systems
Abstract

A recurrent neural network (RNN) possesses the echo state property (ESP) if, for a given input sequence, it ``forgets'' any internal states of the driven (nonautonomous) system and asymptotically follows a unique, possibly complex trajectory. The lack of ESP is conventionally understood as a lack of reliable behaviour in RNNs. Here, we show that RNNs can reliably perform computations under a more general principle that accounts only for their local behaviour in phase space. To this end, we formulate a generalisation of the ESP and introduce an echo index to characterise the number of simultaneously stable responses of a driven RNN. We show that it is possible for the echo index to change with inputs, highlighting a potential source of computational errors in RNNs due to characteristics of the inputs driving the dynamics., Comment: Revised version, 43 pages, 6 figures

Published
2020
Full Text
View/download PDF

40. Recurrence Quantification Analysis of Dynamic Brain Networks

Author

Lopes, Marinho A., Zhang, Jiaxiang, Krzemiński, Dominik, Hamandi, Khalid, Chen, Qi, Livi, Lorenzo, and Masuda, Naoki

Subjects
Quantitative Biology - Neurons and Cognition, Condensed Matter - Disordered Systems and Neural Networks
Abstract

Evidence suggests that brain network dynamics is a key determinant of brain function and dysfunction. Here we propose a new framework to assess the dynamics of brain networks based on recurrence analysis. Our framework uses recurrence plots and recurrence quantification analysis to characterize dynamic networks. For resting-state magnetoencephalographic dynamic functional networks (dFNs), we have found that functional networks recur more quickly in people with epilepsy than healthy controls. This suggests that recurrence of dFNs may be used as a biomarker of epilepsy. For stereo electroencephalography data, we have found that dFNs involved in epileptic seizures emerge before seizure onset, and recurrence analysis allows us to detect seizures. We further observe distinct dFNs before and after seizures, which may inform neurostimulation strategies to prevent seizures. Our framework can also be used for understanding dFNs in healthy brain function and in other neurological disorders besides epilepsy., Comment: 77 pages, 11 figures; note: the acknowledgments section is the most complete in this arxiv version (compared to the published version in EJN)

Published
2020
Full Text
View/download PDF

41. Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

Author

Bianchi, Filippo Maria, Grattarola, Daniele, Livi, Lorenzo, and Alippi, Cesare

Subjects
Computer Science - Machine Learning, Mathematics - Spectral Theory, Statistics - Machine Learning
Abstract

In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations. In this work, we propose the Node Decimation Pooling (NDP), a pooling operator for GNNs that generates coarser graphs while preserving the overall graph topology. During training, the GNN learns new node representations and fits them to a pyramid of coarsened graphs, which is computed offline in a pre-processing stage. NDP consists of three steps. First, a node decimation procedure selects the nodes belonging to one side of the partition identified by a spectral algorithm that approximates the \maxcut{} solution. Afterwards, the selected nodes are connected with Kron reduction to form the coarsened graph. Finally, since the resulting graph is very dense, we apply a sparsification procedure that prunes the adjacency matrix of the coarsened graph to reduce the computational cost in the GNN. Notably, we show that it is possible to remove many edges without significantly altering the graph structure. Experimental results show that NDP is more efficient compared to state-of-the-art graph pooling operators while reaching, at the same time, competitive performance on a significant variety of graph classification tasks.

Published
2019
Full Text
View/download PDF

42. Tucatinib's journey from clinical development to clinical practice: New horizons for HER2-positive metastatic disease and promising prospects for brain metastatic spread

Author

Criscitiello, Carmen, Corti, Chiara, De Laurentiis, Michelino, Bianchini, Giampaolo, Pistilli, Barbara, Cinieri, Saverio, Castellan, Lucio, Arpino, Grazia, Conte, Pierfranco, Di Meco, Francesco, Gennari, Alessandra, Guarneri, Valentina, Visani, Luca, Livi, Lorenzo, Marchetti, Paolo, Puglisi, Fabio, Viale, Giuseppe, Del Mastro, Lucia, De Placido, Sabino, and Curigliano, Giuseppe

Published
2023
Full Text
View/download PDF

44. Safety profile of cyclin-dependent kinase (CDK) 4/6 inhibitors with concurrent radiation therapy: A systematic review and meta-analysis

Author

Becherini, Carlotta, Visani, Luca, Caini, Saverio, Bhattacharya, Indrani S., Kirby, Anna M., Nader Marta, Gustavo, Morgan, Gilberto, Salvestrini, Viola, Coles, Charlotte E., Cortes, Javier, Curigliano, Giuseppe, de Azambuja, Evandro, Harbeck, Nadia, Isacke, Clare M., Kaidar-Person, Orit, Marangoni, Elisabetta, Offersen, Birgitte, Rugo, Hope S., Morandi, Andrea, Lambertini, Matteo, Poortmans, Philip, Livi, Lorenzo, and Meattini, Icro

Published
2023
Full Text
View/download PDF

45. Safety profile of trastuzumab-emtansine (T-DM1) with concurrent radiation therapy: A systematic review and meta-analysis

Author

Salvestrini, Viola, Kim, Kyubo, Caini, Saverio, Alkner, Sara, Ekholm, Maria, Skyttä, Tanja, Becherini, Carlotta, Coles, Charlotte E., Kaidar-Person, Orit, Offersen, Birgitte, de Azambuja, Evandro, Visani, Luca, Cortes, Javier, Harbeck, Nadia, Rugo, Hope S., Isacke, Clare M., Marangoni, Elisabetta, Morandi, Andrea, Lambertini, Matteo, Poortmans, Philip, Livi, Lorenzo, and Meattini, Icro

Published
2023
Full Text
View/download PDF

50. Impact of stereotactic body radiotherapy vs palliative radiotherapy on oncologic outcomes of patients with metastatic kidney cancer concomitantly treated with immune checkpoint inhibitors: a preliminary, multicentre experience

Author

Francolini, Giulio, Campi, Riccardo, Di Cataldo, Vanessa, Detti, Beatrice, Loi, Mauro, Triggiani, Luca, La Mattina, Salvatore, Borghetti, Paolo, Magrini, Stefano Maria, Nicosia, Luca, Alongi, Filippo, Ghirardelli, Paolo, Vavassori, Vittorio, Allegra, Andrea Gaetano, Aquilano, Michele, Scoccimarro, Erika, Peruzzi, Anna, Pastina, Pierpaolo, Visani, Luca, Desideri, Isacco, Serni, Sergio, Meattini, Icro, and Livi, Lorenzo

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results