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2,997 results on '"Chiò A"'

1. SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds

Author

Hou, Zhixing, Gao, Maoxu, Yu, Hang, Yang, Mengyu, and Ieong, Chio-In

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

This paper introduces a Spiking Diffusion Policy (SDP) learning method for robotic manipulation by integrating Spiking Neurons and Learnable Channel-wise Membrane Thresholds (LCMT) into the diffusion policy model, thereby enhancing computational efficiency and achieving high performance in evaluated tasks. Specifically, the proposed SDP model employs the U-Net architecture as the backbone for diffusion learning within the Spiking Neural Network (SNN). It strategically places residual connections between the spike convolution operations and the Leaky Integrate-and-Fire (LIF) nodes, thereby preventing disruptions to the spiking states. Additionally, we introduce a temporal encoding block and a temporal decoding block to transform static and dynamic data with timestep $T_S$ into each other, enabling the transmission of data within the SNN in spike format. Furthermore, we propose LCMT to enable the adaptive acquisition of membrane potential thresholds, thereby matching the conditions of varying membrane potentials and firing rates across channels and avoiding the cumbersome process of manually setting and tuning hyperparameters. Evaluating the SDP model on seven distinct tasks with SNN timestep $T_S=4$, we achieve results comparable to those of the ANN counterparts, along with faster convergence speeds than the baseline SNN method. This improvement is accompanied by a reduction of 94.3\% in dynamic energy consumption estimated on 45nm hardware.

Published
2024

2. Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression

Author

Marinello, Elena, Tavazzi, Erica, Longato, Enrico, Bosoni, Pietro, Dagliati, Arianna, Vazifehdan, Mahin, Bellazzi, Riccardo, Trescato, Isotta, Guazzo, Alessandro, Vettoretti, Martina, Tavazzi, Eleonora, Ahmad, Lara, Bergamaschi, Roberto, Cavalla, Paola, Manera, Umberto, Chio, Adriano, and Di Camillo, Barbara

Subjects
Computer Science - Machine Learning
Abstract

Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project. We employed predictive models, including Random Forest (RF) and Logistic Regression (LR), with varying sets of input features to predict the occurrence of relapses based on clinical and pollutant data collected over a week. The RF yielded the best result, with an AUC-ROC score of 0.713. Environmental variables, such as precipitation, NO2, PM2.5, humidity, and temperature, were found to be relevant to the prediction.

Published
2024

3. FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba

Author

Xie, Xinyu, Cui, Yawen, Ieong, Chio-In, Tan, Tao, Zhang, Xiaozhi, Zheng, Xubin, and Yu, Zitong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multi-modal image fusion aims to combine information from different modes to create a single image with comprehensive information and detailed textures. However, fusion models based on convolutional neural networks encounter limitations in capturing global image features due to their focus on local convolution operations. Transformer-based models, while excelling in global feature modeling, confront computational challenges stemming from their quadratic complexity. Recently, the Selective Structured State Space Model has exhibited significant potential for long-range dependency modeling with linear complexity, offering a promising avenue to address the aforementioned dilemma. In this paper, we propose FusionMamba, a novel dynamic feature enhancement method for multimodal image fusion with Mamba. Specifically, we devise an improved efficient Mamba model for image fusion, integrating efficient visual state space model with dynamic convolution and channel attention. This refined model not only upholds the performance of Mamba and global modeling capability but also diminishes channel redundancy while enhancing local enhancement capability. Additionally, we devise a dynamic feature fusion module (DFFM) comprising two dynamic feature enhancement modules (DFEM) and a cross modality fusion mamba module (CMFM). The former serves for dynamic texture enhancement and dynamic difference perception, whereas the latter enhances correlation features between modes and suppresses redundant intermodal information. FusionMamba has yielded state-of-the-art (SOTA) performance across various multimodal medical image fusion tasks (CT-MRI, PET-MRI, SPECT-MRI), infrared and visible image fusion task (IR-VIS) and multimodal biomedical image fusion dataset (GFP-PC), which is proved that our model has generalization ability. The code for FusionMamba is available at https://github.com/millieXie/FusionMamba.

Published
2024

5. Prevalence and motor-functional correlates of frontotemporal-spectrum disorders in a large cohort of non-demented ALS patients

Author

Poletti, Barbara, Aiello, Edoardo Nicolò, Consonni, Monica, Iazzolino, Barbara, Torre, Silvia, Solca, Federica, Faltracco, Veronica, Telesca, Alessandra, Palumbo, Francesca, Dalla Bella, Eleonora, Bersano, Enrica, Riva, Nilo, Verde, Federico, Messina, Stefano, Doretti, Alberto, Maranzano, Alessio, Morelli, Claudia, Calvo, Andrea, Silani, Vincenzo, Lauria, Giuseppe, Chiò, Adriano, and Ticozzi, Nicola

Published
2024
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6. Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication

Author

Gianinazzi, Lukas, Ziogas, Alexandros Nikolaos, Huang, Langwen, Luczynski, Piotr, Ashkboos, Saleh, Scheidl, Florian, Carigiet, Armon, Ge, Chio, Abubaker, Nabil, Besta, Maciej, Ben-Nun, Tal, and Hoefler, Torsten

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, F.2.1
Abstract

We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, data transfer becomes a bottleneck. An approach based on dense matrix multiplication algorithms leads to suboptimal scalability and fails to exploit the sparsity in the problem. To address these challenges, we propose decomposing the sparse matrix into a small number of highly structured matrices called arrow matrices, which are connected by permutations. Our approach enables communication-avoiding multiplications, achieving a polynomial reduction in communication volume per iteration for matrices corresponding to planar graphs and other minor-excluded families of graphs. Our evaluation demonstrates that our approach outperforms a state-of-the-art method for sparse matrix multiplication on matrices with hundreds of millions of rows, offering near-linear strong and weak scaling.

Published
2024
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8. Multi-Task End-to-End Training Improves Conversational Recommendation

Author

Ram, Naveen, Kuzmin, Dima, Chio, Ellie Ka In, Alzantot, Moustafa Farid, Ontanon, Santiago, Jash, Ambarish, and Li, Judith Yue

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue. While previous works in this area adopt complex multi-component approaches where the dialogue management and entity recommendation tasks are handled by separate components, we show that a unified transformer model, based on the T5 text-to-text transformer model, can perform competitively in both recommending relevant items and generating conversation dialogue. We fine-tune our model on the ReDIAL conversational movie recommendation dataset, and create additional training tasks derived from MovieLens (such as the prediction of movie attributes and related movies based on an input movie), in a multitask learning setting. Using a series of probe studies, we demonstrate that the learned knowledge in the additional tasks is transferred to the conversational setting, where each task leads to a 9%-52% increase in its related probe score., Comment: 10 pages, 4 tables, 1 figure

Published
2023

9. Mechanism-free repurposing of drugs for C9orf72-related ALS/FTD using large-scale genomic data

Author

Baloh, Robert H., Bowser, Robert, Brady, Christopher B., Brice, Alexis, Broach, James, Camu, William, Chia, Ruth, Chio, Adriano, Cooper-Knock, John, Cusi, Daniele, Ding, Jinhui, Drepper, Carsten, Drory, Vivian E., Dunckley, Travis L., Feldman, Eva, Floeter, Mary Kay, Fratta, Pietro, Gerhard, Glenn, Gibbs, J. Raphael, Gibson, Summer B., Glass, Jonathan D., Goutman, Stephen A., Hardy, John, Harms, Matthew B., Heiman-Patterson, Terry D., Jansson, Lilja, Kirby, Janine, Laaksovirta, Hannu, Landers, John E., Landi, Francesco, Le Ber, Isabelle, Lumbroso, Serge, Guissart, Claire, MacGowan, Daniel JL., Maragakis, Nicholas J., Mora, Gabriele, Mouzat, Kevin, Myllykangas, Liisa, Orrell, Richard W., Ostrow, Lyle W., Pickering-Brown, Stuart, Pioro, Erik P., Pulst, Stefan M., Ravits, John M., Renton, Alan E., Robberecht, Wim, Rogaeva, Ekaterina, Rothstein, Jeffrey D., Salvi, Erika, Scholz, Sonja W., Sendtner, Michael, Shaw, Pamela J., Sidle, Katie C., Simmons, Zachary, Stone, David J., Tienari, Pentti J., Traynor, Bryan J., Trojanowski, John Q., Troncoso, Juan C., Valori, Miko, Van Damme, Philip, Van Deerlin, Vivianna M., Van Den Bosch, Ludo, Zinman, Lorne, Angelocola, Stefania M., Ausiello, Francesco P., Barberis, Marco, Bartolomei, Ilaria, Battistini, Stefania, Bersano, Enrica, Bisogni, Giulia, Borghero, Giuseppe, Brunetti, Maura, Cabona, Corrado, Calvo, Andrea, Canale, Fabrizio, Canosa, Antonio, Cantisani, Teresa A., Capasso, Margherita, Caponnetto, Claudia, Cardinali, Patrizio, Carrera, Paola, Casale, Federico, Colletti, Tiziana, Conforti, Francesca L., Conte, Amelia, Conti, Elisa, Corbo, Massimo, Cuccu, Stefania, Bella, Eleonora Dalla, D'Errico, Eustachio, DeMarco, Giovanni, Dubbioso, Raffaele, Ferrarese, Carlo, Ferraro, Pilar M., Filippi, Massimo, Fini, Nicola, Floris, Gianluca, Fuda, Giuseppe, Gallone, Salvatore, Gianferrari, Giulia, Giannini, Fabio, Grassano, Maurizio, Greco, Lucia, Iazzolino, Barbara, Introna, Alessandro, La Bella, Vincenzo, Lattante, Serena, Lauria, Giuseppe, Liguori, Rocco, Logroscino, Giancarlo, Logullo, Francesco O., Lunetta, Christian, Mandich, Paola, Mandrioli, Jessica, Manera, Umberto, Manganelli, Fiore, Marangi, Giuseppe, Marinou, Kalliopi, Marrosu, Maria Giovanna, Martinelli, Ilaria, Messina, Sonia, Moglia, Cristina, Monsurrò, Maria Rosaria, Mosca, Lorena, Murru, Maria R., Origone, Paola, Passaniti, Carla, Petrelli, Cristina, Petrucci, Antonio, Pirisi, Angelo, Pozzi, Susanna, Pugliatti, Maura, Quattrini, Angelo, Ricci, Claudia, Riolo, Giulia, Riva, Nilo, Russo, Massimo, Sabatelli, Mario, Salamone, Paolina, Salivetto, Marco, Salvi, Fabrizio, Santarelli, Marialuisa, Sbaiz, Luca, Sideri, Riccardo, Simone, Isabella, Simonini, Cecilia, Spataro, Rossella, Tanel, Raffaella, Tedeschi, Gioacchino, Ticca, Anna, Torriello, Antonella, Tranquilli, Stefania, Tremolizzo, Lucio, Trojsi, Francesca, Vasta, Rosario, Vacchiano, Veria, Vita, Giuseppe, Volanti, Paolo, Zollino, Marcella, Zucchi, Elisabetta, Silani, Vincenzo, Fogh, Isabella, Ticozzi, Nicola, Ratti, Antonia, Tiloca, Cinzia, Peverelli, Silvia, Gellera, Cinzia, Pinter, Giuseppe Lauria, Taroni, Franco, Pensato, Viviana, Castellotti, Barbara, Comi, Giacomo P., Corti, Stefania, Del Bo, Roberto, Cereda, Cristina, Ceroni, Mauro, Gagliardi, Stella, Corrado, Lucia, Mazzini, Letizia, Sorarù, Gianni, Raggi, Flavia, Siciliano, Gabriele, Simoncini, Costanza, Lo Gerfo, Annalisa, Filosto, Massimiliano, Inghilleri, Maurizio, Ferlini, Alessandra, Corcia, Philippe, Couratier, Philippe, Vourc'h, Patrick, Hardiman, Orla, McLaughlin, Russell, Gotkine, Marc, Drory, Vivian, van den Veldink, Jan H., Berg, Leonard H., de Carvalho, Mamede, Mora Pardina, Jesus S., Povedano, Monica, Andersen, Peter, Weber, Markus, Başak, Ayşe Nazlı, Al-Chalabi, Ammar, Shaw, Chris, Morrison, Karen E., Adeleye, Adelani, Alba, Camille, Bacikova, Dagmar, Dalgard, Clifton L., Hupalo, Daniel N., McGrath Martinez, Elisa, Soltis, Anthony R., Sukumar, Gauthaman, Viollet, Coralie, Wilkerson, Matthew D., Saez-Atienzar, Sara, Souza, Cleide dos Santos, Beal, Selina N., Lorenzini, Ileana, Huang, Ruili, Levy, Jennifer, Burciu, Camelia, Jones, Ashley, Dewan, Ramita, van Vugt, Joke J.F.A., van Rheenen, Wouter, Tunca, Ceren, Bayraktar, Elif, Xia, Menghang, Iacoangeli, Alfredo, Shatunov, Aleksey, Verde, Federico, Kenna, Kevin, Al Khleifat, Ahmad, Opie-Martin, Sarah, Piccinelli, Stefano Cotti, Padovani, Alessandro, Galimberti, Daniela, Serpente, Maria, Fenoglio, Chiara, Scarpini, Elio, Curtis, Charles J., Lee, Sang Hyuck, Chung, Raymond, Patel, Hamel, Cooper-Knock, Johnathan, Breen, Gerome, Dobson, Richard J.B., van den Berg, Leonard H., D’Alfonso, Sandra, Chandran, Siddharthan, Pal, Suvankar, Johnson, Kory, Doucet-O’Hare, Tara, Pasternack, Nicholas, Wang, Tongguang, Nath, Avindra, Veldink, Jan H., Chiò, Adriano, Sattler, Rita, Shaw, Christopher E., and Ferraiuolo, Laura

Published
2024
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10. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2024.

Author

Giovanni Birolo, Pietro Bosoni, Guglielmo Faggioli, Helena Aidos, Roberto Bergamaschi, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel García Dominguez, Marta Gromicho, Alessandro Guazzo, Enrico Longato, Sara C. Madeira, Umberto Manera, Stefano Marchesin 0001, Laura Menotti, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Isotta Trescato, Martina Vettoretti, Barbara Di Camillo, and Nicola Ferro 0001

Published
2024
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11. Overview of iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge.

Author

Giovanni Birolo, Pietro Bosoni, Guglielmo Faggioli, Helena Aidos, Roberto Bergamaschi, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel García Dominguez, Marta Gromicho, Alessandro Guazzo, Enrico Longato, Sara C. Madeira, Umberto Manera, Stefano Marchesin 0001, Laura Menotti, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Isotta Trescato, Martina Vettoretti, Barbara Di Camillo, and Nicola Ferro 0001

Published
2024

13. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2024

Author

Birolo, Giovanni, Bosoni, Pietro, Faggioli, Guglielmo, Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Piero, Dominguez, Jose Manuel García, Gromicho, Marta, Guazzo, Alessandro, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Marchesin, Stefano, Menotti, Laura, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Trescato, Isotta, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goeuriot, Lorraine, editor, Mulhem, Philippe, editor, Quénot, Georges, editor, Schwab, Didier, editor, Di Nunzio, Giorgio Maria, editor, Soulier, Laure, editor, Galuščáková, Petra, editor, García Seco de Herrera, Alba, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2024
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14. iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge

Author

Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, Di Camillo, Barbara, de Carvalho, Mamede Alves, Ferro, Nicola, Fariselli, Piero, Dominguez, Jose Manuel García, Madeira, Sara C., Tavazzi, Eleonora, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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15. Fractal dimension, approximation and data sets

Author

Betti, L., Chio, I., Fleischman, J., Iosevich, A., Iulianelli, F., Kirila, S., Martino, M., Mayeli, A., Pack, S., Sheng, Z., Taliancic, C., Thomas, A., Whybra, N., Wyman, E., Yildirim, U., and Zhao, K.

Subjects
Mathematics - Statistics Theory, Mathematics - Classical Analysis and ODEs, 28A75, 62R07
Abstract

The purpose of this paper is to study the fractal phenomena in large data sets and the associated questions of dimension reduction. We examine situations where the classical Principal Component Analysis is not effective in identifying the salient underlying fractal features of the data set. Instead, we employ the discrete energy, a technique borrowed from geometric measure theory, to limit the number of points of a given data set that lie near a $k$-dimensional hyperplane, or, more generally, near a set of a given upper Minkowski dimension. Concrete motivations stemming from naturally arising data sets are described and future directions outlined.

Published
2022

17. Predictors for progression in amyotrophic lateral sclerosis associated to SOD1 mutation: insight from two population-based registries

Author

Martinelli, Ilaria, Ghezzi, Andrea, Zucchi, Elisabetta, Gianferrari, Giulia, Ferri, Laura, Moglia, Cristina, Manera, Umberto, Solero, Luca, Vasta, Rosario, Canosa, Antonio, Grassano, Maurizio, Brunetti, Maura, Mazzini, Letizia, De Marchi, Fabiola, Simonini, Cecilia, Fini, Nicola, Vinceti, Marco, Pinti, Marcello, Chiò, Adriano, Calvo, Andrea, and Mandrioli, Jessica

Published
2023
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18. Resting‐state fMRI functional connectome of C9orf72 mutation status

Author

Mario Stanziano, Davide Fedeli, Umberto Manera, Stefania Ferraro, Jean P. Medina Carrion, Sara Palermo, Paola Sciortino, Maurizio Cogoni, Federica Agosta, Silvia Basaia, Massimo Filippi, Marina Grisoli, Maria C. Valentini, Filippo De Mattei, Antonio Canosa, Andrea Calvo, Maria G. Bruzzone, Adriano Chiò, Anna Nigri, and Cristina Moglia

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objective The resting‐state functional connectome has not been extensively investigated in amyotrophic lateral sclerosis (ALS) spectrum disease, in particular in relationship with patients' genetic status. Methods Here we studied the network‐to‐network connectivity of 19 ALS patients carrying the C9orf72 hexanucleotide repeat expansion (C9orf72+), 19 ALS patients not affected by C9orf72 mutation (C9orf72−), and 19 ALS‐mimic patients (ALSm) well‐matched for demographic and clinical variables. Results When compared with ALSm, we observed greater connectivity of the default mode and frontoparietal networks with the visual network for C9orf72+ patients (P = 0.001). Moreover, the whole‐connectome showed greater node degree (P

Published
2024
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19. Electrically Conductive 2D Material Coatings for Flexible & Stretchable Electronics: A Comparative Review of Graphenes & MXenes

Author

Mercadillo, Vicente Orts, Chan, Kai Chio, Caironi, Mario, Athanassiou, Athanassia, Kinloch, Ian A., Bissett, Mark, and Cataldi, Pietro

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

There is growing interest in transitioning electronic components and circuitry from stiff and rigid substrates to more flexible and stretchable platforms, such as thin plastics, textiles, and foams. In parallel, the push for more sustainable, biocompatible, and cost-efficient conductive inks to coat these substrates, has led to the development of formulations with novel nanomaterials. Among these, 2D materials, and particularly graphenes and MXenes, have received intense research interest due to their increasingly facile and scalable production, high electrical conductivity, and compatibility with existing manufacturing techniques. They enable a range of electronic devices, including strain and pressure sensors, supercapacitors, thermoelectric generators, and heaters. These new flexible and stretchable electronic devices developed with 2D material coatings are poised to unlock exciting applications in the wearable, healthcare and Internet of Things sectors. This review has surveyed key data from more than 200 articles published over the last 6 years, to provide a quantitative analysis of recent progress in the field and shade light on future directions and prospects of this technology. We find that despite the different chemical origins of graphenes and MXenes, their shared electrical properties and 2D morphology, guarantee intriguing performance in end applications, leaving plenty of space for shared progress and advancements in the future.

Published
2022
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20. A comparative study of the MACD-base trading strategies: evidence from the US stock market

Author

Chio, Pat Tong

Subjects
Quantitative Finance - Portfolio Management, Quantitative Finance - General Finance
Abstract

In recent years, more and more investors use technical analysis methods in their own trading. Evaluating the effectiveness of technical analysis has become more feasible due to increasing computing capability and blooming public data, which indie investors can perform stock analysis and backtest their own trading strategy conveniently. The Moving Average Convergence Divergence (MACD) indicator is one of the popular technical indicators that are widely used in different strategies. In order to verify the MACD effectiveness, in this thesis, I use the MACD indicator with traditional parameters (12, 26, 9) to build various trading strategies. Then, I apply these strategies to stocks listed on three indices in the US stock market (i.e., Dow-Jones, Nasdaq, and S&P 500) and evaluate its performance in terms of win rate, profitability, Sharpe ratio, number of trades and maximum drawdown. The backtesting is programmed using Python, covering the period between 01/01/2015 and 28-08-2021. The result shows that the win-rate of the strategy with only the MACD indicator is less than 50%. However, the win-rate is improved for the trading strategies that combine the MACD indicator with other momentum indicators like the Money Flow Index (MFI) and the Relative Strength Index (RSI). Based on this result, I redesign the MACD mathematical formula by taking the trading volume and daily price volatility into consideration to derive a new indicator called VPVMA. The results show that the win-rate and risk-adjust performance of this new trading strategy have been improved significantly. In general, the findings suggest that while all the MACD trading strategies mentioned above can generate positive returns, the performance is not good without using other momentum indicators. Hence, the VPVMA indicator performs better.

Published
2022

23. Potential immunomodulatory effects of CAS+IMD monoclonal antibody cocktail in hospitalized patients with COVID-19

Author

Wang, Bei, Golubov, Jacquelynn, Oswald, Erin M., Poon, Patrick, Wei, Qiaozhi, Lett, Clarissa, Shehadeh, Fadi, Kaczynski, Matthew, Felix, Lewis Oscar, Mishra, Biswajit, Mylona, Evangelia K., Wipperman, Matthew F., Chio, Erica, Hamon, Sara C., Hooper, Andrea T., Somersan-Karakaya, Selin, Musser, Bret J., Petro, Christopher D., Hamilton, Jennifer D., Sleeman, Matthew A., Kalliolias, George D., Mylonakis, Eleftherios, and Skokos, Dimitris

Published
2024
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34. Safety and efficacy of arimoclomol in patients with early amyotrophic lateral sclerosis (ORARIALS-01): a randomised, double-blind, placebo-controlled, multicentre, phase 3 trial

Author

Granit, Volkan, Steele, Julie, Levy, Wendy, Paredes, Maria Elena, Hernandez, Jessica, Bilsker, Martin, Szacka, Katarzyna, Ronert, Adam, Jablońska, Dorota, Łuczak, Alina Zuzanna, Chaverri, Delia, Janse van Mantgem, Mark R, Bunte, Tommy M, Broere, Bianca, de Fockert, Arianne, Sanchez-Tejerina, Daniel, Landabaso, Carmen, Calvo, Andrea, Moglia, Cristina, Manera, Umberto, Canosa, Antonio, Vasta, Rosario, Salamone, Paolina, Fuda, Giuseppe, DeMarco, Giovanni, Casale, Federico, ME Forsberg, Karin, Winroth, Ivar, Almgren Stenberg, Erica, Holmgren, Monica, Amador, Maria del Mar, Lenglet, Timothee, Querin, Giorgia, Coudoin, Sylvie, Pavlakis, Pantelis, Holzberg, Shara, Sideri, Riccardo, Marinou, Kalliopi, Czarnecki, Maciej, Ługiewicz, Renata, Biel-Czarnecka, Marta, Boczkowska, Marcelina, Schotte, Caroline, Vynckier, Jan, Van Daele, Sien, Claeys, Thomas, Delmotte, Koen, Swinnen, Bart, Serrien, Anouk, D'Hondt, Ann, Lamaire, Nikita, Debien, Elisa, Jones, Sarah, Vachon, Chris, Grogan, James, Solorzano, Guillermo, Crowell, Allison, Rakocevic, Goran, Wagoner, Mary, Alma, Osmanovic, Flavia, Wiehler, Sonja, Körner, Olivia, Schreiber-Katz, Camilla, Wohnrade, Anastasia, Sarikidi, Carola, Kassebaum, Chantal, Fischer, Adamo, Ashley, Turcotte, Nicole, Duncan, Jessie, Turner, Ivone, Elman, Lauren, Massie, Rami, Berube, Maxime, Saunders, Natalie, Salmon, Kristiana, Foucher, Juliette, Agessandro, Abrahao, Shirley, Pham, Jahan, Mookshah, Phung, Liane, Statland, Jeffrey, Jawdat, Omar, Dimachkie, Mazen, Pasnoor, Mamatha, Farmakidis, Constantine, Heim, Andrew, Lillig, Katie, Lackey, Alyssa, Weber, Markus, Kurz, Martina, Levine, Todd, Benatar, Michael, Hansen, Thomas, Rom, Dror, Geist, Marie A, Blaettler, Thomas, Camu, William, Kuzma-Kozakiewicz, Magdalena, van den Berg, Leonard H, Morales, Raul Juntas, Chio, Adriano, Andersen, Peter M, Pradat, Pierre-Francois, Lange, Dale, Van Damme, Philip, Mora, Gabriele, Grudniak, Mariusz, Elliott, Matthew, Petri, Susanne, Olney, Nicholas, Ladha, Shafeeq, Goyal, Namita A, Meyer, Thomas, Hanna, Michael G, Quinn, Colin, Genge, Angela, Zinman, Lorne, Jabari, Duaa, Shoesmith, Christen, Ludolph, Albert C, Neuwirth, Christoph, Nations, Sharon, Shefner, Jeremy M, Turner, Martin R, Wuu, Joanne, Bennett, Richard, Dang, Hoang, and Sundgreen, Claus

Published
2024
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37. Genome-wide structural variant analysis identifies risk loci for non-Alzheimer’s dementias

Author

Soltis, Anthony R., Viollet, Coralie, Sukumar, Gauthaman, Alba, Camille, Lott, Nathaniel, McGrath Martinez, Elisa, Tuck, Meila, Singh, Jatinder, Bacikova, Dagmar, Zhang, Xijun, Hupalo, Daniel N., Adeleye, Adelani, Wilkerson, Matthew D., Pollard, Harvey B., Dalgard, Clifton L., Black, Sandra E., Gan-Or, Ziv, Keith, Julia, Masellis, Mario, Rogaeva, Ekaterina, Brice, Alexis, Lesage, Suzanne, Xiromerisiou, Georgia, Calvo, Andrea, Canosa, Antonio, Chio, Adriano, Logroscino, Giancarlo, Mora, Gabriele, Krüger, Reijko, May, Patrick, Alcolea, Daniel, Clarimon, Jordi, Fortea, Juan, Gonzalez-Aramburu, Isabel, Infante, Jon, Lage, Carmen, Lleó, Alberto, Pastor, Pau, Sanchez-Juan, Pascual, Brett, Francesca, Aarsland, Dag, Al-Sarraj, Safa, Attems, Johannes, Gentleman, Steve, Hardy, John A., Hodges, Angela K., Love, Seth, McKeith, Ian G., Morris, Christopher M., Morris, Huw R., Palmer, Laura, Pickering-Brown, Stuart, Ryten, Mina, Thomas, Alan J., Troakes, Claire, Albert, Marilyn S., Barrett, Matthew J., Beach, Thomas G., Bekris, Lynn M., Bennett, David A., Boeve, Bradley F., Dawson, Ted M., Dickson, Dennis W., Faber, Kelley, Ferman, Tanis, Ferrucci, Luigi, Flanagan, Margaret E., Foroud, Tatiana M., Ghetti, Bernardino, Gibbs, J. Raphael, Goate, Alison, Goldstein, David S., Graff-Radford, Neill R., Kaufmann, Horacio, Kukull, Walter A., Leverenz, James B., Lopez, Grisel, Mao, Qinwen, Masliah, Eliezer, Monuki, Edwin, Newell, Kathy L., Palma, Jose-Alberto, Perkins, Matthew, Pletnikova, Olga, Renton, Alan E., Resnick, Susan M., Rosenthal, Liana S., Ross, Owen A., Scherzer, Clemens R., Serrano, Geidy E., Shakkottai, Vikram G., Sidransky, Ellen, Tanaka, Toshiko, Tayebi, Nahid, Topol, Eric, Torkamani, Ali, Troncoso, Juan C., Woltjer, Randy, Wszolek, Zbigniew K., Scholz, Sonja W., Baloh, Robert H., Bowser, Robert, Broach, James, Camu, William, Chiò, Adriano, Cooper-Knock, John, Drepper, Carsten, Drory, Vivian E., Dunckley, Travis L., Feldman, Eva, Fratta, Pietro, Gerhard, Glenn, Gibson, Summer B., Glass, Jonathan D., Harms, Matthew B., Heiman-Patterson, Terry D., Jansson, Lilja, Kirby, Janine, Kwan, Justin, Laaksovirta, Hannu, Landers, John E., Landi, Francesco, Le Ber, Isabelle, Lumbroso, Serge, MacGowan, Daniel J.L., Maragakis, Nicholas J., Mouzat, Kevin, Myllykangas, Liisa, Orrell, Richard W., Ostrow, Lyle W., Pamphlett, Roger, Pioro, Erik, Pulst, Stefan M., Ravits, John M., Robberecht, Wim, Rothstein, Jeffrey D., Sendtner, Michael, Shaw, Pamela J., Sidle, Katie C., Simmons, Zachary, Stein, Thor, Stone, David J., Tienari, Pentti J., Traynor, Bryan J., Valori, Miko, Van Damme, Philip, Van Deerlin, Vivianna M., Van Den Bosch, Ludo, Zinman, Lorne, Kaivola, Karri, Chia, Ruth, Ding, Jinhui, Rasheed, Memoona, Fujita, Masashi, Menon, Vilas, Walton, Ronald L., Collins, Ryan L., Billingsley, Kimberley, Brand, Harrison, Talkowski, Michael, Zhao, Xuefang, Dewan, Ramita, Stark, Ali, Ray, Anindita, Solaiman, Sultana, Alvarez Jerez, Pilar, Malik, Laksh, Tienari, Pentti, Mazzini, Letizia, D'Alfonso, Sandra, Moglia, Cristina, and De Jager, Philip L.

Published
2023
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38. The role of peripheral immunity in ALS: a population‐based study

Author

Maurizio Grassano, Umberto Manera, Fabiola De Marchi, Paolo Cugnasco, Enrico Matteoni, Margherita Daviddi, Luca Solero, Alessandro Bombaci, Francesca Palumbo, Rosario Vasta, Antonio Canosa, Paolina Salamone, Giuseppe Fuda, Federico Casale, Letizia Mazzini, Andrea Calvo, Cristina Moglia, and Adriano Chiò

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Systemic inflammation has been proposed as a relevant mechanism in amyotrophic lateral sclerosis (ALS). Still, comprehensive data on ALS patients' innate and adaptive immune responses and their effect on the clinical phenotype are lacking. Here, we investigate systemic immunity in a population‐based ALS cohort using readily available hematological indexes. Methods We collected clinical data and the complete blood count (CBC) at diagnosis in ALS patients from the Piemonte and Valle d'Aosta Register for ALS (PARALS) from 2007 to 2019. Leukocytes populations, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), systemic‐immune‐inflammation index (SII), and lymphocyte‐to‐monocyte ratio (LMR) were derived from CBC. All variables were analyzed for association with clinical features in the entire cohort and then in sex‐ and age‐based subgroups. Results Neutrophils (P = 0.001) and markers of increased innate immunity (NLR, P = 0.008 and SII, P = 0.006) were associated with a faster disease progression. Similarly, elevated innate immunity correlated with worse pulmonary function and shorter survival. The prognosis in women also correlated with low lymphocytes (P = 0.045) and a decreased LMR (P = 0.013). ALS patients with cognitive impairment exhibited lower monocytes (P = 0.0415). Conclusions and Relevance The dysregulation of the systemic immune system plays a multifaceted role in ALS. More specifically, an elevated innate immune response is associated with faster progression and reduced survival. Conversely, ALS patients with cognitive impairment showed a reduction in monocyte count. Additionally, immune response varied according to sex and age, thus suggesting that involved immune pathways are patient specific. Further studies will help translate those findings into clinical practice or targeted treatments.

Published
2023
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40. Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries

Author

Sodhi, Sukhdeep S., Chio, Ellie Ka-In, Jash, Ambarish, Ontañón, Santiago, Apte, Ajit, Kumar, Ankit, Jeje, Ayooluwakunmi, Kuzmin, Dima, Fung, Harry, Cheng, Heng-Tze, Effrat, Jon, Bali, Tarush, Jindal, Nitin, Cao, Pei, Singh, Sarvjeet, Zhou, Senqiang, Khan, Tameen, Wankhede, Amol, Alzantot, Moustafa, Wu, Allen, and Chandra, Tushar

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

As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, Mondegreen, to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts., Comment: Accepted in KDD 2021

Published
2021

42. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Author

van Rheenen, Wouter, van der Spek, Rick AA, Bakker, Mark K, van Vugt, Joke JFA, Hop, Paul J, Zwamborn, Ramona AJ, de Klein, Niek, Westra, Harm-Jan, Bakker, Olivier B, Deelen, Patrick, Shireby, Gemma, Hannon, Eilis, Moisse, Matthieu, Baird, Denis, Restuadi, Restuadi, Dolzhenko, Egor, Dekker, Annelot M, Gawor, Klara, Westeneng, Henk-Jan, Tazelaar, Gijs HP, van Eijk, Kristel R, Kooyman, Maarten, Byrne, Ross P, Doherty, Mark, Heverin, Mark, Al Khleifat, Ahmad, Iacoangeli, Alfredo, Shatunov, Aleksey, Ticozzi, Nicola, Cooper-Knock, Johnathan, Smith, Bradley N, Gromicho, Marta, Chandran, Siddharthan, Pal, Suvankar, Morrison, Karen E, Shaw, Pamela J, Hardy, John, Orrell, Richard W, Sendtner, Michael, Meyer, Thomas, Başak, Nazli, van der Kooi, Anneke J, Ratti, Antonia, Fogh, Isabella, Gellera, Cinzia, Lauria, Giuseppe, Corti, Stefania, Cereda, Cristina, Sproviero, Daisy, D’Alfonso, Sandra, Sorarù, Gianni, Siciliano, Gabriele, Filosto, Massimiliano, Padovani, Alessandro, Chiò, Adriano, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Canosa, Antonio, Grassano, Maurizio, Beghi, Ettore, Pupillo, Elisabetta, Logroscino, Giancarlo, Nefussy, Beatrice, Osmanovic, Alma, Nordin, Angelica, Lerner, Yossef, Zabari, Michal, Gotkine, Marc, Baloh, Robert H, Bell, Shaughn, Vourc’h, Patrick, Corcia, Philippe, Couratier, Philippe, Millecamps, Stéphanie, Meininger, Vincent, Salachas, François, Mora Pardina, Jesus S, Assialioui, Abdelilah, Rojas-García, Ricardo, Dion, Patrick A, Ross, Jay P, Ludolph, Albert C, Weishaupt, Jochen H, Brenner, David, Freischmidt, Axel, Bensimon, Gilbert, Brice, Alexis, Durr, Alexandra, Payan, Christine AM, Saker-Delye, Safa, Wood, Nicholas W, Topp, Simon, Rademakers, Rosa, Tittmann, Lukas, Lieb, Wolfgang, Franke, Andre, Ripke, Stephan, Braun, Alice, and Kraft, Julia

Subjects
Human Genome, Neurodegenerative, Clinical Research, Rare Diseases, Prevention, ALS, Neurosciences, Genetics, Brain Disorders, Aetiology, 2.1 Biological and endogenous factors, Neurological, Amyotrophic Lateral Sclerosis, Brain, Cholesterol, Disease Progression, Female, Genome-Wide Association Study, Glutamine, Humans, Male, Mendelian Randomization Analysis, Microsatellite Repeats, Mutation, Neurodegenerative Diseases, Neurons, Quantitative Trait Loci, RNA-Seq, Risk Factors, SLALOM Consortium, PARALS Consortium, SLAGEN Consortium, SLAP Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons.

Published
2021

43. BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training

Author

Zhao, Letian, Xu, Rui, Wang, Tianqi, Tian, Teng, Wang, Xiaotian, Wu, Wei, Ieong, Chio-in, and Jin, Xi

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence
Abstract

The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. To satisfy the requirement of computation and memory of DNN training, distributed deep learning based on model parallelism has been widely recognized. We propose a new pipeline parallelism training framework, BaPipe, which can automatically explore pipeline parallelism training methods and balanced partition strategies for DNN distributed training. In BaPipe, each accelerator calculates the forward propagation and backward propagation of different parts of networks to implement the intra-batch pipeline parallelism strategy. BaPipe uses a new load balancing automatic exploration strategy that considers the parameters of DNN models and the computation, memory, and communication resources of accelerator clusters. We have trained different DNNs such as VGG-16, ResNet-50, and GNMT on GPU clusters and simulated the performance of different FPGA clusters. Compared with state-of-the-art data parallelism and pipeline parallelism frameworks, BaPipe provides up to 3.2x speedup and 4x memory reduction in various platforms.

Published
2020

44. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023.

Author

Guglielmo Faggioli, Alessandro Guazzo, Stefano Marchesin 0001, Laura Menotti, Isotta Trescato, Helena Aidos, Roberto Bergamaschi, Giovanni Birolo, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel García Dominguez, Marta Gromicho, Enrico Longato, Sara C. Madeira, Umberto Manera, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo, and Nicola Ferro 0001

Published
2023
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45. Overview of iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge.

Author

Guglielmo Faggioli, Alessandro Guazzo, Stefano Marchesin 0001, Laura Menotti, Isotta Trescato, Helena Aidos, Roberto Bergamaschi, Giovanni Birolo, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel García Dominguez, Marta Gromicho, Enrico Longato, Sara C. Madeira, Umberto Manera, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo, and Nicola Ferro 0001

Published
2023

48. Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023

Author

Faggioli, Guglielmo, Guazzo, Alessandro, Marchesin, Stefano, Menotti, Laura, Trescato, Isotta, Aidos, Helena, Bergamaschi, Roberto, Birolo, Giovanni, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Piero, García Dominguez, Jose Manuel, Gromicho, Marta, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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49. Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesian Networks and Transfer Learning to Develop Prognostic Models of Amyotrophic Lateral Sclerosis

Author

Longato, Enrico, Tavazzi, Erica, Chió, Adriano, Mora, Gabriele, Sparacino, Giovanni, Di Camillo, Barbara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Juarez, Jose M., editor, Marcos, Mar, editor, Stiglic, Gregor, editor, and Tucker, Allan, editor

Published
2023
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50. iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge

Author

Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, Di Camillo, Barbara, de Carvalho, Mamede Alves, Ferro, Nicola, Fariselli, Piero, Dominguez, Jose Manuel García, Madeira, Sara C., Tavazzi, Eleonora, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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