Papers | Parallel Computing

@article{21:FGCS:jupyflow,

title = {Distributed workflows with Jupyter},

author = {Iacopo Colonnelli and Marco Aldinucci and Barbara Cantalupo and Luca Padovani and Sergio Rabellino and Concetto Spampinato and Roberto Morelli and Rosario Di Carlo and Nicolò Magini and Carlo Cavazzoni},

url = {https://www.sciencedirect.com/science/article/pii/S0167739X21003976},

doi = {10.1016/j.future.2021.10.007},

issn = {0167-739X},

year  = {2022},

date = {2022-01-01},

journal = {Future Generation Computer Systems},

volume = {128},

pages = {282–298},

abstract = {The designers of a new coordination interface enacting complex workflows have to tackle a dichotomy: choosing a language-independent or language-dependent approach. Language-independent approaches decouple workflow models from the host code's business logic and advocate portability. Language-dependent approaches foster flexibility and performance by adopting the same host language for business and coordination code. Jupyter Notebooks, with their capability to describe both imperative and declarative code in a unique format, allow taking the best of the two approaches, maintaining a clear separation between application and coordination layers but still providing a unified interface to both aspects. We advocate the Jupyter Notebooks' potential to express complex distributed workflows, identifying the general requirements for a Jupyter-based Workflow Management System (WMS) and introducing a proof-of-concept portable implementation working on hybrid Cloud-HPC infrastructures. As a byproduct, we extended the vanilla IPython kernel with workflow-based parallel and distributed execution capabilities. The proposed Jupyter-workflow (Jw) system is evaluated on common scenarios for High Performance Computing (HPC) and Cloud, showing its potential in lowering the barriers between prototypical Notebooks and production-ready implementations.},

keywords = {across, deephealth, jupyter-workflow, streamflow},

pubstate = {published},

tppubtype = {article}

}

@article{21:eit:covidclaire,

title = {The CLAIRE COVID-19 initiative: approach, experiences and recommendations},

author = {Gianluca Bontempi and Ricardo Chavarriaga and Hans De Canck and Emanuela Girardi and Holger Hoos and Iarla Kilbane‐Dawe and Tonio Ball and Ann Nowé and Jose Sousa and Davide Bacciu and Marco Aldinucci and Manlio De Domenico and Alessandro Saffiotti and Marco Maratea},

url = {https://iris.unito.it/retrieve/handle/2318/1784271/747923/Bontempi2021_Article_TheCLAIRECOVID-19InitiativeApp-3.pdf},

doi = {10.1007/s10676-020-09567-7},

year  = {2021},

date = {2021-02-01},

journal = {Ethics and Information Technology},

publisher = {Springer},

abstract = {A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.},

keywords = {deephealth},

pubstate = {published},

tppubtype = {article}

}

@article{21:ai4numbers:minerva,

title = {A Review of Machine Learning for Cardiology},

author = {Yasir Arfat and Gianluca Mittone and Roberto Esposito and Barbara Cantalupo and Gaetano Maria De Ferrari and Marco Aldinucci},

url = {https://iris.unito.it/retrieve/handle/2318/1796298/780512/21_AI4numbers-preprint.pdf},

doi = {10.23736/s2724-5683.21.05709-4},

year  = {2021},

date = {2021-01-01},

journal = {Minerva cardiology and angiology},

abstract = {This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {article}

}

@article{21:neohic:bmc,

title = {Advantages of using graph databases to explore chromatin conformation capture experiments},

author = {Daniele D'Agostino and Pietro Liò and Marco Aldinucci and Ivan Merelli},

url = {https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-020-03937-0.pdf},

doi = {10.1186/s12859-020-03937-0},

isbn = {1471-2105},

year  = {2021},

date = {2021-01-01},

journal = {BMC Bioinformatics},

volume = {22},

number = {2},

pages = {43–58},

abstract = {High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells.},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {article}

}

@article{21:lancet,

title = {Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets},

author = {Fabrizio D'Ascenzo and Ovidio De Filippo and Guglielmo Gallone and Gianluca Mittone and Marco Agostino Deriu and Mario Iannaccone and Albert Ariza-Solé and Christoph Liebetrau and Sergio Manzano-Fernández and Giorgio Quadri and Tim Kinnaird and Gianluca Campo and Jose Paulo Simao Henriques and James M Hughes and Alberto Dominguez-Rodriguez and Marco Aldinucci and Umberto Morbiducci and Giuseppe Patti and Sergio Raposeiras-Roubin and Emad Abu-Assi and Gaetano Maria De Ferrari and Francesco Piroli and Andrea Saglietto and Federico Conrotto and Pierluigi Omedé and Antonio Montefusco and Mauro Pennone and Francesco Bruno and Pier Paolo Bocchino and Giacomo Boccuzzi and Enrico Cerrato and Ferdinando Varbella and Michela Sperti and Stephen B. Wilton and Lazar Velicki and Ioanna Xanthopoulou and Angel Cequier and Andres Iniguez-Romo and Isabel Munoz Pousa and Maria Cespon Fernandez and Berenice Caneiro Queija and Rafael Cobas-Paz and Angel Lopez-Cuenca and Alberto Garay and Pedro Flores Blanco and Andrea Rognoni and Giuseppe Biondi Zoccai and Simone Biscaglia and Ivan Nunez-Gil and Toshiharu Fujii and Alessandro Durante and Xiantao Song and Tetsuma Kawaji and Dimitrios Alexopoulos and Zenon Huczek and Jose Ramon Gonzalez Juanatey and Shao-Ping Nie and Masa-aki Kawashiri and Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Sergio Leonardi and Walter Grosso Marra and Alaide Chieffo and Umberto Michelucci and Dario Piga and Marta Malavolta and Sebastiano Gili and Marco Mennuni and Claudio Montalto and Luigi Oltrona Visconti and Yasir Arfat},

url = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},

doi = {10.1016/S0140-6736(20)32519-8},

issn = {0140-6736},

year  = {2021},

date = {2021-01-01},

journal = {The Lancet},

volume = {397},

number = {10270},

pages = {199–207},

abstract = {Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding. Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making.},

keywords = {ai, cardio, deephealth, hpc4ai},

pubstate = {published},

tppubtype = {article}

}

@article{20Lstreamflow:tetc,

title = {StreamFlow: cross-breeding cloud with HPC},

author = {Iacopo Colonnelli and Barbara Cantalupo and Ivan Merelli and Marco Aldinucci},

url = {https://arxiv.org/pdf/2002.01558},

doi = {10.1109/TETC.2020.3019202},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Transactions on Emerging Topics in Computing},

volume = {9},

number = {4},

pages = {1723–1737},

abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single cell transcriptomic data analysis workflow.},

keywords = {deephealth, hpc4ai, streamflow},

pubstate = {published},

tppubtype = {article}

}