One of the major criteria for the scientificity of a research study is reproducibility. In this talk we will present the main definitions around reproducibility. We will examine to what extent computer science and simulation works are concerned. We will give some examples of applications including parallel stochastic simulations, which are too often presented as non-reproducible. Anyone wishing to produce quality scientific work should pay attention to the numerical reproducibility of his simulation results. Significant differences can be observed in the results of simulations if the practitioner does not apply best practices. We will see that even for parallel stochastic simulations, it is possible to reproduce the same numerical results by implementing a rigorous method tested up to a billion threads. It is possible to check parallel results with their sequential counterpart before scaling, thus gaining confidence in the proposed simulation models. This conference will present best practices able to face even the silent errors impacting top supercomputers, including the Exascale machine arrived this year.
Professor David Hill is doing his research at the French Centre for National Research (CRNS) in the LIMOS laboratory (UMR 6158). He recently supervised research at CERN in High Performance Computing. He was Vice President of Blaise Pascal University (2008-2012) and also past director of a French Regional Computing Center (CRRI) (2008-2010). Prof Hill was appointed two times deputy director (2005-2007 ; 2018-2021) of the Auvergne Institute of Computer Science ISIMA. He has authored or co-authored more than 250 papers and has also published several scientific books.
CHOOM is a new Artificial Intelligence (AI) model that offers novel deep dynamic knowledge for predicting the toxicity of molecules and their metabolism. It is based on our object-oriented model based on temporal fuzzy vector spaces (FVSOOM) to qualitatively and quantitatively assess the structure-activity relationship (QSAR) from its already known component molecules. The QSAR approaches advocated by the Organisation for Economic Co-operation and Development (OECD) are mainly based on top-down analytical research. CHOOM offers a systemic evaluation of the emergence of the properties of a new molecule based on the ascending inheritance of FVSOOM and thus an explanation of the causes of its toxicity at each level of its composition hierarchy from each atom to the whole molecule. CHOOM is an innovation AI tool for assessing the continuous flow of new molecules produced by human activity whose potential toxicity must be tested to avoid harmful issues to health and environment sustainability. The reproducibility of the model is based on chemical knowledge of invariant covalent bonds between atoms and previously stored experience of already known molecules.
Joël Colloc earned his M.D. at the medical faculty of Lyon and a specialty degree of forensic medicine with a degree of clinical toxicology. He received a MSc. degree of IT from the Business School of Lyon (IAE) and a MSc. degree of computer sciences from the engineering school INSA of Lyon. He served as forensic physician at the Edouard Herriot Hospital in the neurological emergency department to cure drug addicted people, medical ethics and developed drug and addiction database. He went on to earn his Ph.D. in computer sciences at the INSA of Lyon.
As Hospital assistant at the laboratory of medical computer science and he taught IT at the medical faculty. He was elected as associate professor in computer sciences at IAE of Lyon and he earned his accreditation to supervise researches in sciences at the Lyon 1 University. He is a Le Havre Normandy University professor in computer sciences since 2003.
His main research topics concern e-health and particularly: fuzzy vectorial spaces (FVS), multi-agent clinical decision support systems (MADSS) and knowledge bases, Case Based Reasoning, ontologies, nervous system modeling and cognitive sciences and AI applications in medicine and human sciences.
His human sciences researches try to conciliate the ethics of using Big Data in epidemiological studies, autonomous systems and robots and keeping ethics use of AI in order to improve clinical decision in medicine while preserving the patient-caregiver relationship, the privacy and the freewill choice of the patients.