24 papers · ranked by Valyu relevance
Martino Grasselli, Gianmarco Mori, Havva Nilsu Oz
Despite decades of research in risk management, most of the literature has focused on scalar risk measures (like e.g. Value-at-Risk and Expected Shortfall). While such scalar measures provide compact and tractable summaries, they provide a poor informative value as they miss the intrinsic multivariate nature of risk.…
Authors not listed
We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations…
Martino Grasselli, Gianmarco Mori, Havva Nilsu Oz
Despite decades of research in risk management, most of the literature has focused on scalar risk measures (like e.g. Value-at-Risk and Expected Shortfall). While such scalar measures provide compact and tractable summaries, they provide a poor informative value as they miss the intrinsic multivariate nature of risk.…
Dimiter Prodanov
Biosafety risk assessment traditionally relies on categorical scales embodied by the four WHO Risk Groups and biocontainment levels. Mapping such categories to quantitative metrics is an open problem for the field: the classifications are too coarse for operational decision-making, yet strictly probabilistic language…
Martino Grasselli, Gianmarco Mori, Havva Nilsu Oz
Despite decades of research in risk management, most of the literature has focused on scalar risk measures (like e.g. Value-at-Risk and Expected Shortfall). While such scalar measures provide compact and tractable summaries, they provide a poor informative value as they miss the intrinsic multivariate nature of risk.…
Zuzana Krajcovicova, Pedro Pablo Pérez-Velasco, Carlos Vázquez
Models continue to increase their already broad use across industry as well as their sophistication. Worldwide regulation oblige financial institutions to manage and address model risk with the same severity as any other type of risk, e.g. [8], which besides defines model risk as the potential for adverse consequences…
Shuo Yang, Shizhen Li, Yanjun Huang, Hong Chen
Data-Driven Risk Quantification Model Authors: ['Shuo Yang' 'Shizhen Li' 'Yanjun Huang' 'Hong Chen'] Abstract—Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios. However, as a result of the…
Chen Wang, Fan Wang, Malgorzata Bogdan, Marco Masala + 5 more
Polygenic risk scores (PRS) are widely used in post-GWAS analyses to predict complex traits across humans, animals, and plants. While significant progress has been made in developing new PRS methods, much less attention has been given to quantifying the uncertainty associated with these predictions. In this work, we…
Olumayowa Adefowope Adekoya, Hany F. Atlam, Harjinder Singh Lallie, Shaoen Wu + 3 more
'Shaoen Wu' 'Periklis Chatzimisios' 'Jinbo Xiong' 'Mahmoud Daneshmand'] The increasing frequency and sophistication of cyber attacks have posed significant challenges for digital financial organisations, particularly in quantifying their multidimensional impacts. These challenges are largely attributed to the lack of a…
M. Christopher Newland
A recent article (Joslyn, P. R., & Morris, S. L. in Perspectives on Behavior Science, 47(1), 167-196, 2024) advocates the use of risk ratios, or relative risk, in behavior analysis. The authors present a strong case for the use of risk ratios and how they might improve the science and application of behavior analysis.…
Janez Kušar, Lidija Rihar, Urban Žargi, Marko Starbek
Project management of product/service orders has become a mode of operation in many companies. Although these are mostly cyclically recurring projects, risk management is very important for them. An extended risk-analysis model for new product/service projects is presented in this paper. Emphasis is on a solution…
Hormuzd A. Katki, Mark Schiffman
Diagnostic accuracy statistics, including predictive values, risk-differences, Youden’s index and Area Under the Curve (AUC), assess the promise of novel biomarkers proposed as diagnostic tests. We reinterpret these statistics in light of risk-stratification (how well a biomarker separates those at higher risk from…
James A. Watson, Chris C. Holmes
Exploration and modelling of individual treatment effects and treatment heterogeneity is an important aspect of precision medicine in randomized controlled trials (RCTs). The usual approach is to develop a predictive model for individual outcomes and then look for an interaction effect between treatment allocation and…
Alexandra C. Gillett, Evangelos Vassos, Cathryn M. Lewis
Stratified medicine requires models of disease risk incorporating genetic and environmental factors. These may combine estimates from different studies and models must be easily updatable when new estimates become available. The logit scale is often used in genetic and environmental association studies however the…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Shabnam Vatanpour, Steve E. Hrudey, Irina Dinu, Igor Burstyn + 1 more
'Gheorghe Luta'] The risk assessment matrix is a widely accepted, semi-quantitative tool for assessing risks, and setting priorities in risk management. Although the method can be useful to promote discussion to distinguish high risks from low risks, a published critique described a problem when the frequency and…
Verity Horigan, Robin Simons, Kim Kavanagh, Louise Kelly
Qualitative risk assessment (QRA) can provide decision support in line with the requirement for an objective, unbiased assessment of disease risk according to the Agreement on the Application of Sanitary and Phytosanitary Measures of the World Trade Organization. However, in order for a QRA to be objective and…
Kjersti Johnsrud, Therese Seierstad, David Russell, Mona-Elisabeth Revheim
A significant proportion of ischemic strokes are caused by emboli from unstable atherosclerotic carotid artery plaques with inflammation being a key feature of plaque instability and stroke risk. Positron emission tomography (PET) depicting the uptake of 2-deoxy-2-(^18^F)-fluoro-D-glucose (^18^F-FDG) in carotid artery…
Alexander Muacevic, John R Adler, Abdul M Kaleem, Jebastin Koilpillai + 1 more
'Jebastin Koilpillai' 'Damodharan Narayanasamy'] The aim of this article is to present ideas and tools of risk assessment that can be implemented to overcome various pharmaceutical quality challenges. These elements cover the development, production, distribution, inspection, and reporting of review procedures for drug…
Authors not listed
Occupational chemical hazards pose profound risks to chemists in laboratory and industrial settings, encompassing acute and chronic exposures that imperil sensory organs (e.g., ocular, auditory, olfactory, dermal) and vital physiological systems. This manuscript delineates a multifaceted, innovative protocol suite…
Gaëlle Lefort, Laurence Liaubet, Cécile Canlet, Patrick Tardivel + 6 more
In metabolomics, the detection of new biomarkers from NMR spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses. We present ASICS, an R package…
Hidenobu Kondo, Shih-Wei Chiu, Yukikazu Hayashi, Naoto Takahashi + 1 more
'Takuhiro Yamaguchi'] Background The risk-based approach (RBA) of clinical trial was first introduced in 2011-2012. RBA necessitates implementing risk reduction activities that are proportionate to risk in order to reduce avoidable quality issues. However, there is no consistent methodology or research for identifying…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…