Topological obstructions in the way of data-driven collective variables

Nonlinear dimensionality reduction (NLDR) techniques are increasingly used to visualize molecular trajectories and to create data-driven collective variables for enhanced sampling simulations. The success of these methods relies on their ability to identify the essential degrees of freedom characterizing conformational changes. Here, we show that NLDR methods face serious obstacles when the underlying collective variables present periodicities, e.g., arising from proper dihedral angles. As a result, NLDR methods collapse very distant configurations, thus leading to misinterpretations and inefficiencies in enhanced sampling. Here, we identify this largely overlooked problem and discuss possible approaches to overcome it. We also characterize the geometry and topology of conformational changes of alanine dipeptide, a benchmark system for testing new methods to identify collective variables. Nonlinear dimensionality reduction (NLDR) techniques are increasingly used to visualize molecular trajectories and to create data-driven collective variables for enhanced sampling simulations. The success of these methods relies on their ability to identify the essential degrees of freedom characterizing conformational changes. Here, we show that NLDR methods face serious obstacles when the underlying collective variables present periodicities, e.g., arising from proper dihedral angles. As a result, NLDR methods collapse very distant configurations, thus leading to misinterpretations and inefficiencies in enhanced sampling. Here, we identify this largely overlooked problem and discuss possible approaches to overcome it. We also characterize the geometry and topology of conformational changes of alanine dipeptide, a benchmark system for testing new methods to identify collective variables

Charting molecular free-energy landscapes with an atlas of collective variables

Collective variables (CVs) are a fundamental tool to understand molecular flexibility, to compute free energy landscapes, and to enhance sampling in molecular dynamics simulations. However, identifying suitable CVs is challenging, and is increasingly addressed with systematic data-driven manifold learning techniques. Here, we provide a flexible framework to model molecular systems in terms of a collection of locally valid and partially overlapping CVs: an atlas of CVs. The specific motivation for such a framework is to enhance the applicability and robustness of CVs based on manifold learning methods, which fail in the presence of periodicities in the underlying conformational manifold. More generally, using an atlas of CVs rather than a single chart may help us better describe different regions of conformational space. We develop the statistical mechanics foundation for our multi-chart description and propose an algorithmic implementation. The resulting atlas of data-based CVs are then used to enhance sampling and compute free energy surfaces in two model systems, alanine dipeptide and ß-D-glucopyranose, whose conformational manifolds have toroidal and spherical topologies

Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables

Collective variables (CVs) are low-dimensional representations of the state of a complex system, which help us rationalize molecular conformations and sample free energy landscapes with molecular dynamics simulations. Given their importance, there is need for systematic methods that effectively identify CVs for complex systems. In recent years, nonlinear manifold learning has shown its ability to automatically characterize molecular collective behavior. Unfortunately, these methods fail to provide a differentiable function mapping high-dimensional configurations to their low-dimensional representation, as required in enhanced sampling methods. We introduce a methodology that, starting from an ensemble representative of molecular flexibility, builds smooth and nonlinear data-driven collective variables (SandCV) from the output of nonlinear manifold learning algorithms. We demonstrate the method with a standard benchmark molecule, alanine dipeptide, and show how it can be non-intrusively combined with off-the-shelf enhanced sampling methods, here the adaptive biasing force method. We illustrate how enhanced sampling simulations with SandCV can explore regions that were poorly sampled in the original molecular ensemble. We further explore the transferability of SandCV from a simpler system, alanine dipeptide in vacuum, to a more complex system, alanine dipeptide in explicit water.Peer ReviewedPostprint (published version

Towards matching user mobility traces in large-scale datasets

The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources. With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories. Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people's mobility traces, coming from a mobile network operator and transportation smart card usage. We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users. We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8% success only after a one-week long observation of their mobility traces, and over 55% after four weeks. We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets. Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time. We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals

Requirements for an Intelligent Maintenance System for Industry 4.0

comprobación paso "titulo publicación " - Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future[EN] Recent advances in the development of technological devices and software for Industry 4.0 have pushed a change in the maintenance management systems and processes. Nowadays, in order to maintain a company competitive, a computerised management system is required to help in its maintenance tasks. This paper presents an analysis of the complexities and requirements for maintenance of Industry 4.0. It focuses on intelligent systems that can help to improve the intelligent management of maintenance. Finally, it presents a summary of lessons learned specified as guidelines for the design of such intelligent systems that can be applied horizontally to any company in the Industry.This work is supported by the FEDER/Ministry of Science, Innovation and Universities - State Research Agency RTC-2017-6401-7Garcia, E.; Araujo, A.; Palanca Cámara, J.; Giret Boggino, AS.; Julian Inglada, VJ.; Botti, V. (2019). Requirements for an Intelligent Maintenance System for Industry 4.0. Springer. 340-351. https://doi.org/10.1007/978-3-030-27477-1_26S340351CEN, European Committee for Standardization: EN 13306:2017. Maintenance Terminology. European Standard (2017)Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of Industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505–6519 (2018). https://doi.org/10.1109/access.2017.2783682Crespo Marquez, A., Gupta, J.N.: Contemporary maintenance management: process, framework and supporting pillars. Omega 34(3), 313–326 (2006). https://doi.org/10.1016/j.omega.2004.11.003Ferreira, L.L., Albano, M., Silva, J., Martinho, D., Marreiros, G., di Orio, G., Malo, P., Ferreira, H.: A pilot for proactive maintenance in Industry 4.0. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS). IEEE (2017). https://doi.org/10.1109/wfcs.2017.7991952Goh, K., Tjahjono, B., Baines, T., Subramaniam, S.: A review of research in manufacturing prognostics. In: 2006 IEEE International Conference on Industrial Informatics, Singapore, pp. 417–422. IEEE (2006). https://doi.org/10.1109/INDIN.2006.275836Hashemian, H.M., Bean, W.C.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(10), 3480–3492 (2011). https://doi.org/10.1109/TIM.2009.2036347Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M.B., Sutheralnd, J.W.: Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP 80, 506–511 (2019)Lu, B., Durocher, D., Stemper, P.: Predictive maintenance techniques. IEEE Ind. Appl. Mag. 15(6), 52–60 (2009). https://doi.org/10.1109/MIAS.2009.934444Mrugalska, B., Wyrwicka, M.K.: Towards lean production in Industry 4.0. Procedia Eng. 182, 466–473 (2017). https://doi.org/10.1016/j.proeng.2017.03.135O’Donoghue, C., Prendergast, J.: Implementation and benefits of introducing a computerised maintenance management system into a textile manufacturing company. J. Mater. Process. Technol. 153, 226–232 (2004)Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., Loncarski, J.: Machine learning approach for predictive maintenance in Industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE (2018). https://doi.org/10.1109/mesa.2018.8449150Patil, R.B., Mhamane, D.A., Kothavale, P.B., Kothavale, B.: Fault tree analysis: a case study from machine tool industry. Available at SSRN 3382241 (2018)Potes Ruiz, P.A., Kamsu-Foguem, B., Noyes, D.: Knowledge reuse integrating the collaboration from experts in industrial maintenance management. Knowl. Based Syst. 50, 171–186 (2013). https://doi.org/10.1016/j.knosys.2013.06.005Razmi-Farooji, A., Kropsu-Vehkaperä, H., Härkönen, J., Haapasalo, H.: Advantages and potential challenges of data management in e-maintenance. J. Qual. Maint. Eng. (2019)Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Harnisch, M.: Industry 4.0: the future of productivity and growth in manufacturing industries. Boston Consult. Group 9(1), 54–89 (2015)Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., Vasilakos, A.V.: A manufacturing big data solution for active preventive maintenance. IEEE Trans. Ind. Inform. 13(4), 2039–2047 (2017). https://doi.org/10.1109/tii.2017.267050

Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants

This report describes the status of ongoing research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R&D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions

Customized clinical practice guidelines for management of adult cataract in Iran

Purpose: To customize clinical practice guidelines (CPGs) for cataract management in the Iranian population. Methods: First, four CPGs (American Academy of Ophthalmology 2006 and 2011, Royal College of Ophthalmologists 2010, and Canadian Ophthalmological Society 2008) were selected from a number of available CPGs in the literature for cataract management. All recommendations of these guidelines, together with their references, were studied. Each recommendation was summarized in 4 tables. The first table showed the recommendation itself in clinical question components format along with its level of evidence. The second table contained structured abstracts of supporting articles related to the clinical question with their levels of evidence. The third table included the customized recommendation of the internal group respecting its clinical advantage, cost, and complications. In the fourth table, the internal group their recommendations from 1 to 9 based on the customizing capability of the recommendation (applicability, acceptability, external validity). Finally, customized recommendations were sent one month prior to a consensus session to faculty members of all universities across the country asking for their comments on recommendations. Results: The agreed recommendations were accepted as conclusive while those with no agreement were discussed at the consensus session. Finally, all customized recommendations were codified as 80 recommendations along with their sources and levels of evidence for the Iranian population. Conclusion: Customization of CPGs for management of adult cataract for the Iranian population seems to be useful for standardization of referral, diagnosis and treatment of patients. © 2015 Journal of Ophthalmic and Vision Research | Published by Wolters Kluwer - Medknow

Federated Learning for Breast Density Classification: A Real-World Implementation

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.Comment: Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig.

Heterogeneity of Associations between Total and Types of Fish Intake and the Incidence of Type 2 Diabetes: Federated Meta-Analysis of 28 Prospective Studies Including 956,122 Participants.

The association between fish consumption and new-onset type 2 diabetes is inconsistent and differs according to geographical location. We examined the association between the total and types of fish consumption and type 2 diabetes using individual participant data from 28 prospective cohort studies from the Americas (6), Europe (15), the Western Pacific (6), and the Eastern Mediterranean (1) comprising 956,122 participants and 48,084 cases of incident type 2 diabetes. Incidence rate ratios (IRRs) for associations of total fish, shellfish, fatty, lean, fried, freshwater, and saltwater fish intake and type 2 diabetes were derived for each study, adjusting for a consistent set of confounders and combined across studies using random-effects meta-analysis. We stratified all analyses by sex due to observed interaction (p = 0.002) on the association between fish and type 2 diabetes. In women, for each 100 g/week higher intake the IRRs (95% CIs) of type 2 diabetes were 1.02 (1.01-1.03, I2 = 61%) for total fish, 1.04 (1.01-1.07, I2 = 46%) for fatty fish, and 1.02 (1.00-1.04, I2 = 33%) for lean fish. In men, all associations were null. In women, we observed variation by geographical location: IRRs for total fish were 1.03 (1.02-1.04, I2 = 0%) in the Americas and null in other regions. In conclusion, we found evidence of a neutral association between total fish intake and type 2 diabetes in men, but there was a modest positive association among women with heterogeneity across studies, which was partly explained by geographical location and types of fish intake. Future research should investigate the role of cooking methods, accompanying foods and environmental pollutants, but meanwhile, existing dietary regional, national, or international guidelines should continue to guide fish consumption within overall healthy dietary patterns