A PINN Approach to Symbolic Differential Operator Discovery with Sparse Data

Given ample experimental data from a system governed by differential equations, it is possible to use deep learning techniques to construct the underlying differential operators. In this work we perform symbolic discovery of differential operators in a situation where there is sparse experimental data. This small data regime in machine learning can be made tractable by providing our algorithms with prior information about the underlying dynamics. Physics Informed Neural Networks (PINNs) have been very successful in this regime (reconstructing entire ODE solutions using only a single point or entire PDE solutions with very few measurements of the initial condition). We modify the PINN approach by adding a neural network that learns a representation of unknown hidden terms in the differential equation. The algorithm yields both a surrogate solution to the differential equation and a black-box representation of the hidden terms. These hidden term neural networks can then be converted into symbolic equations using symbolic regression techniques like AI Feynman. In order to achieve convergence of these neural networks, we provide our algorithms with (noisy) measurements of both the initial condition as well as (synthetic) experimental data obtained at later times. We demonstrate strong performance of this approach even when provided with very few measurements of noisy data in both the ODE and PDE regime

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Extending the Exposure Score of Web Browsers by Incorporating CVSS

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Yet its content differs from one browser to another. Despite the privacy and security risks of User-Agent strings, very few works have tackled this problem. Our previous work proposed giving Internet browsers exposure relative scores to aid users to choose less intrusive ones. Thus, the objective of this work is to extend our previous work through: first, conducting a user study to identify its limitations. Second, extending the exposure score via incorporating data from the NVD. Third, providing a full implementation, instead of a limited prototype. The proposed system: assigns scores to users’ browsers upon visiting our website. It also suggests alternative safe browsers, and finally it allows updating the back-end database with a click of a button. We applied our method to a data set of more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available here [4].</p

Multilocus Microsatellite Typing reveals intra-focal genetic diversity among strains of Leishmania tropica in Chichaoua Province, Morocco

AbstractIn Morocco, cutaneous leishmaniasis (CL) caused by Leishmania (L.) tropica is a major public health threat. Strains of this species have been shown to display considerable serological, biochemical, molecular biological and genetic heterogeneity; and Multilocus Enzyme Electrophoresis (MLEE), has shown that in many countries including Morocco heterogenic variants of L. tropica can co-exist in single geographical foci. Here, the microsatellite profiles discerned by MLMT of nine Moroccan strains of L. tropica isolated in 2000 from human cases of CL from Chichaoua Province were compared to those of nine Moroccan strains of L. tropica isolated between 1988 and 1990 from human cases of CL from Marrakech Province, and also to those of 147 strains of L. tropica isolated at different times from different worldwide geographical locations within the range of distribution of the species. Several programs, each employing a different algorithm, were used for population genetic analysis. The strains from each of the two Moroccan foci separated into two phylogenetic clusters independent of their geographical origin. Genetic diversity and heterogeneity existed in both foci, which are geographically close to each other. This intra-focal distribution of genetic variants of L. tropica is not considered owing to in situ mutation. Rather, it is proposed to be explained by the importation of pre-existing variants of L. tropica into Morocco

Perspectives on automated composition of workflows in the life sciences [version 1; peer review: 2 approved]

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.Stian Soiland-Reyes was supported by BioExcel-2 Centre of Excellence, funded by European Commission Horizon 2020 programme under European Commission contract H2020-INFRAEDI-02-2018 823830. Carole Goble was supported by EOSC-Life, funded by European Commission Horizon 2020 programme under grant agreement H2020-INFRAEOSC-2018-2 824087. We gratefully acknowledge the financial support from the Lorentz Center, ELIXIR, and the Leiden University Medical Center (LUMC) that made the workshop possible. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscriptPeer Reviewed"Article signat per 33 autors/es: Anna-Lena Lamprecht , Magnus Palmblad, Jon Ison, Veit Schwämmle , Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin , Paul Groth , Hans Ienasescu, Pratik Jagtap, Matúš Kalaš , Vedran Kasalica, Alireza Khanteymoori , Tobias Kuhn12, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert9, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft "Postprint (published version

A New Security Threat in MCUs -- SoC-wide timing side channels and how to find them

Microarchitectural timing side channels have been thoroughly investigated as a security threat in hardware designs featuring shared buffers (e.g., caches) and/or parallelism between attacker and victim task execution. Contradicting common intuitions, recent activities demonstrate, however, that this threat is real also in microcontroller SoCs without such features. In this paper, we describe SoC-wide timing side channels previously neglected by security analysis and present a new formal method to close this gap. In a case study with the RISC-V Pulpissimo SoC platform, our method found a vulnerability to a so far unknown attack variant that allows an attacker to obtain information about a victim's memory access behavior. After implementing a conservative fix, we were able to verify that the SoC is now secure w.r.t. timing side channels

Community Participation in Two Vaccination Trials in Slums of Kolkata, India: A Multi-level Analysis

This study aims at understanding the individual and community-level characteristics that influenced participation in two consecutive vaccine trials (typhoid and cholera) in urban slums of Kolkata, India. The study area was divided into 80 geographic clusters (communities), with 59,533 subjects aged ≥2 years for analysis. A multi-level model was employed in which the individuals were seen nested within the cluster. Rates of participation in both the trials were nearly the same; those who participated in the initial trial were likely to participate in the subsequent cholera vaccine trial. Communities with predominantly Hindu population, lower percentage of households with an educated household head, or lower percentage of households owning a motorbike had higher participation than their counterparts. At individual scale, higher participation was observed among younger subjects, females, and individuals from households with a household head who had no or minimal education. Geographic patterns were also observed in participation in the trials. The results illustrated that participation in the trial was mostly influenced by various individual and community-level factors, which need to be addressed for a successful vaccination campaign

Quantifying the core deficit in classical schizophrenia

In the classical descriptions of schizophrenia, Kraepelin and Bleuler recognised disorganization and impoverishment of mental activity as fundamental symptoms. Their classical descriptions also included a tendency to persisting disability. The psychopathological processes underlying persisting disability in schizophrenia remain poorly understood. The delineation of a core deficit underlying persisting disability would be of value in predicting outcome and enhancing treatment. We tested the hypothesis that mental disorganization and impoverishment are associated with persisting impairments of cognition and role-function, and together reflect a latent core deficit that is discernible in cases diagnosed by modern criteria. We used Confirmatory Factor Analysis to determine whether measures of disorganisation, mental impoverishment, impaired cognition and role functioning in 40 patients with schizophrenia represent a single latent variable. Disorganization scores were computed from the variance shared between disorganization measures from three commonly used symptom scales. Mental impoverishment scores were computed similarly. A single factor model exhibited a good fit, supporting the hypothesis that these measures reflect a core deficit.Persisting brain disorders are associated with a reduction in Post Motor Beta Rebound (PMBR), the characteristic increase in electrophysiological beta amplitude that follows a motor response. Patients had significantly reduced PMBR compared with healthy controls. PMBR was negatively correlated with core deficit score.While the symptoms constituting impoverished and disorganised mental activity are dissociable in schizophrenia, nonetheless, the variance that these two symptom domains share with impaired cognition and role function, appears to reflect a pathophysiological process that might be described as the core deficit of classical schizophrenia