[{"id":285,"title":"Create standalone simulation tools to facilitate collaboration between HEP and machine learning community","sha":"ff0489d51bdb17359cef823c1d6b7029","user_id":null,"state":"published","body":"Discussions at recent workshops have made it clear that one of the key barriers to collaboration between high energy physics and the machine learning community is access to training data. Recent successes in data sharing through the [HiggsML](http://doi.org/10.7483/OPENDATA.ATLAS.ZBP2.M5T8) and [Flavours of Physics](https://www.kaggle.com/c/flavours-of-physics/data) Kaggle challenges have borne much fruit, but required significant effort to coordinate.\r\n\r\nWhile static simulated datasets are useful for challenges, in the course of investigating new machine learning techniques it is advantageous to be able to generate training data on demand (e.g. Refs. [1](https://archive.ics.uci.edu/ml/datasets/HEPMASS), [2](https://archive.ics.uci.edu/ml/datasets/SUSY), [3](https://archive.ics.uci.edu/ml/datasets/HIGGS) ). \r\nTherefore we recommend efforts be made to produce the ingredients required to facilitate such collaboration:\r\n   * Specific challenges for HEP experiments should be fully specified such that minimal domain-specific knowledge is required to attack them.\r\n   * Stand-alone simulators should be made open source. They should be developed to be easy to use without domain-specific expertise, while still being representative of real experimental challenges. Such a simulation will permit non-HEP researchers to generate realistic HEP datasets for training and testing. These simulators could range from truth-level simulation of a hard scattering to fast simulation like [Delphes](http://dx.doi.org/10.1007/JHEP02(2014)057), to full [GEANT4](http://dx.doi.org/10.1016/S0168-9002(03)01368-8) simulation of sensor arrays.\r\n   * Performance metrics (objective functions) and operational constraints should be defined to evaluate proposed solutions.\r\n","created_at":"2016-02-26T21:34:14.721Z","updated_at":"2016-04-03T17:04:01.541Z","zenodo_id":99761,"doi":"http://dx.doi.org/10.5281/zenodo.46864","subject":null,"tags":["dslhc","machinelearning","datascience","open data","simulation"],"vote_count":3,"view_count":1,"score":3.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":25,"title":"Evaluating the potential quality of parliamentary candidates via realistic strategy game environments","sha":"8abf5ab31f1702c316bf2f2efdb86a49","user_id":49,"state":"published","body":"Politicians routinely gravitate towards positions of significant influence and responsibility, despite limited or absent experience in managing highly complex systems such as sociteies.. In these positions, they influence (or make) decisions and policies,  using reasoning based on a combination of limited information, personal political leanings, personal ambitions, and often limited understanding. By the time this power has already been wielded, it is typically too late to recover from mistakes.I propose that prospective members of pariliaments be evaluated - before being allowed into a position of power - by their performance in a series of challenging scenarios provided by a complex and realistic government strategy game. Think 'SimCity', but with emphases on data-rich simulations of prevailing economic, health, education and other systems. Following perhaps two or three weeks of this, a prospective parliamentary candidate would emerge with a performance profile, available to the public, as a reference point for potential voters.","created_at":"2015-02-11T22:14:00.337Z","updated_at":"2019-04-29T09:57:05.835Z","zenodo_id":22048,"doi":"http://dx.doi.org/10.5281/zenodo.15046","subject":null,"tags":["government","society","simulation","computing"],"vote_count":5,"view_count":1,"score":5.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":384,"title":"Apple and orange: comparing patient safety, health data and the airline industry ","sha":"0c68c30683be70fb7fff96032e13bf56","user_id":null,"state":"published","body":"Over the decades, the airline industry which was already very safe, has made further strides to improve safety. The current trend in patient safety and simulation conferences is to repeatedly draw parallel conclusions, decrying those in healthcare for not emulating the airline industry's carefully regulated practices. \r\n\r\nThis comparison only applies in limited areas of healthcare. We have made advances by adopting CRM principles in acute resuscitation. Checklists aid some surgical procedures. But running an airline is orders of magnitude simpler than healthcare. The physics of flight is indeed complex but can now be fully simulated. In contrast, we still struggle to simulate the physiome of single cells. Airline business logistics are also simpler. But imagine saying to 200 patients: the appendectomy flight is departing at 1530 - please recline your tables. \r\n\r\nIt is odd how poor we are at estimating risk. Many fear taking a flight (0.5 micromorts) much more than a general anesthetic (6 micromorts). We are pleased that both are much safer than they were, and yes, there are lessons to be learned from analysis of systems errors. \r\n\r\nBut let's not go marching blithely down to the orange grove of tightly regulated practices. The comparison has run out of juice. ","created_at":"2017-05-13T04:03:49.745Z","updated_at":"2017-05-14T06:00:40.393Z","zenodo_id":579641,"doi":"https://doi.org/10.5281/zenodo.579641","subject":null,"tags":["patient safety","simulation"],"vote_count":0,"view_count":1,"score":0.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]