[{"id":64,"title":"Piracy and (Digital) Economy - Beneficial side-effects of outlawed acts and black market economies for societies. ","sha":"2c6e126f305abe33c168c66d25be24f8","user_id":244,"state":"published","body":"There are various pointers in recent research that suggest that product piracy as a means of distribution is a mayor factor in not only innovation but also education and economic advancement. Independent studies also show  positive effects for the establishment of the class of goods that have been pirated on local markets. \r\n\r\nThis is especially true if looked at in combination with weak copyright protection mechanisms.The book market in Germany in Early Modern Europe - for example- could not have had such an explosive growth in comparison to the book market Great Britain at the same time without widespread and cheap illegal reprints available for a low price, no effective censorship nor copyright protection (Eisenstein_1980, Gieseke_1998). While Great Britain had a strict copyright regime in place around 1710, Germany did not establish a common copyright until around 1830. This lead to a wide-spread distribution of knowledge, a highly skilled workforce, a vibrant civil society and to Germany closing up and surpassing other industrial nations in the 19th century (Höffner_2010).\r\n\r\nThis research idea aims at linking these findings to the recent debate on internet piracy, copyright protections, pricing of goods and measures to foster innovation and economic growth.\r\n\r\nElizabeth_Eisenstein,1980:The.Printing.Press.as.an.Agent.of.Change.\r\nMichael_Gieseke,1998:Der.Buchdruck.in.der.frühen.Neuzeit.(Printing.in.Early.Modern.Europe)\r\nEckhard_Höffner,2010:Geschichte.und.Wesen.des.Urheberrechts.(History.of.Copyright.and.Related.Rights)","created_at":"2015-02-19T10:20:08.942Z","updated_at":"2018-05-21T14:50:35.520Z","zenodo_id":23127,"doi":"http://dx.doi.org/10.5281/zenodo.15519","subject":null,"tags":["piracy","software","torrent","information","data_exchange","knowledge","history_of_ideas","education","workforce","distribution_channels","logistics","piracy_of_goods","black_market_economy","product_piracy"],"vote_count":8,"view_count":1,"score":8.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":294,"title":"Kickstarting research into end-to-end trigger systems","sha":"6e2cc286c54020f08d774487315585e8","user_id":null,"state":"published","body":"The data volumes (_**O**_(TB/s)) created at the Large Hadron Collider (LHC) are too large to record. Typical rejection factors are _**O**_(100-1000), and using as little CPU time as possible to reject an event is the goal. More powerful decision features take more CPU time to construct, therefore the discrimination power of decisions is limited by the available CPU time.\r\n\r\nCurrent approaches rely on hand-crafted features, and hand tuned decision cascades. We propose the possibility of using deep learning techniques to construct an end-to-end system (using the raw data electronics data as input) that learns the decision function of the trigger system (the global HLT decision). Deep Neural Networks can be efficiently evaluated on dedicated hardware and can be used to preempt the decision of the full system.\r\n\r\nFor LHCb we estimate that 750GB of data contain about 10000000 candidates of which 100000 will be accepted by the trigger system.\r\n\r\nTo kickstart research into this new area of research each of the four LHC experiments should release a few seconds of HLT output as well as the corresponding input. This will allow collaboration with experts from the field of machine-learning and cooperation between the experiments to solve this challenging problem.","created_at":"2016-04-01T11:31:52.210Z","updated_at":"2016-04-16T13:09:00.853Z","zenodo_id":105137,"doi":"http://dx.doi.org/10.5281/zenodo.48784","subject":null,"tags":["lhc","deep learning","machinelearning","data_exchange"],"vote_count":1,"view_count":1,"score":1.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":478,"title":"Detecting the delimiter in CSVs that lie","sha":"f3e6d0c680c88caf54c1e4b0ae995768","user_id":null,"state":"published","body":"File extensions for data sharing sometimes lie about their contents. \r\n\r\nHere is an algorithm to infer the actual delimiter of a CSV, TSV or any related format:\r\n\r\n- Assume that alpha-numeric characters (A-Z, a-z, 0-9) and the period/full stop (.) are cannot be delimiters.\r\n- Begin with input text _a_.\r\n- Store a short sample of _a_ as _b_, by copying _n_ lines from the input text _a_.\r\n- Rank every character that appears in _b_ by frequency, creating candidate delimiters _c_\r\n- For every candidate delimiter _d_ in _c_, split each line in _b_. If every line has the same number of splits, _d_ is the delimiter.\r\n\r\nAn implementation of this idea is available [on Zenodo](https://zenodo.org/record/1323186) and within the [Python Package Index](https://pypi.org/project/detect-delimiter/).","created_at":"2018-07-29T08:12:03.204Z","updated_at":"2020-10-18T13:32:41.085Z","zenodo_id":1402269,"doi":"https://doi.org/10.5281/zenodo.1402269","subject":null,"tags":["text processing","data_exchange","data-manipulation"],"vote_count":2,"view_count":1,"score":2.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]