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"This is the witty, candid story of a daring young man who made his own way to the heights of American journalism and public life, of the great adventure that took him at only twenty years old straight from Harvard to almost four years in the shooting war in the South Pacific, and back, from a maverick New Hampshire weekly to an apprenticeship for Newsweek in postwar Paris, then to the Washington Bureau chief's desk, and finally to the apex of his career at The Washington Post." "Bradlee took the helm of The Washington Post in 1965. He and his reporters transformed it into one of the most influential and respected news publications in the world, reinvented modern investigative journalism, and redefined the way news is reported, published, and read. Under his direction, the paper won eighteen Pulitzer prizes. His leadership and investigative drive following the break-in at the Democratic National Committee led to the downfall of a president, and kept every president afterward on his toes." "Bradlee, backed every step of the way by the Graham family, challenged the federal government over the right to publish the Pentagon Papers - and won. His ingenuity, and the spirited reporting of Sally Quinn, now his wife, led to the creation of the Style Section, a revolutionary newspaper feature in its time, now copied by just about every paper in the country

Our daily life activity leaves digital trails in an increasing number of databases (commercial web sites, internet service providers, search engines, location tracking systems, etc). Personal digital trails are commonly exposed to accidental disclosures resulting from negligence or piracy and to ill-intentioned scrutinization and abusive usages fostered by fuzzy privacy policies. No one is sheltered because a single event (e.g., applying for a job or a credit) can suddenly make our history a precious asset. By definition, access control fails preventing trail disclosures, motivating the integration of the Limited Data Retention principle in legislations protecting data privacy. By this principle, data is withdrawn from a database after a predefined time period. However, this principle is difficult to apply in practice, leading to retain useless sensitive information for years in databases. In this paper, we propose a simple and practical data degradation model where sensitive data undergoes a progressive and irreversible degradation from an accurate state at collection time, to intermediate but still informative degraded states, up to complete disappearance when the data becomes useless. The benefits of data degradation is twofold: (i) by reducing the amount of accurate data, the privacy offence resulting from a trail disclosure is drastically reduced and (ii) degrading the data in line with the application purposes offers a new compromise between privacy preservation and application reach. We introduce in this paper a data degradation model, analyze its impact over core database techniques like storage, indexation and transaction management and propose degradation-aware techniques.

Our daily life activity leaves digital trails in an increasing number of databases (commercial web sites, internet service providers, search engines, location tracking systems, etc). Personal digital trails are commonly exposed to accidental disclosures resulting from negligence or piracy and to ill-intentioned scrutinization and abusive usages fostered by fuzzy privacy policies. No one is sheltered because a single event (e.g., applying for a job or a credit) can suddenly make our history a precious asset. By definition, access control fails preventing trail disclosures, motivating the integration of the Limited Data Retention principle in legislations protecting data privacy. By this principle, data is withdrawn from a database after a predefined time period. However, this principle is difficult to apply in practice, leading to retain useless sensitive information for years in databases. In this paper, we propose a simple and practical data degradation model where sensitive data undergoes a progressive and irreversible degradation from an accurate state at collection time, to intermediate but still informative degraded states, up to complete disappearance when the data becomes useless. The benefits of data degradation is twofold: (i) by reducing the amount of accurate data, the privacy offence resulting from a trail disclosure is drastically reduced and (ii) degrading the data in line with the application purposes offers a new compromise between privacy preservation and application reach. We introduce in this paper a data degradation model, analyze its impact over core database techniques like storage, indexation and transaction management and propose degradation-aware techniques.

Our daily life activity leaves digital trails in an increasing number of databases (commercial web sites, internet service providers, search engines, location tracking systems, etc). Personal digital trails are commonly exposed to accidental disclosures resulting from negligence or piracy and to ill-intentioned scrutinization and abusive usages fostered by fuzzy privacy policies. No one is sheltered because a single event (e.g., applying for a job or a credit) can suddenly make our history a precious asset. By definition, access control fails preventing trail disclosures, motivating the integration of the Limited Data Retention principle in legislations protecting data privacy. By this principle, data is withdrawn from a database after a predefined time period. However, this principle is difficult to apply in practice, leading to retain useless sensitive information for years in databases. In this paper, we propose a simple and practical data degradation model where sensitive data undergoes a progressive and irreversible degradation from an accurate state at collection time, to intermediate but still informative degraded states, up to complete disappearance when the data becomes useless. The benefits of data degradation is twofold: (i) by reducing the amount of accurate data, the privacy offence resulting from a trail disclosure is drastically reduced and (ii) degrading the data in line with the application purposes offers a new compromise between privacy preservation and application reach. We introduce in this paper a data degradation model, analyze its impact over core database techniques like storage, indexation and transaction management and propose degradation-aware techniques.

AbstractVariability in infiltration characteristics of soils creates need for selection of appropriate infiltration model. Recently, a novel infiltration model was proposed and reported to perform excellently in estimating infiltration rate of soils of Kurukshetra, India, however, this model need to be tested for its reliability at global level. In this regard, the present study analyses the reliability of the novel model based on infiltration database comprising of 16 data sets from different parts of the world to arrive at some generalized results on the reliability of novel model as compared to commonly used infiltration models. Comparative analysis reveals that out of the 16 data sets for 9 sites (57%). Horton model was found to be best model while novel model was found as best‐fit model in five cases (31%). Based on the present study and earlier investigations, it may be inferred that novel model could be a useful model to estimate infiltration rate in loams.

This article is about how novices become experts in the domain of piano playing, focusing on how they develop internalized models of other people, a form of culture in the mind. Six novice and nine junior expert college students were individually asked to practice a short piece of music; they were asked to verbalize what they thought during the exercise about the performance they were executing and how they planned to perform it after their practice sessions had been concluded. The novices seemed to have internalized a generalized 'other' in their mind, who commanded them to perform accurately and smoothly as goals of the exercise. The junior experts seemed to possess also a model of the audience in their mind, from the perspective of whom they could check and refine their performance. Interviews with two concert class pianists showed that they possessed not only generalized others but also specific others in their mind and could refine their performance based on these models. How experts can be creative in spite of a set of constraints posed by the culture is discussed.

The exchange of research data and physical specimens has become an issue of major importance for modern research. Many reports indicate problems with quality, trustworthiness and reproducibility of research results, mainly due to poor documentation of the data generation or the collection of specimens. The significant impact of flawed research results on health, economics and political decisions has frequently been stated. Consequently, professional societies and research initiatives call for improved and standardised documentation of the data and specimens used in research studies. Provenance information documents the evolution of an object and can be used to assess its quality and reliability. This deliverable defines components of distributed provenance information to enable interlinking of provenance information generated in different organisations involved in the research process, such as biobanks, research centres, universities or analytical laboratories. The distributed provenance information model builds on an existing provenance information standard, W3C PROV, and follows a general provenance composition pattern. Both W3C PROV and provenance composition pattern is described in this document. Since understanding of the term "provenance information" differs across different domains and research communities, this deliverable firstly harmonises this understanding by providing a general explanation of how provenance information is generated and used. In particular, this deliverable defines a connector, that is a provenance component containing technical information to traverse through provenance information. The connector is subsequently added to provenance information generated by different organisations. This deliverable also defines how to interpret identifiers of provenance structures in a distributed environment and how to include and interpret persistent identifiers of documented objects. This deliverable deals with the common provenance model developed as a part of a standardisation process in the ...