1,721,294 research outputs found
Fete de la Mutualite sous la presidente de M. Viviani Ministere du Travail
No full textFETE DE LA MUTUALITE SOUS LA PRESIDENTE DE M. VIVIANI MINISTERE DU TRAVAIL
Fete de la Mutualite sous la presidente de M. Viviani Ministere du Travail ( -
Relativistic Covariance of the 2-Nucleon Contact Interactions
No full textRelativistic covariance restricts the number of two-nucleon contact operators. We show that these constraints can be implemented starting from a complete set of relativistically invariant contact operators up to order Q in the parity-violating sector and order Q2 in the parity-conserving one
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Towards a more refined model of three-nucleon interaction
No full textWe derive the minimal form of the two-derivative three-nucleon contact interaction by imposing all constraints from discrete symmetries and Fierz identities. In order to comply with the requirements of Poincare' covariance, a basis of operators depending on relative momenta is used. The resulting interaction depends on 10 unknown low-energy constants and leads to a three-nucleon potential which we give in local form in coordinate space
Subleading contributions to the three-nucleon contact interaction
No full textWe obtain a minimal form of the two-derivative three-nucleon contact Lagrangian, by imposing all constraints deriving from discrete symmetries, Fierz identities, and Poincare' covariance. The resulting interaction, depending on 10 unknown low-energy constants, leads to a three-nucleon potential which we give in a local form in configuration space. We also consider the leading (no-derivative) four-nucleon interaction and show that there exists only one independent operator
Bakamjian-Thomas mass operator for few-nucleon systems from chiral dynamics
No full textWe present an exploratory study consisting in the formulation of a relativistic quantum mechanics to describe few-nucleon systems at low energy, starting from the quantum field theoretical chiral Lagrangian involving pions and nucleons. To this aim we construct a Bakamjian-Thomas mass operator and perform a truncation of the Fock space that respects at each stage the relativistic covariance. Such a truncation is justified, at sufficiently low energy, in the framework of a systematic chiral expansion. As an illustration we discuss the bound-state observables and low-energy phase shifts of nucleon-nucleon and pion-nucleon scattering at the leading order of our scheme
Assessing User Privacy on Social Media: The Twitter Case Study
Full text linkAt the time of writing, nearly four billion people worldwide employ social media platforms such as Facebook, Instagram, WeChat, TikTok, etc. to share content of various kinds, which may also include personal data. In addition to this, users interact with members of the virtual community, leaving behind important behavioral traces. In most cases, people do not have a full understanding of who will be able to access and use such a body of information, and for what purposes. Although social platforms provide users with some tools to protect their privacy, the very nature of these technologies and the psychological characteristics of users often lead them to ignore such solutions.
To address this issue, in this paper we aim to propose a model for assessing the privacy of users on social media by identifying the critical aspects associated with their content and interactions generated on such platforms. This model, in particular, considers distinct features, of different kinds, that capture the level of users’ exposure with respect to privacy. These features, dropped into a vector space, are used to derive a score that expresses, in a measurable way, the privacy risk of users compared to the information available on social media about them. The proposed model is instantiated and tested on data collected from the microblogging platform Twitter, on which the results of the experimental evaluation are analyzed. Specifically, the model is tested by considering both a binary scenario, i.e., where users’ privacy is evaluated as at risk or not, a multi-class scenario, i.e., where their privacy is evaluated against different risk ranges, and a ranking scenario, i.e., where the users are ranked according to their privacy assessment
Unveiling the Privacy Risk: A Trade-off between User Behavior and Information Propagation in Social Media
Full text linkThis study delves into the privacy risks associated with user interactions in complex networks such as those generated on social media platforms. In such networks, potentially sensitive information can be extracted and/or inferred from explicitly user-generated content and its (often uncontrolled) dissemination. Hence, this preliminary work first studies an unsupervised model generating a privacy risk score for a given user, which considers both sensitive information released directly by the user and content propagation in the complex network. In addition, a supervised model is studied, which identifies and incorporates features related to privacy risk. The results of both multi-class and binary privacy risk classification for both models are presented, using the Twitter platform as a scenario, and a publicly accessible purpose-built dataset
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