About: Fingerprint (computing) is a research topic. Over the lifetime, 20189 publications have been published within this topic receiving 201984 citations. The topic is also known as: digital fingerprint & Empreinte numérique.
Papers published on a yearly basis
•10 Mar 2005
TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
Abstract: A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators
01 Apr 1992
TL;DR: This document describes the MD5 message-digest algorithm, which takes as input a message of arbitrary length and produces as output a 128-bit "fingerprint" or "message digest" of the input.
Abstract: This document describes the MD5 message-digest algorithm. The algorithm takes as input a message of arbitrary length and produces as output a 128-bit "fingerprint" or "message digest" of the input. This memo provides information for the Internet community. It does not specify an Internet standard.
13 Dec 2017
TL;DR: This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices.
Abstract: Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.
TL;DR: It is argued that imaging provides a useful way to define functional fingerprints because it is possible to compare activations across many cortical areas and across a wide range of tasks.
Abstract: The functions of a cortical area are determined by its extrinsic connections and intrinsic properties. Using the database CoCoMac, we show that each cortical area has a unique pattern of corticocortical connections — a ‘connectional fingerprint’. We present examples of such fingerprints and use statistical analysis to show that no two areas share identical patterns. We suggest that the connectional fingerprint underlies the observed cell-firing differences between areas during different tasks. We refer to this pattern as a ‘functional fingerprint’ and present examples of such fingerprints. In addition to electrophysiological analysis, functional fingerprints can be determined by functional brain imaging. We argue that imaging provides a useful way to define such fingerprints because it is possible to compare activations across many cortical areas and across a wide range of tasks.
TL;DR: Simple formulas are derived showing how the progress of a physical mapping project is affected by the nature of the fingerprinting scheme, and the analytic considerations involved in selecting an appropriate fingerprinting schemes for a particular project are discussed.
Abstract: Results from physical mapping projects have recently been reported for the genomes of Escirerichiu coli, Saccharomycee cerevisiae, and Caenorhabditis elegune, and similar projects are currently being planned for other organisms. In such projects, the physical map is assembled by first “fingerprinting” a large number of clones chosen at random from a recombinant library and then inferring overlaps between clones with sufficiently similar fingerprints. Although the basic approach is the same, there are many possible choices for the fingerprint used to characterize the clones and the rules for declaring overlap. In this paper, we derive simple formulas showing how the progress of a physical mapping project is affected by the nature of the Angerprinting scheme. Using these formulas, we discuss the analytic considerations involved in selecting an appropriate Angerprinting scheme for a particular project. 63 1998 Academic Press, Inc.
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