53,010 research outputs found
Deficient Reasoning for Dark Matter in Galaxies
Astronomers have been using the measured luminosity to estimate the {\em
luminous mass} of stars, based on empirically established mass-to-light ratio
which seems to be only applicable to a special class of stars---the
main-sequence stars---with still considerable uncertainties. Another basic tool
to determine the mass of a system of stars or galaxies comes from the study of
their motion, as Newton demonstrated with his law of gravitation, which yields
the {\em gravitational mass}. Because the luminous mass can at best only
represent a portion of the gravitational mass, finding the luminous mass to be
different or less than the gravitational mass should not be surprising. Using
such an apparent discrepancy as a compelling evidence for the so-called dark
matter, which has been believed to possess mysterious nonbaryonic properties
and present a dominant amount in galaxies and the universe, seems to be too far
a stretch when seriously examining the facts and uncertainties in the
measurement techniques. In our opinion, a galaxy with star type distribution
varying from its center to edge may have a mass-to-light ratio varying
accordingly. With the thin-disk model computations based on measured rotation
curves, we found that most galaxies have a typical mass density profile that
peaks at the galactic center and decreases rapidly within of the
cut-off radius, and then declines nearly exponentially toward the edge. The
predicted mass density in the Galactic disk is reasonably within the reported
range of that observed in interstellar medium. This leads us to believe that
ordinary baryonic matter can be sufficient for supporting the observed galactic
rotation curves; speculation of large amount of non-baryonic matter may be
based on an ill-conceived discrepancy between gravitational mass and luminous
mass which appears to be unjustified
Knowledge-based systems and geological survey
This personal and pragmatic review of the philosophy underpinning methods of geological surveying suggests that important influences of information technology have yet to make their impact. Early approaches took existing systems as metaphors, retaining the separation of maps, map explanations and information archives, organised around map sheets of fixed boundaries, scale and content. But system design should look ahead: a computer-based knowledge system for the same purpose can be built around hierarchies of spatial objects and their relationships, with maps as one means of visualisation, and information types linked as hypermedia and integrated in mark-up languages. The system framework and ontology, derived from the general geoscience model, could support consistent representation of the underlying concepts and maintain reference information on object classes and their behaviour. Models of processes and historical configurations could clarify the reasoning at any level of object detail and introduce new concepts such as complex systems. The up-to-date interpretation might centre on spatial models, constructed with explicit geological reasoning and evaluation of uncertainties. Assuming (at a future time) full computer support, the field survey results could be collected in real time as a multimedia stream, hyperlinked to and interacting with the other parts of the system as appropriate. Throughout, the knowledge is seen as human knowledge, with interactive computer support for recording and storing the information and processing it by such means as interpolating, correlating, browsing, selecting, retrieving, manipulating, calculating, analysing, generalising, filtering, visualising and delivering the results. Responsibilities may have to be reconsidered for various aspects of the system, such as: field surveying; spatial models and interpretation; geological processes, past configurations and reasoning; standard setting, system framework and ontology maintenance; training; storage, preservation, and dissemination of digital records
Technology assessment between risk, uncertainty and ignorance
The use of most if not all technologies is accompanied by negative side effects, While we may profit from todayâs technologies, it is most often future generations who bear most risks. Risk analysis therefore becomes a delicate issue, because future risks often cannot be assigned a meaningful occurance probability. This paper argues that technology assessement most often deal with uncertainty and ignorance rather than risk when we include future generations into our ethical, political or juridal thinking. This has serious implications as probabilistic decision approaches are not applicable anymore. I contend that a virtue ethical approach in which dianoetic virtues play a central role may supplement a welfare based ethics in order to overcome the difficulties in dealing with uncertainty and ignorance in technology assessement
Using spatio-temporal continuity constraints to enhance visual tracking of moving objects
We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-temporal continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-temporal continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video
Models of everywhere revisited: a technological perspective
The concept âmodels of everywhereâ was first introduced in the mid 2000s as a means of reasoning about the
environmental science of a place, changing the nature of the underlying modelling process, from one in which
general model structures are used to one in which modelling becomes a learning process about specific places, in
particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another
it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere,
models of everything and models at all times, being constantly re-evaluated against the most current
evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities.
However, the approach has, as yet, not been fully utilised or explored. This paper examines the
concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first
proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud
computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again
at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the
remaining research questions. The paper concludes by identifying the key elements of a research agenda that
should underpin such experimentation and deployment
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