620,350 research outputs found
Discovery Is Never By Chance: Designing for (Un)Serendipity
Serendipity has a long tradition in the history of science as having played a key role in many significant discoveries. Computer scientists, valuing the role of serendipity in discovery, have attempted to design systems that encourage serendipity. However, that research has focused primarily on only one aspect of serendipity: that of chance encounters. In reality, for serendipity to be valuable chance encounters must be synthesized into insight. In this paper we show, through a formal consideration of serendipity and analysis of how various systems have seized on attributes of interpreting serendipity, that there is a richer space for design to support serendipitous creativity, innovation and discovery than has been tapped to date. We discuss how ideas might be encoded to be shared or discovered by ‘association-hunting’ agents. We propose considering not only the inventor’s role in perceiving serendipity, but also how that inventor’s perception may be enhanced to increase the opportunity for serendipity. We explore the role of environment and how we can better enable serendipitous discoveries to find a home more readily and immediately
Possibility of observing MSSM charged Higgs in association with a W boson at LHC
Possibility of observing associated production of charged Higgs and W boson
in the framework of MSSM at LHC is studied. Both leptonic and hadronic decays
of W boson are studied while the charged Higgs boson is considered to decay to
a lepton and a neutrino. Therefore two search categories are defined
based on the leptonic and hadronic final states, i.e.
and where or and is a light jet from
decay. The discovery chance of the two categories is evaluated at an
integrated luminosity of 300 \invfb at LHC. It is shown that both leptonic and
hadronic final states have the chance of discovery at high \tanb. Finally
and contours are provided for both search categories.Comment: 20 pages, 19 figure
Forecasting stock prices using Genetic Programming and Chance Discovery
In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning techniques have been utilized to predict movements in financial markets. Machine learning classifiers involve extending the past experiences into the future. However the rareness of some events makes difficult to create a model that detect them. For example bubbles burst and crashes are rare cases, however their detection is crucial since they have a significant impact on the investment. One of the main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic Programming has limitation to deal with unbalanced environments. In a previous work we described the Repository Method, it is a technique that analyses decision trees produced by Genetic Programming to discover classification rules. The aim of that work was to forecast future opportunities in financial stock markets on situations where positive instances are rare. The objective is to extract and collect different rules that classify the positive cases. It lets model the rare instances in different ways, increasing the possibility of identifying similar cases in the future. The objective of the present work is to find out the factors that work in favour of Repository Method, for that purpose a series of experiments was performed.Forecasting, Chance discovery, Genetic programming, machine learning
Discovering New Variable Stars at Key Stage 3
Details of the London pilot of the `Discovery Project' are presented, where
university-based astronomers were given the chance to pass on some real and
applied knowledge of astronomy to a group of selected secondary school pupils.
It was aimed at students in Key Stage 3 of their education, allowing them to be
involved in real astronomical research at an early stage of their education,
the chance to become the official discoverer of a new variable star, and to be
listed in the International Variable Star Index database, all while learning
and practising research-level skills. Future plans are discussed.Comment: 10 pages, 1 figur
Change and planning in chance discovery.
The discovery of risks and opportunities, known collectively as chances, can have a significant impact on decision making. Chances (risks or opportunities) can be discovered from our daily observations and background knowledge. A person can easily identify chances in a news article. In doing so, the person combines the new information in the article with some background knowledge. Hence, we develop a deductive system to discover relative chances with respect to a particular chance seeker. A chance discovery system that uses a general purpose knowledge base and specialized reasoning algorithms is proposed. The thesis evaluates the implementation of this chance discovery system and discusses the achievements and limitations of its elements, such as Natural Language Processing Tool, Knowledge Entry Tool, Inference Engine and Planner. Finally, A case study about a virtual transportation planning domain implemented using SHOP planner is presented. Example chances are detected in this domain. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .W89. Source: Masters Abstracts International, Volume: 44-03, page: 1418. Thesis (M.Sc.)--University of Windsor (Canada), 2005
Conditions for detecting CP violation via neutrinoless double beta decay
Neutrinoless double beta decay data together with information on the absolute
neutrino masses obtained from the future KATRIN experiment and/or astrophysical
measurements give a chance to find CP violation in the lepton sector with
Majorana neutrinos. We derive and discuss necessary conditions which make
discovery of such CP violation possible for the future neutrino oscillation and
mass measurements data.Comment: 15 pages, 4 figures, RevTe
Serendipity: why some organizations are luckier than others
Serendipity refers to the accidental discovery of something valuable. It is sometimes presented as an element of organizational learning but has been the object of scarce research. In this paper, I discuss the notion of serendipity in the organizational context, and elaborate a model of organizational serendipity. Four building blocks are considered: the conditions that facilitate serendipitous discovery, the search for a solution for a given problem, a process of bisociation leading to the combination of previously unrelated skills or information, and the discovery of an unexpected solution to a different problem. I also discuss what organizations can do to improve the chances of serendipity.serendipity; search; bisociation; chance; accidental discoveries; unintentional learning
Ugarit
In 1928 a Syrian peasant farmer stumbled by chance onto a funerary vault of ancient provenance about half a mile from the Mediterranean coastline of Syria and about six miles north of the modern-day city of Latakia. This unforeseen discovery led to an archaeological excavation ofTell Ras Shamra (Cape Fennel) by the eminent French excavator Claude Schaeffer. What Schaeffer\u27s team unearthed was not merely an ancient tomb, but a city complete with palaces, private homes, temples, and streets paved with stone
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