The aim of this article is to inquire about potential relationship between change of crime rates and change of gross domestic product growth rate, based on historical statistics of Japan. This national-level study used a dataset covering 88 years and 13 attributes. The data were processed with the self-organizing map, separation power checked by our ScatterCounter method, assisted by other clustering methods and statistical methods for obtaining comparable results. The article is an exploratory application of the SOM in research of (...) criminal phenomena through processing of multivariate data. The research confirmed previous findings that SOM was able to cluster efficiently the present data and characterize these different clusters. Other machine learning methods were applied to ensure clusters computed with SOM. The correlations obtained between GDP and other attributes were mostly weak, with a few of them interesting. (shrink)

This study investigated Finnish nurses’ experiences and views on end-of-life decision making and compared them with physicians’ views. For this purpose, a questionnaire was sent to 800 nurses, of which 51% responded. Most of the nurses had a positive attitude towards and respect for living wills, more often than physicians. Most also believed that a will had an effect on decision making. Almost all of the nurses considered it their responsibility to talk to physicians about respecting living wills. Do-not-resuscitate (DNR) (...) orders were often interpreted to imply partial or complete palliative (symptom-orientated) care, which may cause confusion. Half of the nurses reported that a DNR decision was discussed always or often with a patient who was able to communicate; physicians were more positive in this respect. Surprisingly, many nurses (44%) stated that active treatment continued too long. Two-thirds thought that their opinions were taken into account sufficiently, even though only half believed that, in general, they had some impact. (shrink)

Technology for local textual inference is central to producing a next generation of intelligent yet robust human language processing systems. One can think of it as Information Retrieval++. It is needed for a search on male fertility may be affected by use of cell phones to match a document saying Startling new research into mobile phones suggests they can reduce a man’s sperm count up to 30%, despite the fact that the only word overlap is phones. But textual inference is (...) useful more broadly. It is an enabling technology for applications of document interpretation, such as customer response management, where one would like to conclude from the message My Squeezebox regularly skips during music playback that Sender has set up Squeezebox and Sender can hear music through Squeezebox, and information extraction, where from the text Jorma Ollila joined Nokia in 1985 and held a variety of key management positions before taking the helm in 1992, one wants to extract that Jorma Ollila has served as the CEO of Nokia, a relation that might be more formally denoted as role(CEO, Nokia, Jorma Ollila). Textual inference is a difficult problem (as the results from early evaluations have shown): current systems do statistically better than random guessing, but not by very much. Nevertheless, it is also an area where there is promising developing technology and a good deal of natural language community interest. In other words, it is an ideal research problem. To further this research agenda, data sets have been constructed to assess textual inference systems. This paper examines how the task of textual inference has been and should be defined and discusses what kind of evaluation data is appropriate for the task.1.. (shrink)