A data mining based methodology for the multidimensional study of public open spaces

Public open spaces can only be apprehended from multiple simultaneous perspectives. Urban morphology traditional descriptive methods have recognized limitations in relating the polymorphic and polysemantic nature of these spaces’ attributes, derived from the different standpoints on their formal, historical and geographic idiosyncrasies. Identities and similarities may be disclosed by multivariate statistical analysis and data mining techniques by studying the relations between formal and intangible spatial properties in a multidimensional space. In an ongoing PhD research project we outline a method for the synchronic analysis and classification of the public open spaces, departing from a corpus of 126 Portuguese urban squares, whose analysis is intended to interactively (re)define it. Part of the work done so far is presented: (i) firming the concepts, criteria and attributes to extract; (ii) adaptation and/or creation of new analytical methods and tools; and (iii) research on multivariate analysis, data mining and data visualization techniques.info:eu-repo/semantics/acceptedVersio

Linking data mining, spatial analysis and algorithmic design: A review on a primer workshop based on Python

The field of data mining, the practical application of machine learning, has recently become a full flagged science known as Data Science. An interdisciplinary discipline in the intersection of A.I., computer science, statistics, data visualization and database management, its main objectives are pattern recognition/knowledge discovery in datasets and prediction/data modelling. The application of this latter objective has become recently the subject of intense debate following the case of its use outside the scientific research. From political campaigns to the first mortal accident involving a self-driven car, these events brought the field to the highlights and, although its tools are not new, the scale of their implementation raises important questions considering their application, the nature of personal digital data and free will. In research its application is most relevant in data rich fields and quantitative analysis. Here we can include spatial and urban analysis, which nowadays deal with huge datasets, e.g. combining Big Data from the internet, time series or unstructured data fluxes with urban form and structure, helping to assess or construct new investigation hypotheses. Using case-based reasoning and optimization data mining becomes a predictive tool able to assist the design process, producing scenarios or helping to explore constrained design solution spaces. The presentation will (i) briefly introduce the topic of data mining; (ii) its usage in urban analysis and design, and, mainly, (iii) report on a preliminary evaluation of the related workshop carried out in the context of the present seminar. The workshop introduces data mining to participants in a hands-on approach, focusing in simple tasks so concepts are internalized by playing with tools and scripting. The focus is on python scripting using Anaconda python data analysis package and Jupyter interactive Notebooks. In this way participants get a glimpse on one of the most flexible and widely used programming languages across a variety of fields, from algorithm design to data analysis, that is able of customize the tools that sometimes customize our own investigation or practice.info:eu-repo/semantics/publishedVersio

Development of liquid xenon detectors for medical imaging

In the present paper, we report on our developments of liquid xenon detectors for medical imaging, positron emission tomography and single photon imaging, in particular. The results of the studies of several photon detectors (photomultiplier tubes and large area avalanche photodiode) suitable for detection of xenon scintillation are also briefly described.Comment: 13 pages, 5 figures, presented on the International Workshop on Techniques and Applications of Xenon Detectors (Xenon01), ICRR, Univ. of Tokyo, Kashiwa, Japan, December 3-4, 2001 (submitted to proceedings

Neural networks with dynamical synapses: from mixed-mode oscillations and spindles to chaos

Understanding of short-term synaptic depression (STSD) and other forms of synaptic plasticity is a topical problem in neuroscience. Here we study the role of STSD in the formation of complex patterns of brain rhythms. We use a cortical circuit model of neural networks composed of irregular spiking excitatory and inhibitory neurons having type 1 and 2 excitability and stochastic dynamics. In the model, neurons form a sparsely connected network and their spontaneous activity is driven by random spikes representing synaptic noise. Using simulations and analytical calculations, we found that if the STSD is absent, the neural network shows either asynchronous behavior or regular network oscillations depending on the noise level. In networks with STSD, changing parameters of synaptic plasticity and the noise level, we observed transitions to complex patters of collective activity: mixed-mode and spindle oscillations, bursts of collective activity, and chaotic behaviour. Interestingly, these patterns are stable in a certain range of the parameters and separated by critical boundaries. Thus, the parameters of synaptic plasticity can play a role of control parameters or switchers between different network states. However, changes of the parameters caused by a disease may lead to dramatic impairment of ongoing neural activity. We analyze the chaotic neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I., 2004) and show that it has a collective nature.Comment: 7 pages, Proceedings of 12th Granada Seminar, September 17-21, 201