Understanding the Socio-Economic Distribution and Consequences of Patterns of Multiple Deprivation: An Application of Self-Organising Maps. ESRI WP302. June 2009

In this paper we apply self organising maps (SOM) to a detailed set of material deprivation indicators from the Irish component of European Union Community Statistics on Income and Living Conditions (EU-SILC). The first stage of our analysis involves the identification and description of sixteen clusters of multiple deprivation that allow us to provide a detailed account of such deprivation in contemporary Ireland. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of the manner in which individuals experience their economic circumstances. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. It suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. This finding, combined with those relating to the role of socio-economic factors in accounting for cluster membership, confirms that a focus on a set of eight SOM macro clusters seems most appropriate if our interest lies in broad patterns stratification. For other purposes differentiation within clusters, which clearly takes a systematic form, may prove to be crucial

Tools for spatial density estimation

The purpose of this talk is to illustrate the main features and applications of two new Stata programs for spatial density estimation: spgrid and spkde. The spgrid program generates two-dimensional arrays of evenly spaced points spanning across any regular or irregular study region specified by the user. In turn, the spkde program carries out spatial kernel density estimation based on reference points generated by spgrid.

Largo teso: The Seven Studies for guitar by Maurizio Pisati

In this contribution, composer and interpreter talk about the Seven Studies from their respective points of view. Maurizio Pisati explains how he developed a new guitar, departing from a single study and arriving at the overall formal conception through timbres, techniques and articulations; and how the soloistic studies led him to a guitarled ensemble piece. Elena Càsoli deals with issues such as the score\u27s indications and the instrumental techniques

Understanding the Socio-Economic Distribution and Consequences of Patterns of Multiple Deprivation: An Application of Self-Organising Maps

In this paper we apply self organising maps (SOM) to a detailed set of material deprivation indicators from the Irish component of European Union Community Statistics on Income and Living Conditions (EU-SILC). The first stage of our analysis involves the identification and description of sixteen clusters of multiple deprivation that allow us to provide a detailed account of such deprivation in contemporary Ireland. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of the manner in which individuals experience their economic circumstances. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. It suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. This finding, combined with those relating to the role of socio-economic factors in accounting for cluster membership, confirms that a focus on a set of eight SOM macro clusters seems most appropriate if our interest lies in broad patterns stratification. For other purposes differentiation within clusters, which clearly takes a systematic form, may prove to be crucial.

Packet Classification via Improved Space Decomposition Techniques

P ack et Classification is a common task in moder n Inter net r outers. The goal is to classify pack ets into "classes" or "flo ws" according to some ruleset that looks at multiple fields of each pack et. Differ entiated actions can then be applied to the traffic depending on the r esult of the classification. Ev en though rulesets can be expr essed in a r elati v ely compact way by using high le v el languages, the r esulting decision tr ees can partition the sear ch space (the set of possible attrib ute v alues) in a potentially v ery lar ge ( and mor e) number of r egions. This calls f or methods that scale to such lar ge pr oblem sizes, though the only scalable pr oposal in the literatur e so far is the one based on a F at In v erted Segment T r ee [1 ]. In this paper we pr opose a new geometric technique called G-filter f or pack et classification on dimensions. G-filter is based on an impr o v ed space decomposition technique. In addition to a theor etical analysis sho wing that classification in G-filter has time complexity and slightly super -linear space in the number of rules, we pr o vide thor ough experiments sho wing that the constants in v olv ed ar e extr emely small on a wide range of pr oblem sizes, and that G-filter impr o v e the best r esults in the literatur e f or lar ge pr oblem sizes, and is competiti v e f or small sizes as well

A Scalable Algorithm for Metric High-Quality Clustering in Information Retrieval Tasks

We consider the problem of finding efficiently a high quality k-clustering of n points in a (possibly discrete) metric space. Many methods are known when the point are vectors in a real vector space, and the distance function is a standard geometric distance such as L1, L2 (Euclidean) or L2 2 (squared Euclidean distance). In such cases efficiency is often sought via sophisticated multidimensional search structures for speeding up nearest neighbor queries (e.g. variants of kd-trees). Such techniques usually work well in spaces of moderately high dimension say up to 6 or 8). Our target is a scenario in which either the metric space cannot be mapped into a vector space, or, if this mapping is possible, the dimension of such a space is so high as to rule out the use of the above mentioned techniques. This setting is rather typical in Information Retrieval applications. We augment the well known furthest-point-first algorithm for kcenter clustering in metric spaces with a filtering step based on the triangular inequality and we compare this algorithm with some recent fast variants of the classical k-means iterative algorithm augmented with an analogous filtering schemes. We extensively tested the two solutions on synthetic geometric data and real data from Information Retrieval applications. The main conclusion we draw is that our modified furthest-point-first method attains solutions of better or comparable quality within a fraction of the time used by the fast k-means algorithm. Thus our algorithm is valuable when either real time constraints or the large amount of data highlight the poor scalability of traditional clustering methods

Mapping Patterns of Multiple Deprivation Using Self-Organising Maps: An Application to EU-SILC Data for Ireland. ESRI WP286. March 2009

The development of conceptual frameworks for the analysis of social exclusion has somewhat out-stripped related methodological developments. This paper seeks to contribute to this process through the application of self-organising maps (SOMs) to the analysis of a detailed set of material deprivation indicators relating to the Irish case. The SOM approach allows us to offer a differentiated and interpretable picture of the structure of multiple deprivation in contemporary Ireland. Employing this approach, we identify 16 clusters characterised by distinct profiles across 42 deprivation indicators. Exploratory analyses demonstrate that position in the income distribution varies systematically by cluster membership. Moreover, in comparison with an analogous latent class approach, the SOM analysis offers considerable additional discriminatory power in relation to individuals’ experience of their economic circumstances. The results suggest that the SOM approach could prove a valuable addition to a ‘methodological platform’ for analysing the shape and form of social exclusion

A murine model of cerebral cavernous malformations with acute hemorrhage

Cavernomas are multi-lumen and blood-filled vascular malformations which form in the brain and the spinal cord. They lead to hemorrhage, epileptic seizures, neurological deficits, and paresthesia. An effective medical treatment is still lacking, and the available murine models for cavernomas have several limitations for preclinical studies. These include disease phenotypes that differ from human diseases, such as restriction of the lesions to the cerebellum, and absence of acute hemorrhage. Additional limitations of current murine models include rapid development of lesions, which are lethal before the first month of age. Here, we have characterized a murine model that recapitulates features of the human disease: lesions develop after weaning throughout the entire CNS, including the spinal cord, and undergo acute hemorrhage. This provides a preclinical model to develop new drugs for treatment of acute hemorrhage in the brain and spinal cord, as an unmet medical emergency for patients with cavernomas

Packet Classification via Improved Space Decomposition Techniques

Packet Classification is a common task in modern Internet routers. In a nutshell, the goal is to classify packets into ``classes\u27\u27 or ``flows\u27\u27 according to some ruleset that looks at multiple fields of each packet. Differentiated actions can then be applied to the traffic depending on the result of the classification. One way to approach the task is to model it as a point location problem in a multidimensional space, partitioned into a large number of regions, (up to $10^6$ or more, generated by the number of possible paths in the decision tree resulting from the specification of the ruleset). Many solutions proposed in the literature not to scale well with the size of the problem, with the exception of one based on a Fat Inverted Segment Tree. In this paper we propose a new geometric filtering technique, called {em g-filter}, which is competitive with the best result in the literature, and is based on an improved space decomposition technique. A theoretical worst case asymptotic analysis shows that classification in {em g-filter} has $O(1)$ time complexity, and space complexity close to linear in the number of rules. Additionally, thorough experiments show that the constants involved are extremely small on a wide range of problem sizes, and improve the best results in the literature. Finally, the g-filter method is not limited to 2-dimensional rules, but can handle any number of attributes with only a moderate increased overhead per additional dimension

La giustizia riparativa nell’esecuzione penale: riforme inattuate

Il presente contributo si sofferma, in una prospettiva evolutiva, sull’implementazione della giustizia riparativa nell’ordinamento penitenziario per adulti e minori. Dopo aver evidenziato i limitati spazi tradizionalmente attribuiti a tale istituto nell’esecuzione penale, sono esaminate le innovazioni e le occasioni mancate nel contesto dell’attuazione della delega per la «previsione di attività di giustizia riparativa e delle relative procedure, quali momenti qualificanti del percorso di recupero sociale sia in ambito intramurario sia nell’esecuzione delle misure alternative» contenuta nella legge n. 103/2017. La riflessione è, infatti, resa urgente dalla recente approvazione di una ulteriore delega in materia di giustizia riparativa «durante l’esecuzione della pena» ex lege n. 134/2021.This paper focuses on the evolution of Italian Prison Law (No. 354/1975) as far as restorative justice is concerned. After some preliminary observations about the traditional role of restorative justice at the post-conviction stage in the Italian legal framework, the author analyses the missed opportunities in the implementation of Law No. 103/2017, that aimed at enhancing the role of restorative justice in favour of adult and juvenile detainees. The need to investigate the shortcomings of the recent reforms is urgent in the light of the recent Law No. 134/2021, expressly addressing the need to implement a restorative justice framework in the context of the execution of criminal sanctions