Neutral evolution and turnover over centuries of English word popularity

Here we test Neutral models against the evolution of English word frequency and vocabulary at the population scale, as recorded in annual word frequencies from three centuries of English language books. Against these data, we test both static and dynamic predictions of two neutral models, including the relation between corpus size and vocabulary size, frequency distributions, and turnover within those frequency distributions. Although a commonly used Neutral model fails to replicate all these emergent properties at once, we find that modified two-stage Neutral model does replicate the static and dynamic properties of the corpus data. This two-stage model is meant to represent a relatively small corpus (population) of English books, analogous to a `canon', sampled by an exponentially increasing corpus of books in the wider population of authors. More broadly, this mode -- a smaller neutral model within a larger neutral model -- could represent more broadly those situations where mass attention is focused on a small subset of the cultural variants.Comment: 12 pages, 5 figures, 1 tabl

Near Resonantly Enhanced Schlieren for Wake Flow Visualisation in Shock Tunnels

A new variant of the resonantly enhanced schlieren or shadowgraph technique has been developed for visualising flows with small density gradients using seeded lithium metal as the resonant species. The novelty of the technique lies in the use of a diode laser as the light source for the visualisation rather than systems based upon solid-state-pumped dye lasers or spectral lamps. We present time-resolved visualisations of near-wake flows around a cylinder in a hypersonic freestream in a shock tunnel, showing flow structures that cannot be resolved using a conventional standard schlieren system. Furthermore, a method of removing, at least partially, the limitation related to line-ofsight visualisation is demonstrated

Combining Classification and Clustering for Tweet Sentiment Analysis

The goal of sentiment analysis is to determine opinions, emotions, and attitudes presented in source material. In tweet sentiment analysis, opinions in messages can be typically categorized as positive or negative. To classify them, researchers have been using traditional classifiers like Naive Bayes, Maximum Entropy, and Support Vector Machines (SVM). In this paper, we show that a SVM classifier combined with a cluster ensemble can offer better classification accuracies than a stand-alone SVM. In our study, we employed an algorithm, named 'C POT.3'E-SL, capable to combine classifier and cluster ensembles. This algorithm can refine tweet classifications from additional information provided by clusterers, assuming that similar instances from the same clusters are more likely to share the same class label. The resulting classifier has shown to be competitive with the best results found so far in the literature, thereby suggesting that the studied approach is promising for tweet sentiment classification.Capes (Proc. DS-7253238/D)CNPq (Proc. 303348/2013-5)FAPESP (Proc. 2013/07375-0 and 2010/20830-0

MACOC: a medoid-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

The benefits and challenges of participating in Participatory Guarantee Systems (PGS) initiatives following institutional formalization in Chile

Participatory Guarantee Systems (PGS) provide an alternative certification system for smallholder organic farmers producing for the domestic market. Officially recognized in Chile since 2007, PGS certification has grown in momentum in recent years. We used semi-structured interviews, surveys and participant observations to shed light on the PGS movement in Chile and the respective governance framework. Our results indicate that after PGS formalization, Chilean PGS initiatives struggle to comply with wide-ranging administrative procedures similar to those requested for third-party certification (TPC). Furthermore, lacking resources among PGS initiatives inhibit the PGS movement from impacting the national discourse. We present two PGS initiatives and their organizational structures, exploring ‘who’ participate, ‘how’ participation occurs, and ‘what kind’ of participation takes place. Our results indicate that the interaction as well as the exchange of knowledge among PGS members is a central benefit perceived by PGS participants. However, regular PGS member participation is hindered by the required time investment, the distance that PGS members need to travel and the perceived lacking expertise they have

Konsument*innenbewusstsein, -vertrauen und -partizipation in Partizipativen Garantiesystemen (PGS) in Mexiko, Chile und Bolivien

Dieser Beitrag untersucht Konsument*innenbewusstsein, -partizipation und -vertrauen in fünf Partizipativen Garantiesystemen (PGS) in Mexiko, Chile und Bolivie