Formalizing Triggers: A Learning Model for Finite Spaces

In a recent seminal paper, Gibson and Wexler (1993) take important steps to formalizing the notion of language learning in a (finite) space whose grammars are characterized by a finite number of parameters. They introduce the Triggering Learning Algorithm (TLA) and show that even in finite space convergence may be a problem due to local maxima. In this paper we explicitly formalize learning in finite parameter space as a Markov structure whose states are parameter settings. We show that this captures the dynamics of TLA completely and allows us to explicitly compute the rates of convergence for TLA and other variants of TLA e.g. random walk. Also included in the paper are a corrected version of GW's central convergence proof, a list of "problem states" in addition to local maxima, and batch and PAC-style learning bounds for the model.

Instability of anthocyanin accumulation in Vitis vinifera L. var. Gamay Freaux suspension cultures

The inherent instability of metabolite production in plant cell culture-based bioprocessing is a major problem hindering its commercialization. To understand the extent and causes of this instability, this study was aimed at understanding the variability of anthocyanin accumulation during long-term subcultures, as well as within subculture batches, in Vitis vinifera cell cultures. Therefore, four cell line suspensions of Vitis vinitera L. var. Gamay Freaux, A, B, C and D, originated from the same callus by cell-aggregate cloning, were established with starting anthocyanin contents of 2.73 +/- 0.15, 1.45 +/- 0.04, 0.77 +/- 0.024 and 0.27 +/- 0.04 CV (Color Value)/g-FCW (fresh cell weight), respectively. During weekly subculturing of 33 batches over 8 months, the anthocyanin biosynthetic capacity was gradually lost at various rates, for all four cell lines, regardless of the significant difference in the starting anthocyanin content. Contrary to this general trend, a significant fluctuation in the anthocyanin content was observed, but with an irregular cyclic pattern. The variabilities in the anthocyanin content between the subcultures for the 33 batches, as represented by the variation coefficient (VC), were 58, 57, 54, and 84% for V vinifera cell lines A, B, C and D, respectively. Within one subculture, the VCs from 12 replicate flasks for each of 12 independent subcultures were averaged, and found to be 9.7%, ranging from 4 to 17%. High- and low-producing cell lines, VV05 and VV06, with 1.8-fold differences in their basal anthocyanin contents, exhibited different inducibilities to L-phenylalanine feeding, methyl jasmonate and light irradiation. The low-producing cell line, showed greater potential in enhanced the anthocyanin production.

Uma análise de cenários à luz das tendências atuais: sua importância para gestão do capital humano nas organizações.

Este trabalho analisa os cenários previstos por Peter Drucker (1985) e Alvin Toffler (1990) sob a perspectiva da obra de Thomas Friedman (2005). Friedman, em sua obra ?O mundo é plano?, descreve os fatores determinantes do cenário econômico e tecnológico do século XXI. A análise efetuada neste trabalho evidencia que os paradigmas da sociedade da informação na qual vivemos, que são descritos por Friedman (2005), comprovam grande parte das predições efetuadas há mais de uma década por Drucker e Toffler. Estes autores contribuiram para a construção do ?presente? de diversas organizações uma vez que seus pensamentos embasaram o planejamento estratégico de várias empresas e instituições. Este trabalho visa ressaltar que projeções de futuro criteriosas, sustentadas por fatos e tendências, são importantes ferramentas para o desenvolvimento de ações empresariais, dentre elas, o estabelecimento de estratégias de gestão de pessoas.

From Limen to Lumen: computing students in liminal spaces.From Limen to Lumen: computing students in liminal spaces

Eckerdal, A., McCartney, R., Mostr?m, J. E., Sanders, K., Thomas, L., and Zander, C. 2007. From Limen to Lumen: computing students in liminal spaces. In Proceedings of the Third international Workshop on Computing Education Research (Atlanta, Georgia, USA, September 15 - 16, 2007). ICER '07. ACM, New York, NY, 123-132.

Can theories of social capital explain dissenting patterns of engagement in the new Europe?

Korosteleva-Polglase, Elena, 'Can theories of social capital explain dissenting patterns of engagement in the new Europe?', Contemporary Politics, (2006) 12(2) pp.175-191 RAE2008

Re-conceptualising rural resources as countryside capital: The case of rural tourism

Brian Garrod, Roz Wornell and Ray Youell (2006). Re-conceptualising rural resources as countryside capital: The case of rural tourism. Journal of Rural Studies, 22 (1), 117-128. RAE2008

Salicylate accumulation inhibits growth at chilling temperature in Arabidopsis

Ian M. Scott, Shannon M. Clarke, Jacqueline E. Wood and Luis A.J. Mur (2004). Salicylate accumulation inhibits growth at chilling temperature in Arabidopsis. Plant Physiology, 135(2), 1040-1049. RAE2008

Proposta metodológica para análise da qualidade urbanística de Frentes de Água

Dissertação apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Arquitetura e Urbanismo

Lights and shades of mission work, or, Leaves from a worker's note book : being reminiscenses of seven years service at the capital, 1885-1892 / by B.H.W.

http://www.archive.org/details/lightsandshades00bhwuoft

Cortical Dynamics of Contextually-Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation

How do humans use predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, a certain combination of objects can define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. A neural model, ARTSCENE Search, is developed to illustrate the neural mechanisms of such memory-based contextual learning and guidance, and to explain challenging behavioral data on positive/negative, spatial/object, and local/distant global cueing effects during visual search. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined by enhancing target-like objects in space as a scene is scanned with saccadic eye movements. The model clarifies the functional roles of neuroanatomical, neurophysiological, and neuroimaging data in visual search for a desired goal object. In particular, the model simulates the interactive dynamics of spatial and object contextual cueing in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortical cells (area 46) prime possible target locations in posterior parietal cortex based on goalmodulated percepts of spatial scene gist represented in parahippocampal cortex, whereas model ventral prefrontal cortical cells (area 47/12) prime possible target object representations in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex. The model hereby predicts how the cortical What and Where streams cooperate during scene perception, learning, and memory to accumulate evidence over time to drive efficient visual search of familiar scenes.

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.

3-D Object Recognition by the ART-EMAP Evidence Accumulation Network

ART-EMAP synthesizes adaptive resonance theory (AHT) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). The network extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage I introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Simulations of the four ART-EMAP stages demonstrate performance on a difficult 3-D object recognition problem.