EU accession and Poland's external trade policy

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Unsupervised Learning of Individuals and Categories from Images

Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data

Mental Imagery and Chunks: Empirical and Computational Findings

To investigate experts’ imagery in chess, players were required to recall briefly-presented positions in which the pieces were placed on the intersections between squares (intersection positions). Position types ranged from game positions to positions where both the piece distribution and location were randomized. Simulations were run with the CHREST model (Gobet & Simon, 2000). The simulations assumed that pieces had to be centered back one by one to the middle of the squares in the mind’s eye before chunks could be recognized. Consistent with CHREST’s predictions, chess players (N = 36), ranging from weak amateurs to grandmasters, exhibited much poorer recall on intersection positions than on standard positions (pieces placed on centers of squares). On the intersection positions, the skill difference in recall was larger on game positions than on the randomized positions. Participants recalled bishops better than knights, suggesting that Stroop-like interference impairs recall of the latter. The data supported both the time parameter in CHREST for shifting pieces in the mind’s eye (125 ms per piece) and the seriality assumption. In general, the study reinforces the plausibility of CHREST as a model of cognition

Solving constraints within a graph based dependency model by digitising a new process of incrementally casting concrete structures

The mechanisation of incrementally casting concrete structures can reduce the economic and environmental cost of the formwork which produces them. Low-tech versions of these forms have been designed to produce structures with cross-sectional continuity, but the design and implementation of complex adaptable formworks remains untenable for smaller projects. Addressing these feasibility issues by digitally modelling these systems is problematic because constraint solvers are the obvious method of modelling the adaptable formwork, but cannot acknowledge the hierarchical relationships created by assembling multiple instances of the system. This thesis hypothesises that these opposing relationships may not be completely disparate and that simple dependency relationships can be used to solve constraints if the real procedure of constructing the system is replicated digitally. The behaviour of the digital model was correlated with the behaviour of physical prototypes of the system which were refined based on digital explorations of its possibilities. The generated output is assessed physically on the basis of its efficiency and ease of assembly and digitally on the basis that permutations can be simply described and potentially built in reality. One of the columns generated by the thesis will be cast by the redesigned system in Lyon at the first F2F (file to factory) continuum workshop

CRISPR/Cas9 mediated knockout of rb1 and rbl1 leads to rapid and penetrant retinoblastoma development in Xenopus tropicalis

Retinoblastoma is a pediatric eye tumor in which bi-allelic inactivation of the Retinoblastoma 1 (RB1) gene is the initiating genetic lesion. Although recently curative rates of retinoblastoma have increased, there are at this time no molecular targeted therapies available. This is, in part, due to the lack of highly penetrant and rapid retinoblastoma animal models that facilitate rapid identification of targets that allow therapeutic intervention. Different mouse models are available, all based on genetic deactivation of both Rb1 and Retinoblastoma-like 1 (Rbl1), and each showing different kinetics of retinoblastoma development. Here, we show by CRISPR/Cas9 techniques that similar to the mouse, neither rb1 nor rbl1 single mosaic mutant Xenopus tropicalis develop tumors, whereas rb1/rbl1 double mosaic mutant tadpoles rapidly develop retinoblastoma. Moreover, occasionally presence of pinealoblastoma (trilateral retinoblastoma) was detected. We thus present the first CRISPR/Cas9 mediated cancer model in Xenopus tropicalis and the first genuine genetic non-mammalian retinoblastoma model. The rapid kinetics of our model paves the way for use as a pre-clinical model. Additionally, this retinoblastoma model provides unique possibilities for fast elucidation of novel drug targets by triple multiplex CRISPR/Cas9 gRNA injections (rb1 + rbl1 + modifier gene) in order to address the clinically unmet need of targeted retinoblastoma therapy