Weaving encounters: towards an art of participation

In the early 21st Century, with the phenomenon of digital convergence, the consecration of Web 2.0, the decrease of the cost of cameras and video recorders, the proliferation of mobile phones, laptops and wireless technologies, we witness the arising of a new wave of media, of an informal, personal and at times “minority” nature, facilitating social networks, a culture of fans, of sharing and remix. As digital networks become fully and deeply intricate in our experience, the idea of “participation” arises as one of the most complex and controversial themes of the contemporary critical discourse, namely in what concerns contemporary art and new media art. However, the idea of “participation” as a practice or postulate traverses the 20th century art playing an essential role in its auto-critic, in questioning the concept of author, and in the dilution of the frontiers between art, “life” and society, emphasizing the process, the everyday and a community sense. As such, questioning the new media art in light of a “participatory art” (Frieling, 2008) invokes a double gaze simultaneously attentive to the emerging figures of a “participatory aesthetics” in digital arts and of the genealogy in which it is included. In fact, relating the new media art with the complex and paradoxical phenomenon of “participation” allows us to, on the one hand, avoid “digital formalism” (Lovink, 2008) and analyse the relations between digital art and contemporary social movements; on the other hand, this angle of analysis contributes to reinforce the dialogue and the links between digital art and contemporary art, questioning the alleged frontiers that separate them.

Trends Of CPU, GPU and FPGA for high-performance computing

Floating-point computing with more than one TFLOP of peak performance is already a reality in recent Field-Programmable Gate Arrays (FPGA). General-Purpose Graphics Processing Units (GPGPU) and recent many-core CPUs have also taken advantage of the recent technological innovations in integrated circuit (IC) design and had also dramatically improved their peak performances. In this paper, we compare the trends of these computing architectures for high-performance computing and survey these platforms in the execution of algorithms belonging to different scientific application domains. Trends in peak performance, power consumption and sustained performances, for particular applications, show that FPGAs are increasing the gap to GPUs and many-core CPUs moving them away from high-performance computing with intensive floating-point calculations. FPGAs become competitive for custom floating-point or fixed-point representations, for smaller input sizes of certain algorithms, for combinational logic problems and parallel map-reduce problems. © 2014 Technical University of Munich (TUM).

A director’s approach to art and religion : o quadro, um musical da Paixão segundo o sr. João (The masterpiece, a musical of The passion according to John): trabalho de projecto

Trabalho de Projecto submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro - especialização em Encenação.

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.