Derek Jarman’s allegories of spectacle: inter-artistic embodiment

ABSTRACT - Derek Jarman was a multifaceted artist whose intermedial versatility reinforces a strong authorial discourse. He constructs an immersive allegorical world of hybrid art where different layers of cinematic, theatrical and painterly materials come together to convey a lyrical form and express a powerful ideological message. In Caravaggio (1986) and Edward II (1991), Jarman approaches two european historical figures from two different but concomitant perspectives. In Caravaggio, through the use of tableaux of abstract meaning and by focusing on the detailing of the models’ poses, Jarman re-enacts the allegorical spirit of Caravaggio’s paintings through entirely cinematic resources. Edward II was a king, and as a statesman he possessed a certain dose of showmanship. In this film Jarman reconstructs the theatrical basis of Christopher Marlowe’s Elizabethan play bringing it up to date in a successfully abstract approach to the musical stage. In this article, I intend to conjoin the practice of allegory in film with certain notions of existential phenomenology as advocated by Vivian Sobchack and Laura U. Marks, in order to address the relationship between the corporeality of the film and the lived bodies of the spectators. In this context, the allegory is a means to convey intradiegetically the sense-ability at play in the cinematic experience, reinforcing the textural and sensual nature of both film and viewer, which, in turn, is also materially enhanced in the film proper, touching the spectator in a supplementary fashion. The two corporealities favour an inter-artistic immersion achieved through coenaesthesia.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Parallel sparse unmixing of hyperspectral data

In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. This method is based on the spectral unmixing by splitting and augmented Lagrangian (SUNSAL) that estimates the material's abundance fractions. The parallel method is performed in a pixel-by-pixel fashion and its implementation properly exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for simulated and real hyperspectral datasets reveal significant speedup factors, up to 1 64 times, with regards to optimized serial implementation.