Dimension. Dimension hereby covenants that Dimension shall not, alone or in collaboration with a Third Party, (a) during the Research Term conduct clinical development of, and (b) during the term of this Agreement Commercialize, […***…], other than the Compounds/Vectors, GT Products and Licensed GT Products in accordance with the provisions of this Agreement.
Dimension. Decision-making regarding migration (autonomous/family/other); Migration history.
Dimension. Family and context affective-emotional. Family and affective-emotional (present) context: home, partner, family, children… Specific Inherent Needs.
Dimension. Finally, assuming that all computational-intensive kernels are executed on GPU, it is beneficial to reshape all relevant arrays in this fashion, since the potential overheads of the CPU- executed code are in this case negligible. We conclude by observing that this technique (i) transparently solves any uncoalesced accesses introduced by other compiler op- timizations such as tiling, and (ii) yields speed-ups as high as 28×.
4. Experimental Results Experimental Setup. We study the impact of our optimizations on two heterogenous commodity systems: a desktop5 and an integrated mobile6 solution. We compile (i) the sequential-CPU kernel with the gcc compiler versions 4.6.1 and 4.4.3, respectively, with compiler option -O3, and (ii) a very similar version of the CPU code with NVIDIA’s nvcc compiler for OpenCL version 4.2 and 4.1, respectively, with default compiler options. Reported speed- ups were averaged among 20 independent runs. × × × We estimate the three contracts described in Section 2.1: (i) an European option, named Simple, (ii) a discrete barrier option, named Medium, and (iii) a daily-monitored barrier option, named Complex. These contracts are written in terms of a number of underlyings, u, and dates, d: 1 1, 3 5 and 3 367, respectively. This amounts to very different runtime behavior, since u and d dictate (i) the amount of data processed per iteration and (ii) the weight each basic-block kernel has in the overall computation. In addition, we estimate the contracts with both single precision (SimpleF) and double precision (SimpleD) floating points. From a compute perspective this accentuates the different runtime be- havior, as double are more expensive than float operations (and require twice the space). From a financial perspective we note that the results of our parallel versions are equal to the sequential one, with precision higher than 0.001%. This is a consequence of the Sobol quasi-random generator being modeled as described in Sec- tion 2.2, where the parallel implementation preserves the modulo associativity semantics exhibited by the sequential version. Figures 9, 10 and 11 show the speed-up measurements for the described contracts under different optimization conditions. Read- ings for the gaming system are reported as vertical labels over plain area bars, while readings for the mobile solution are reported as horizontal, white labels over crossed regions. All histograms present error bars indicating the standard deviation of the measu...
Dimension. Recent advances in seismic surveying have made it possible to study reservoir developments in a new dimension – time. By shooting seismic every other year on Gullfaks, and cre- ating models which allow the experts to see and interpret the reservoir over many years, new pockets of remaining oil are constantly being discovered and added to the value of available reserves. It was spare capacity for production and injection on Gullfaks which indirectly prompted efforts to see whether the recovery factor could be improved. If the licensees had sold that capacity, they would not have had the same opportunities to handle a possible increase in their own production. “Through discussion in the licence, we resolved to make a commitment here,” says Xxxxx Xxxxxx, Xxxxxxx’s head of resource management on Gullfaks. “We’re naturally grateful for that today.” Neither she nor senior adviser Xxxxxx Xxxxxxx in Petoro would exclude the possibility that the field’s producing life could be extended even further through various improved recovery measures.
Dimension reduction in Classification of EEG data by standard deterministic methods (Inria Leading the Task, M10 – M22) / ITT
1.1 Literature review and tests with traditional methods 4.
1.2 Classify the data using the features proposed in WP3 4.1.3 Apply the classifiers on the data acquired in WP2 Task 4.2 Optimisation of the tools of task1 using GA (Inria Leading the Task, M15 – M30) / ITT UNEX
2.1 Build a GA to optimize the parameters of the deterministic methods used in task 1 4.2.2 Perform parallel coding of this GA 4.2.3 Comparison with deterministic approaches Task 4.3 Build/use new classification methods using GP (WP1) (INESC-ID Leading the Task, M22 – M36) / ITT, UNEX, Inria Task Objectives Apply GP classification methods developed in WP1 combined with the features obtained in WP3. 4.3.1 Build a GP dedicated to this problem 4.3.2 Develop a Parallel implementation of this GP 4.3.3 Performance evaluation of each problem D 4.1 Classification of EEG signals with chaos-based features M24 Inria
Dimension. It allows valid combinations of dimensions. • Formula. It supports complex expressions based on XPath, which can be applied to instance documents to validate your information.
Dimension. The strand diameter is measured before and after Cr-coating by means of a calibrated dual-axis laser micrometer. The ovality of the strands is defined as the difference between the diameters measured for the two axes.
Dimension. In laying out the centre line dimensions mentioned in the drawings or deduced there from and or as directed by the Engineer-in-charge, shall be strictly followed.
Dimension. 3 Dimension 4
