Abstract
Due to the large number of optimizations provided in modern compilers and to compiler optimization specific opportunities, a Design Space Exploration (DSE) is necessary to search for the best sequence of compiler optimizations for a given code fragment (e. g., function). As this exploration is a complex and time consuming task, in this paper we present DSE strategies to select optimization sequences to both improve the performance of each function and reduce the exploration time. The DSE is based on a clustering approach which groups functions with similarities and then explore the reduced search space provided by the optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method which is applied to a symbolic code representation of the source code. The DSE process uses the reduced set identified by clustering in two ways: as the design space or as the initial configuration. In both ways, the adoption of a pre-selection based on clustering allows the use of simple and fast DSE algorithms. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of compiler optimization sequences considering 49 compilation passes and targeting a Xilinx MicroBlaze processor, and were performed aiming performance improvements for 41 functions. Experimental results reveal that the use of our new clustering-based DSE approach achieved a significant reduction on the total exploration time of the search space (18 x over a Genetic Algorithm approach for DSE) at the same time that important performance speedups (43% over the baseline) were obtained by the optimized codes.

Abstract
This paper analyzes the impact of climate dynamics on vegetation growth for a rural mountainous region in northeastern Portugal. As a measure of vegetation growth, we use the normalized difference vegetation index (NDVI), which is based on the ten-day synthesis data set (S10) from Satellite Pour l'Observation de la Terre (SPOT-VEGETATION) imagery from 1998 to 2011. We test whether the dynamic growth pattern of the NDVI has changed due to climate variability, and we test the relationship of NDVI with temperature and available soil water (ASW). In order to do so, we use a time-frequency approach based on Kalman filter regressions in the time domain. The advantage of our approach is that it can be used even in the case where the sample size is relatively small. By estimating the important relationships in the time domain first and transferring them into the frequency domain, we are still able to derive a complete spectrum over all frequencies. In our example, we find a change of the cyclical pattern for the spring season and different changes if we take into account all seasons. In other words, we can distinguish between deterministic changes of the vegetation cycles and stochastic changes that only occur randomly. Deterministic changes imply that the data-generating process has changed (such as climate), whereas stochastic changes imply only temporary changes. We find that individual seasons undergo cyclical changes that are different from other seasons. Moreover, our analysis shows that temperature and ASW are the main drivers of vegetation growth. We can also recognize a shift of the relative importance away from temperature to soil water.

Abstract
The GRAVITY Acquisition Camera was designed to monitor and evaluate the optical beam properties of the four ESO/VLT telescopes simultaneously. The data is used as part of the GRAVITY beam stabilization strategy. Internally the Acquisition Camera has four channels each with: several relay mirrors, imaging lens, H-band filter, a single custom made silica bulk optics (i.e. Beam Analyzer) and an IR detector (HAWAII2-RG). The camera operates in vacuum with operational temperature of: 240k for the folding optics and enclosure, 100K for the Beam Analyzer optics and 80K for the detector. The beam analysis is carried out by the Beam Analyzer, which is a compact assembly of fused silica prisms and lenses that are glued together into a single optical block. The beam analyzer handles the four telescope beams and splits the light from the field mode into the pupil imager, the aberration sensor and the pupil tracker modes. The complex optical alignment and focusing was carried out first at room temperature with visible light, using an optical theodolite/alignment telescope, cross hairs, beam splitter mirrors and optical path compensator. The alignment was validated at cryogenic temperatures. High Strehl ratios were achieved at the first cooldown. In the paper we present the Acquisition Camera as manufactured, focusing key sub-systems and key technical challenges, the room temperature (with visible light) alignment and first IR images acquired in cryogenic operation.

Abstract
Hard real- time multiprocessor scheduling has seen, in recent years, the flourishing of semi-partitioned scheduling algorithms. This category of scheduling schemes combines elements of partitioned and global scheduling for the purposes of achieving efficient utilization of the system's processing resources with strong schedulability guarantees and with low dispatching overheads. The sub-class of slot-based "task-splitting" scheduling algorithms, in particular, offers very good trade-offs between schedulability guarantees (in the form of high utilization bounds) and the number of preemptions/migrations involved. However, so far there did not exist unified scheduling theory for such algorithms; each one was formulated in its own accompanying analysis. This article changes this fragmented landscape by formulating a more unified schedulability theory covering the two state-of-the-art slot-based semi-partitioned algorithms, S-EKG and NPS-F (both fixed job-priority based). This new theory is based on exact schedulability tests, thus also overcoming many sources of pessimism in existing analysis. In turn, since schedulability testing guides the task assignment under the schemes in consideration, we also formulate an improved task assignment procedure. As the other main contribution of this article, and as a response to the fact that many unrealistic assumptions, present in the original theory, tend to undermine the theoretical potential of such scheduling schemes, we identified and modelled into the new analysis all overheads incurred by the algorithms in consideration. The outcome is a new overhead-aware schedulability analysis that permits increased efficiency and reliability. The merits of this new theory are evaluated by an extensive set of experiments.

Abstract
This paper presents an approach accounting for the classification of the main knowledge resources related to the new Science of Service. The main knowledge categories are defined as concepts integrated in an extended Service Science ontology. The ontology derived from several sources was captured using UML and Protege, and then, through a RDF/OWL transformation, a semantically annotated wiki has been directly implemented offering an execution of the ontology together with implemented use cases. Further, a dedicated application was developed - the Service Science Knowledge Environment (SSKE) in order to grant user access to different knowledge categories created along with the proposed ontology. The SSKE is a cloud based collaborative software service, aiming at providing co-created knowledge resources shared by academia, industry and government organizations. This application can be accessed through the Web (http://sske.cloud.upb.ro/) and it can be used for managing service related knowledge.

Abstract