Random 3-SAT: The Plot Thickens

This paper presents an experimental investigation of the following questions: how does the average-case complexity of random 3-SAT, understood as a function of the order (number of variables) for fixed density (ratio of number of clauses to order) instances, depend on the density? Is there a phase transition in which the complexity shifts from polynomial to exponential in the order? Is the transition dependent or independent of the solver? Our experiment design uses three complete SAT solvers embodying different algorithms: GRASP, CPLEX, and CUDD. We observe new phase transitions for all three solvers, where the median running time shifts from polynomial in the order to exponential. The location of the phase transition appears to be solver-dependent. GRASP shifts from polynomial to exponential complexity near the density of 3.8, CPLEX shifts near density 3, while CUDD exhibits this transition between densities of 0.1 and 0.5. This experimental result underscores the dependence between the solver and the complexity phase transition, and challenges the widely held belief that random 3-SAT exhibits a phase transition in computational complexity very close to the crossover point.     

Matisse: A System-on-Chip Design Methodology Emphasizing Dynamic Memory Management

MATISSE is a design environment intended for developing systems characterized by a tight interaction between control and data-flow behavior, intensive data storage and transfer, and stringent real-time requirements. Matisse bridges the gap from a system specification, using a concurrent object-oriented language, to an optimized embedded single-chip hardware/software implementation. Matisse supports stepwise exploration and refinement of dynamic memory management, memory architecture exploration, and gradual incorporation of timing constraints before going to traditional tools for hardware synthesis, software compilation, and inter-processor communication synthesis. With this approach, specifications of embedded systems can be written in a high-level programming language using data abstraction. Application of MATISSE on telecom protocol processing systems in the ATM area shows significant improvements in area usage and power consumption.     

Turn-on jitter of external-cavity semiconductor lasers

Analytical expressions, validated by numerical simulations, are obtained for the turn-on delay jitter of semiconductor lasers subjected to weak optical feedback in short external cavities. The results show explicitly that displacement of the external reflector on optical wavelength scales causes significant changes in the switch-on dynamics of the laser. It is found that more than a 400% increase of jitter can occur under certain circumstances. The demonstrated sensitivity of laser switch-on dynamics to reflector location is considered to be particularly relevant to the performance of packaged laser diodes     

Comparison of Biological Features of Wild European Rabbit Mesenchymal Stem Cells Derived from Different Tissues

Although the European rabbit is an “endangered” species and a notorious biological model, the analysis and comparative characterization of new tissue sources of rabbit mesenchymal stem cells (rMSCs) have not been well addressed. Here, we report for the first time the isolation and characterization of rMSCs derived from an animal belonging to a natural rabbit population within the native region of the species. New rMSC lines were isolated from different tissues: oral mucosa (rOM-MSC), dermal skin (rDS-MSC), subcutaneous adipose tissue (rSCA-MSC), ovarian adipose tissue (rOA-MSC), oviduct (rO-MSC), and mammary gland (rMG-MSC). The six rMSC lines showed plastic adhesion with fibroblast-like morphology and were all shown to be positive for CD44 and CD29 expression (characteristic markers of MSCs), and negative for CD34 or CD45 expression. In terms of pluripotency features, all rMSC lines expressed NANOG, OCT4, and SOX2. Furthermore, all rMSC lines cultured under osteogenic, chondrogenic, and adipogenic conditions showed differentiation capacity. In conclusion, this study describes the isolation and characterization of new rabbit cell lines from different tissue origins, with a clear mesenchymal pattern. We show that rMSC do not exhibit differences in terms of morphological features, expression of the cell surface, and intracellular markers of pluripotency and in vitro differentiation capacities, attributable to their tissue of origin.     

Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction

In this work, we carry out the parallelization of the single level Fast Multipole Method (FMM) for solving acoustic-scattering problems (using the Helmholtz equation) on distributed-memory GPGPU systems. With the aim of enlarging the scope of feasible simulations, the presented solution combines the techniques developed for our distributed-memory CPU solver with our shared-memory GPGPU solver. The performance of the developed solution is proved using two different GPGPU clusters: the first one consists of two workstations with NVIDIA GTX 480 GPUs linked by a Gigabit Ethernet network, and the second one comprises four nodes with NVIDIA Tesla M2090 GPUs linked by an Infiniband network.     

Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.     

A multidimensional data model using the fuzzy model based on the semantic translation

With the rapid development of Web 2.0 sites such as Blogs and Wikis users are encouraged to express opinions about certain products, services or social topics over the web. There is a method for aggregating these opinions, called Opinion Aggregation, which is made up of four steps: Collect, Identify, Classify and Aggregate. In this paper, we present a new conceptual multidimensional data model based on the Fuzzy Model based on the Semantic Translation to solve the Aggregate step of an Opinion Aggregation architecture, which allows exploiting the measure values resulting from integrating heterogeneous information (including unstructured data such as free texts) by means of traditional Business Intelligence tools. We also present an entire Opinion Aggregation architecture that includes the Aggregate step and solves the rest of steps (Collect, Identify and Classify) by means an Extraction, Transformation and Loading process. This architecture has been implemented in an Oracle Relational Database Management System. We have applied it to integrate heterogeneous data extracted from certain high end hotels websites, and we show a case study using the collected data during several years in the websites of high end hotels located in Granada (Spain). With this integrated information, the Data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability and a high precision by means of interactive tools such as the dashboards.     

Where does model-driven engineering help? Experiences from three industrial cases

There have been few experience reports from industry on how Model-Driven Engineering (MDE) is applied and what the benefits are. This paper summarizes the experiences of three large industrial participants in a European research project with the objective of developing techniques and tools for applying MDE on the development of large and complex software systems. The participants had varying degrees of previous experience with MDE. They found MDE to be particularly useful for providing abstractions of complex systems at multiple levels or from different viewpoints, for the development of domain-specific models that facilitate communication with non-technical experts, for the purposes of simulation and testing, and for the consumption of models for analysis, such as performance-related decision support and system design improvements. From the industrial perspective, a methodology is considered to be useful and cost-efficient if it is possible to reuse solutions in multiple projects or products. However, developing reusable solutions required extra effort and sometimes had a negative impact on the performance of tools. While the companies identified several benefits of MDE, merging different tools with one another in a seamless development environment required several transformations, which increased the required implementation effort and complexity. Additionally, user-friendliness of tools and the provision of features for managing models of complex systems were identified as crucial for a wider industrial adoption of MDE.