Impact of spatial effects on income segregation indices

Residential segregation is an inherently spatial phenomenon as it measures the separation of different types of people within a region. Whether measured with an explicitly spatial index, or a classic aspatial index, a region’s underlying spatial properties could manifest themselves in the magnitude of measured segregation. In this paper we implement a Monte Carlo simulation approach to investigate the properties of four segregation indices in regions built with specific spatial properties. This approach allows us to control the experiment in ways that empirical data do not. In general we confirm the expected results for the indices under various spatial properties, but some unexpected results emerge. Both the Dissimilarity Index and Neighborhood Sorting Index are sensitive to region size, but their spatial counterparts, the Adjusted Dissimilarity Index and Generalized Neighborhood Sorting Index, are generally immune to this problem. The paper also lends weight to concerns about the downward pressure on measured segregation when multiple neighborhoods are grouped into a single census tract. Finally, we discuss concerns about the way space is incorporated into segregation indices since the expected value of the spatial indices tested is lower than their aspatial counterparts.     

Schema-based Web wrapping

An effective solution to automate information extraction from Web pages is represented by wrappers. A wrapper associates a Web page with an XML document that represents part of the information in that page in a machine-readable format. Most existing wrapping approaches have traditionally focused on how to generate extraction rules, while they have ignored potential benefits deriving from the use of the schema of the information being extracted in the wrapper evaluation. In this paper, we investigate how the schema of extracted information can be effectively used in both the design and evaluation of a Web wrapper. We define a clean declarative semantics for schema-based wrappers by introducing the notion of (preferred) extraction model, which is essential to compute a valid XML document containing the information extracted from a Web page. We developed the SCRAP (SChema-based wRAPper for web data) system for the proposed schema-based wrapping approach, which also provides visual support tools to the wrapper designer. Moreover, we present a wrapper generalization framework to profitably speed up the design of schema-based wrappers. Experimental evaluation has shown that SCRAP wrappers are not only able to successfully extract the required data, but also they are robust to changes that may occur in the source Web pages.     

Adaptive soft k-nearest-neighbour classifiers

A novel classifier is introduced to overcome the limitations of the k-NN classification systems. It estimates the posterior class probabilities using a local Parzen window estimation with the k-nearest-neighbour prototypes (in the Euclidean sense) to the pattern to classify. A learning algorithm is also presented to reduce the number of data points to store. Experimental results in two hand-written classification problems demonstrate the potential of the proposed classification system.     

Entanglement of formation and concurrence for mixed states

We review some results on analytical computations of the measures for quantum entanglement: entanglement of formation and concurrence. We introduce some estimations of the lower bounds for the entanglement of formation in bipartite mixed states, and of lower bounds for the concurrence in bipartite and tripartite systems. The results on lower bounds for the concurrence are also generalized to arbitrary multipartite systems.     

ASIP-based reconfigurable architectures for power-efficient and real-time image/video processing

To meet both flexibility and performance requirements, particularly when implementing high-end real-time image/video processing algorithms, the paper proposes to combine the application specific instruction-set processor (ASIP) paradigm with the reconfigurable hardware one. As case studies, the design of partially reconfigurable ASIP (r-ASIP) architectures is presented for two classes of algorithms with widespread diffusion in image/video processing: motion estimation and retinex filtering. Design optimizations are addressed at both algorithmic and architectural levels. Special processor concepts used to trade-off performance versus flexibility and to enable new features of post-fabrication configurability are shown. Silicon implementation results are compared to known ASIC, DSP or reconfigurable designs; the proposed r-ASIPs stand for their better performance–flexibility figures in the respective algorithmic class.

Software Architectures for Reducing Priority Inversion and Non-determinism in Real-time Object Request Brokers

There is increasing demand to extend Object RequestBroker (ORB) middleware to support distributed applications withstringent real-time requirements. However, conventional ORB implementations,such as CORBA ORBs, exhibit substantial priority inversion andnon-determinism, which makes them unsuitable for applicationswith deterministic real-time requirements. This paper providestwo contributions to the study and design of real-time ORB middleware.First, it illustrates empirically why conventional ORBs do notyet support real-time quality of service. Second, it evaluatesconnection and concurrency software architectures to identifystrategies that reduce priority inversion and non-determinismin real-time CORBA ORBs. The results presented in this paperdemonstrate the feasibility of using standard OO middleware likeCORBA to support certain types of real-time applications overthe Internet.     

Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing

This paper presents novel algorithmic and architectural solutions for real-time and power-efficient enhancement of images and video sequences. A programmable class of Retinex-like filters, based on the separation of the illumination and reflectance components, is proposed. The dynamic range of the input image is controlled by applying a suitable non-linear function to the illumination, while the details are enhanced by processing the reflectance. An innovative spatially recursive rational filter is used to estimate the illumination. Moreover, to improve the visual quality results of two-branch Retinex operators when applied to videos, a novel three-branch technique is proposed which exploits both spatial and temporal filtering. Real-time implementation is obtained by designing an Application Specific Instruction-set Processor (ASIP). Optimizations are addressed at algorithmic and architectural levels. The former involves arithmetic accuracy definition and linearization of non-linear operators; the latter includes customized instruction set, dedicated memory structure, adapted pipeline, bypasses, custom address generator, and special looping structures. The ASIP is synthesized in standard-cells CMOS technology and its performances are compared to known Digital signal processor (DSP) implementations of real-time Retinex filters. As a result of the comparison, the proposed algorithmic/architectural design outperforms state-of-art Retinex-like operators achieving the best trade-off between power consumption, flexibility, and visual quality.

Determination of the Fe content of embedded Cu-rich particles in ferritic alloys using energy-filtered TEM

A novel technique for the quantification of the iron content of copper precipitates in ferritic steels is presented. Energy-filtered (EF) imaging has been used to extract elemental maps with high spatial resolution. These maps contain enough information to attempt the quantification of the signal produced by the precipitates when either a line profile is measured across them or the whole image signal is integrated. Assumptions such as sphericity of the precipitates and composition variations are discussed. Special attention to the assessment of drift on the information extracted from EF images has been taken. Minimum detectability and optimum acquisition conditions are discussed.     

The Social Sustainable Aquifer Yield: An Indicator for the Analysis and Assessment of the Integrated Aquifers Management

Integrated Aquifers Management (IAM) demands innovative tools and methods that are able to consider as much perspectives as possible. This research is aimed to design, apply and provide an indicator named Social Sustainable Aquifer Yield (SSAY), expressed in units of time that includes pure hydrological variables as well as social ones. The indicator is defined as the relation between the Residence Time, which is the relation between aquifer Storage (S) and Recharge (R) (S/R), and the relation between the aquifer Pumping (P) and the new variable named Aquifer Social Yield (ASY). The whole indicator is defined by this formula: (S/R)/(P/ASY). The assessment of the residence time is essential in aquifers with at least one of the following features: i) high hydraulic diffusivity, and ii) small volume of reserves. Finally, the variable ASY is defined as the average perception from the stakeholders about the maximum acceptable aquifer exploitation. This indicator has been successfully applied in the aquifers located in southern Jaen province (South Spain) belonging to the Water System SE4-Jaén Water Supply. The results probe the high utility of the indicator, especially in regards to the public participation processes.     

GPGPU implementation of growing neural gas: Application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6×$6\times$ compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.