Sorting out single-crossing preferences on networks

This paper studies coalitional strategy-proofness of social choice correspondences that map preference profiles into sets of alternatives. In particular, we focus on the Pareto rule, which associates the set of Pareto optimal alternatives with each preference profile, and examine whether or not there is a necessary connection between coalitional strategy-proofness and Pareto optimality. The definition of coalitional strategy-proofness is given on the basis of a max–min criterion. We show that the Pareto rule is coalitionally strategy-proof in this sense. Moreover, we prove that given an arbitrary social choice correspondence satisfying the coalitional strategy-proofness and nonimposition, all alternatives selected by the correspondence are Pareto optimal. These two results imply that the Pareto rule is the maximal correspondence in the class of coalitionally strategy-proof and nonimposed social choice correspondences.

This article considers social welfare functions for which the set of alternatives is the two-dimensional nonnegative orthant and for which individuals can have any strictly monotonic, linear preference ordering. A Paretian generalized median social welfare function is defined by augmenting the n individuals in society with n — 1 phantom individuals each of whom has a fixed weakly monotonic, linear ordering and, for each profile of preferences, choosing the social preference to be the linear preference with slope equal to the median of all 2n — 1 individual slopes. Bossert and Weymark (1992) showed that the class of Paretian generalized median social welfare functions is characterized by anonymity, weak Pareto, and binary independence of irrelevant alternatives if the social preferences are restricted to be strictly monotonic, linear orderings. In this article we show that the monotonicity and linearity assumptions on social preferences can be weakened to continuity if we add an intra-profile neutrality condition.

Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

If is a set of social alternatives, a social choice rule (SCR) assigns a subset of A to each potential profile of individuals' preferences over A, where the subset is interpreted as the set of 'welfare optima.' A game form (or 'mechanism') implements the social choice rule if, for any potential profile of preferences, (1) any welfare optimum can arise as a Nash equilibrium of the game form (implying, in particular, that a Nash equilibrium exists) and, (2) all Nash equilibria are welfare optimal. The main result of this paper establishes that any SCR that satisfies two properties - monotonicity and no veto power--can be implemented by a game form if there are three or more individuals. The proof is constructive.

In this chapter1 we report on a real world decision aiding process which took place in a large Italian firm, in late 1996 and early 1997, concerning the evaluation of offers following a call for tenders for a very important software acquisition. We will try to extensively present the decision process for which the decision support was requested, the actors involved, the decision aiding process, including the problem structuring and formulation, the evaluation model created and the multiple criteria method adopted. The reader should be aware of the fact that very few real world cases of decision support are reported in literature although much more occur in reality (for noteworthy exceptions see Belton et al. 1997, Bana e Costa et al. 1999, Vincke 1992b, Roy and Bouyssou 1993).KeywordsDecision SupportQuality AttributeOrdinal ScaleParent NodeDigital Terrain ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in ‘point of care’ diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases.

This chapter summarizes the numerical techniques which complete the mathematical model and allow for the application in real cases. Both-spatial and temporal discretizations are presented in a general way, together with possible solution algorithms for the ensuing equations. The properties of the numerical solutions are then analyzed and used to discuss the possible choices of the field variables.

Computer vision systems have become a key element of modern companies. However, its implantation not only requires specialized hardware components but also software to control the correct performance of all these components. The purpose of this article is to present the design and deployment of a software created to manage the different computer vision systems of any company. First, using our experience in mechanization and industrialization of company processes, a general company specification will be proposed as well as the main software requirements that have to be satisfied. Then, a three-level modular design composed of the core, a configuration module, and user interfaces with functionalities capable of satisfying the defined requirements will be presented. Special attention will be given to time restrictions and components’ synchronization. In addition, the different tests that have been carried out to control the correct performance of the software will be shown. The development process will end with a generic, modular, and scalable software able to fit different industrial scenarios by simply modifying a set of input parameters. To illustrate the correct performance of the proposal, the details of its installation in four real companies with different needs will be presented. The proposed work has a practical use in industry and it also provides a thorough description of the main components involved in computer vision systems of real company environments and how to manage them. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The aim of the article is to provide a software solution to manage the computer vision systems of a company. The proposed software is simple enough to be controlled by company operators with no need for great expertise on the technical aspects, but rather on the industrial workflow to be controlled. To develop such a software, it has been necessary to know about all the elements involved in the industrial scenarios controlled by computer vision systems as well as their relationships. This article presents all these elements providing a global and complete view of the problem with special attention to synchronization, which is fundamental to satisfy industrial time restriction requirements. The software is presented from a technical point of view, but also from a practical one, since different examples of its application in real cases are given. These examples illustrate the adaptability of the proposal to different scenarios and also exemplify how the different elements are modified to fit the different situations. In addition, this article provides a comprehensive overview of real industrial situations, focusing on the most important issues faced during real installations.

This paper shows that a regression tree problem can be turned into a classification tree problem reducing the computational cost and providing useful interpretation issues. A TWO-CLASS tree methodoloy for non-parametric regression analysis is introduced. Data are as follows: a numerical response variable and a set of predictors (of categorical and/or numerical type) are measured on a sample of objects, with no probability assumption. Thus a non-parametric approach is proposed. The concepts of prospective and retrospective splits are considered. Main idea is to grow a binary partition of the sample of objects such that, at each node of the tree structure, the numerical response is recoded into a dummy or two-class variable (called theoretical response) on the basis of the optimal partition of the objects into two groups within the set of retrospective splits. A two-stage splitting criterion with a fast algorithm is applied: the best split of the objects is found in the set of candidate (prospective) splits of each predictor modalities by maximizing the predictability of the two-class response. Some applications on real world cases and a simulation study allow to demonstrate that the two-class splitting procedure is computationally less intensive than standard regression tree such as CART. Furthermore, the final partitions obtained by the two-class procedure and the standard one are very similar to each other, in terms of percentage of objects belonging together to the same terminal node. Some aids to the interpretation allow to describe the response variable distribution in the terminal nodes.

Neighboring springs draining fractured-rock aquifers can display large differences in water quality and flow regime, depending on local variations of the connectivity and the aperture size distribution of the fracture network. Consequently, because homogeneous equivalent parameters cannot be assumed a priori for the entire regional aquifer, the vulnerability to pollution of such springs has to be studied on a case by case basis. In this paper, a simple lumped-parameter model usually applied to estimate the mean transit time of water (or tracer) is presented. The original exponential piston-flow model was modified to take land-use distribution into account and applied to predict the evolution of atrazine concentration in a series of springs draining a fractured sandstone aquifer in Luxembourg, where despite a nationwide ban in 2005, atrazine concentrations still had not begun to decrease in 2009. This persistence could be explained by exponentially distributed residence times in the aquifer, demonstrating that in some real world cases, models based on the groundwater residence time distribution can be a powerful tool for trend reversal assessments as recommended for instance by current European Union guidelines.

We introduce ProFound, a source finding and image analysis package. ProFound provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux, size and ellipticity. These inputs are key requirements of ProFit, our recently released galaxy profiling package, where the design aim is that these two software packages will be used in unison to semi-automatically profile large samples of galaxies. The key novel feature introduced in ProFound is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than using more traditional circular or ellipse based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. We apply ProFound in a number of simulated and real world cases, and demonstrate that it behaves reasonably given its stated design goals. In particular, it offers good initial parameter estimation for ProFit, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. A number of bulge-disc decomposition projects are already making use of the ProFound and ProFit pipeline, and adoption is being encouraged by publicly releasing the software for the open source R data analysis platform under an LGPL-3 license on GitHub (github.com/asgr/ProFound).

Automated network triage

Determining the initiation characteristics of the first element in an energetic train is relatively straight forward. It is possible to vary the input stimulus (voltage on a capacitor, current through a bridge wire, laser pulse energy, force of impact, etc.) in a controlled manner and conduct a sensitivity test and analysis to establish the mean and standard deviation of the threshold stimulus. The characteristics that allow relatively straight forward analysis are the ability to vary this input stimulus in a simple and well-controlled manner. It is most often possible to use test equipment that is identical or close to the equipment that will be used in the tactical configuration. Determining the initiation reliability of explosive transfer from one element to the next is not as easy. It is difficult to vary the input stimulus to one element, since this act requires that the output from the previous element be modified or attenuated, such as by increased gaps or barriers. In most real world cases, explosive transfer is a complex three dimensional process. For example, increasing the distance between donor and acceptor can result in the fragments from the donor missing the acceptor. Downloading the donor energetic material can result in a different fracture and flight of the donor’s flying plate. This paper will report on an alternate method that uses first principles to determine the reliability of energetic transfer between a detonator and a detonation acceptor.

GIS is used today to better understand and solve urban problems. This book explores and illustrates the use of geo-information in the planning and management of urban regions. The first part of the book addresses the concept of sustainable urban development, its different frameworks, the many ways of measuring sustainability, and its value in the urban policy arena. The second part discusses how urban planning can shape our cities, examines various spatial configurations of cities, the spread of activities, and the demands placed on different functions to achieve strategic objective. It further focuses on the recognition that urban dwellers are increasingly under threat from natural hazards and climate change.

Safety and Security in Multiagent Systems: Report on the 2nd SASEMAS workshop (SASEMAS'05)