Optimizing Multiple Simultaneous Objectives for Voting and Facility Location

The distributed representation of random and meaningful object pairs in human occipitotemporal cortex: The weighted average as a general rule

In this paper, we utilize a multi-objective genetic algorithm (GA) to investigate the Iterated Prisoner's Dilemma problem with a population of players that don't have uniform objectives. Each of the members of our population has one of four objective pairs. We simulate a tournament similar to those in previous work to investigate patterns of convergence in objective pairs when they are free to change. We also consider the most successful objective pair within a population when members' objective pairs are fixed.

I-Scal: Multidimensional scaling of interval dissimilarities

The pickup and delivery problem (PDP) considers a set of transportation requests, which specify the quantity of product that has to be picked up from an origin and delivered to a destination. There exist a number of vehicles available to be used for completing these tasks. PDP consists of finding a collection of routes with minimum cost, such that all transportation request are serviced. Traditionally, cost has been associated with the number of routes and the total travel distance. However, in many applications, some other objectives emerge, for example, the minimization of travel time and the maximization of the collected profit. If we consider all these four objectives equally important, PDP can be tackled as a many-objective problem. In this paper we are interested in analyzing this many-objective problem in order to study some of its properties, specifically, (i) the change of difficulty when the number of objectives is increased, and (ii) the conflict degree between each pair of objectives. In order to analyze these topics, we compare the performance of a recently proposed multi-objective evolutionary algorithm against that of the well-known -MOEA, which has shown good results in many-objective problems.

Deep style estimator for 3D indoor object collection organization and scene synthesis

Background: Physical activity is known to play an important role in improving the cardio-pulmonary function and arterial blood oxygenation (SaO2) in elderly subjects. Objective: To evaluate the therapeutic efficacy of treadmill training on SaO2, maximum oxygen consumption (VO2 max) and maximum walking distance in elderly healthy subjects. Design. Intervention study Setting. El Salam General Hospitals, Cairo (Egypt). Participants: Our study included 41 subjects. All participants appeared healthy, without chronic diseases and passed the Cardiopulmonary Exercise Test. Intervention: Participants were incorporated in a treadmill-training program of moderate intensity to attain a fraction of 60% to 70% of the maximum heart rate. After 12 weeks of controlled physical activity, the program was made voluntarily for 36 weeks. Measurements: SaO2, VO2 max and maximum walking distance were measured for each individual one-week before and 12 weeks after training and at the 30 th and the 48 th week of the program. Results: Our study included 20 men and 21 women, with mean age of 65.1 + 2.7 years (Range 60-69). An increase in the mean values of SaO2, VO2 max and maximum walking distance after 12 weeks of controlled exercise and also at the 30th week and the 48th week of the program (P<0.05). There has been a decrease in the mean values of VO2 max, SaO2 and maximum walking distance after the 30 th and 48 th week in comparison with the values after the first 12 weeks. Conclusion: Controlled treadmill training for 12 weeks improved SaO2, VO2 max and maximum walking distance of the participants. The mean values of the participants decreased after the 30th and 48 th week due to lack of adherence to the unsupervised program.

The location of a facility can be examined in the context of several classical frameworks in economic geography and operation research where the facility is located such that some objective function is minimised (ie. Love et al., 1988; Ghosh and McLafferty, 1987; Wrigley, 1988; Fotheringham and O’Kelly, 1989). A frequently encountered objective, for example, is to find the optimal location of a new facility in terms of minimising the average time it takes individuals to travel to the nearest facility. That is, a new facility is added to an existing spatial distribution of facilities in order to achieve the maximum reduction in average travel times (the p-median problem). Other objective functions can of course be used to locate the new facility: it could for example, be located so that the maximum distance any individual has to travel is minimised (the minimax problem). Another slant on the problem is to simultaneously locate a set of facilities and to determine the allocation of demand to each of these facilities (the location-allocation problem). Still another is to model the choice of a facility by an individual as a probabilistic function of the attributes of each facility rather than as a deterministic one (a spatial interaction problem).

Clustering on multi-type relational data has attracted increasing interest due to its great practical and theoretical importance. One of the most popular solutions is nonnegative matrix factorization. However, previous work on nonnegative matrix factorization typically copes with multi-type relations individually, and ignores the underlying semantics conveyed by the relation propagation. Additionally, these approaches may suffer from data sparsity as most of the relations between object pairs are unknown. In this paper we propose a novel Meta-Path based Nonnegative Matrix Factorization (MPNMF) framework, which enriches potentially useful similarity semantics for the improved clustering performance. We begin with constructing meta-paths, i.e., paths that connects object types via a sequence of relations, which are appropriately weighted according to certain propagation decay rules. Based on the weighted meta-paths, we are promised to characterize the strength of pairwise interactions among the objects. Together with the attributes in the bag-of-word form, we cluster the objects of target type by collective nonnegative matrix factorization. Experiments on real world datasets demonstrate the effectiveness of our method.

Crosstalk remains an important determinant of stereoscopic 3D (S3D) image quality. Defined as the leakage of one eye's image into the image of the other eye it affects all commercially available stereoscopic viewing systems. Previously we have shown that crosstalk affects perceived depth magnitude in S3D displays. We found that perceived depth between two lines separated in depth decreased as crosstalk increased. The experiments described here extend our previous work to complex images of natural scenes. We controlled crosstalk levels by simulating them in images presented on a zero-crosstalk mirror stereoscope display. The observers were asked to estimate the amount of stereoscopic depth between pairs of objects in stereo-photographs of cluttered rooms. Data show that as crosstalk increased perceived depth decreased; an effect found at all disparities. Similarly to our previous experiments a significant decrease in perceived depth was observed with as little as 2-4% crosstalk. Taken together these results demonstrate that our previous findings generalize to natural scenes and show that crosstalk reduces perceived depth magnitude even in natural scenes with pictorial depth cues.

The Jordan centre of a graph is defined as a vertex whose maximum distance to other nodes in the graph is minimal, and it finds applications in facility location and source detection problems. We study properties of the Jordan centre in the case of random growing trees. In particular, we consider a regular tree graph on which an infection starts from a root node and then spreads along the edges of the graph according to various random spread models. For the Independent Cascade (IC) model and the discrete Susceptible Infected (SI) model, both of which are discrete-time models, we show that as the infected subgraph grows with time, the Jordan centre persists on a single vertex after a finite number of timesteps. As a corollary of our results, we also establish that the distance between the Jordan centre and the infection source (root node) is finite. Finally, we also study the continuous-time version of the SI model and bound the maximum distance between the Jordan centre and the root node at any time.

Mobile Backbone Wireless Networks (MBWN) [10] are wireless networks in which the base stations are mobile. Our strategy is the following: mobile nodes are dynamically grouped into clusters of bounded radius. In the very large wireless networks we deal with we deal with, several hundreds of clusters may be generated. Clustering makes use of a two dimensional Euclidean version of the Antipole Tree data structure [5]. This very effective structure was originally designed for finite sets of points in an arbitrary metric space to support efficient range searching. It requires only a linear number of pair-wise distance calculations among nodes. Mobile Base Stations occupy an approximate centroid of the clusters and are moved according to a fast practical bipartite matching algorithm which tries to minimize both total and maximum distance. We show that the best known computational geometry algorithms [1] become infeasible for our application when a high number of mobile base stations is required. On the other hand our proposed 8% average error solution requires O (k log k) running time instead of the approximatively O (k2) exact algorithm [1]. Communication among nodes is realized by a Clusterhead Gateway Switching Routing (CGSR) protocol [15] where the mobile base stations are organized in a suitable network. Other efficient clustering algorithms [11, 17] may be used instead of the Antipole Tree. However the nice hierarchical structure of the Antipole Tree makes it applicable to other types of mobile wireless (Ad-Hoc) and wired networks but this will be subject of future work.

A key problem in mechanism design is the construction of protocols that reach socially efficient decisions with minimal information revelation. This can reduce agent communication, and further, potentially increase privacy in the sense that agents reveal no more private information than is needed to determine an optimal outcome. This is not always possible: previous work has explored the tradeoff between communication cost and efficiency, and more recently, communication and privacy. We explore a third dimension: the tradeoff between privacy and efficiency. By sacrificing efficiency, we can improve the privacy of a variety of existing mechanisms. We analyze these tradeoffs in both second-price auctions and facility location problems (introducing new incremental mechanisms for facility location along the way). Our results show that sacrifices in efficiency can provide gains in privacy (and communication), in both the average and worst case.

Secondary or postdisinfection is widely used to maintain protective levels of disinfectant within water distribution systems. In contrast to conventional methods that apply disinfectant only at the treatment works, booster disinfection reapplies disinfectant at strategic locations within the distribution system to compensate for the losses that occur as it decays over time. Building on the writers’ previous work, this paper addresses the problem of locating disinfectant booster stations that minimize the dosage required to maintain residuals throughout the supply network, and introduces a useful parameterization for disinfectant source types. The model is related to the general fixed-charge facility location problem and is formulated as a mixed integer linear programming problem. Results for an example network show that disinfectant dosage savings are achievable with the adoption of booster disinfection, and that the rate of savings decreases as the number of booster stations utilized increases. Furthermore, booster disinfection may provide a more even distribution of disinfectant concentrations throughout the network, and has the potential to reduce aggregate exposure of the population to chlorine (and its by-products) while simultaneously improving residual coverage in the periphery of the distribution system.

p-Median based formulations with backbone facility locations

Facility Location embodies a class of problems concerned with locating a set of facilities to serve a geographically distributed population of customers at minimum cost. We address the classical Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure enough facility capacity and all the customers must be served. This is a well-known NP-hard problem in combinatorial optimization that has been extensively studied in the literature. Due to the difficulty of the problem, significant research efforts have been devoted to developing advanced heuristic methods aimed at finding high-quality solutions in reasonable computational times. We propose a Relaxation Adaptive Memory Programming (RAMP) approach for the CFLP. Our method combines lagrangean subgradient search with an improvement method to explore primal-dual relationships to create advanced memory structures that integrate information from both primal and dual solution spaces. The algorithm was tested on the standard ORLIB dataset and on other very large-scale instances for the CFLP. Our approach efficiently found the optimal solution for all ORLIB instances and very competitive results for the large-scale ones. Comparisons with current best-performing algorithms for the CFLP show that our RAMP algorithm exhibits excellent results.