Many Distances in Planar Graphs

We show how to compute in O(n ^4/3log?^1/3 n+n ^2/3 k ^2/3log?n) time the distance between k given pairs of vertices of a planar graph G with n vertices. This improves previous results whenever (n/log?n)^5/6≤k≤n ²/log?⁶ n. As an application, we speed up previous algorithms for computing the dilation of geometric planar graphs.     

Introduction to the special issue of the ECML PKDD 2021 journal track

Data Mining and Knowledge Discovery -     

A proposal for statistical evaluation of the detection of gunshot residues on a suspect

The possibility of accidental contamination of a suspect by gunshot residues (GSRs) is considered. If two hypotheses are taken into account ("the suspect has shot a firearm" and "the suspect has not shot a firearm"), the likelihood ratio of the conditional probabilities of finding a number n of GSRs is defined. Choosing two Poisson distributions, the parameter lambda of the first one coincides with the mean number of GSRs that can be found on a firearm shooter, while the parameter mu of the second one is the mean number of GSRs that can be found on a nonshooter. In this scenario, the likelihood ratio of the conditional probabilities of finding a number n of GSRs in the two hypotheses can be easily calculated. The evaluation of the two parameters lambda and mu and of the goodness of the two probability distributions is performed by using different sets of data: "exclusive" lead-antimony-barium GSRs have been detected in two populations of 31 and 28 police officers at diverse fixed times since firearm practice, and in a population of 81 police officers who stated that they had not handled firearms for almost 1 month. The results show that the Poisson distributions well fit the data for both shooters and nonshooters, and that the probability of detection of two or more GSRs is normally greater if the suspect has shot firearms.     

Synchronous condition-based consensus

The condition-based approach studies restrictions on the inputs to a distributed problem, called conditions, that facilitate its solution. Previous work considered mostly the asynchronous model of computation. This paper studies conditions for consensus in a synchronous system where processes can fail by crashing. It describes a full classification of conditions for consensus, establishing a continuum between the asynchronous and synchronous models, with the following hierarchy where includes all conditions (and in particular the trivial one made up of all possible input vectors). For a condition , we have:

Spatial Optimization Models for Water Supply Allocation

Climate change is likely to result in increased aridity, lower runoff, and declining water supplies for the cities of the Southwestern United States, including Phoenix. The situation in Phoenix is particularly complicated by the large number of water providers, each with its own supply portfolio, demand conditions, and conservation strategies. This paper details spatial optimization models to support water supply allocation between service provider districts, where some districts experience deficits and others experience surpluses in certain years. The approach seeks to reconcile and integrate projections derived from a complex simulation model taking into account current and future climate conditions. The formulated and applied models are designed to help better understand the expected increasingly complex interactions of providers under conditions of climate change. Preliminary results show cooperative agreements would reduce spot shortages that would occur even without climate change. In addition, they would substantially reduce deficits if climate change were to moderately reduce river flows in Phoenix’s major source regions, but have little effect under the most pessimistic scenarios because there are few surpluses available for re-allocation.     

3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

Multimedia Tools and Applications - This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully...     

Evolutionary multiobjective optimization using an outranking-based dominance generalization

One aspect that is often disregarded in the current research on evolutionary multiobjective optimization is the fact that the solution of a multiobjective optimization problem involves not only the search itself, but also a decision making process. Most current approaches concentrate on adapting an evolutionary algorithm to generate the Pareto frontier. In this work, we present a new idea to incorporate preferences into a multi-objective evolutionary algorithm (MOEA). We introduce a binary fuzzy preference relation that expresses the degree of truth of the predicate “x is at least as good as y”. On this basis, a strict preference relation with a reasonably high degree of credibility can be established on any population. An alternative x is not strictly outranked if and only if there does not exist an alternative y which is strictly preferred to x. It is easy to prove that the best solution is not strictly outranked. For validating our proposed approach, we used the non-dominated sorting genetic algorithm II (NSGA-II), but replacing Pareto dominance by the above non-outranked concept. So, we search for the non-strictly outranked frontier that is a subset of the Pareto frontier. In several instances of a nine-objective knapsack problem our proposal clearly outperforms the standard NSGA-II, achieving non-outranked solutions which are in an obviously privileged zone of the Pareto frontier.     

On 1-Laplacian Elliptic Equations Modeling Magnetic Resonance Image Rician Denoising

Modeling magnitude magnetic resonance images (MRI) Rician denoising in a Bayesian or generalized Tikhonov framework using total variation (TV) leads naturally to the consideration of nonlinear elliptic equations. These involve the so called 1-Laplacian operator and special care is needed to properly formulate the problem. The Rician statistics of the data are introduced through a singular equation with a reaction term defined in terms of modified first-order Bessel functions. An existence theory is provided here together with other qualitative properties of the solutions. Remarkably, each positive global minimum of the associated functional is one of such solutions. Moreover, we directly solve this nonsmooth nonconvex minimization problem using a convergent Proximal Point Algorithm. Numerical results based on synthetic and real MRI demonstrate a better performance of the proposed method when compared to previous TV-based models for Rician denoising which regularize or convexify the problem. Finally, an application on real Diffusion Tensor Images, a strongly affected by Rician noise MRI modality, is presented and discussed.