Spatial clustering and its effect on perceived clustering, numerosity, and dispersion

Human observers are able to estimate the numerosity of large sets of visual elements. The occupancy model of perceived numerosity in intermediate numerical ranges is based on overlapping regions of influence. The key idea is that items within a certain range count for less than their actual numerical value and more so the closer they are to their neighbours. Therefore occupancy is sensitive to the grouping of elements, but there are other spatial properties of  configurations that could also influence perceived numerosity, such as: area of convex hull, occupancy area, total degree of connectivity, and local clustering For all indices apart from convex hull, we varied the radius of the area that defined neighbours. We tested perceived numerosity using a fixed number of elements placed at random within a circular region. Observers compared two patterns (presented in two intervals) and chose the one that appeared more numerous. The same observers performed two other separate tasks in which they judged which pattern appeared more dispersed or more clustered. In each pair of images, the number was always the same (22, 28, 34, or 40 items), because we were interested in which "appeared" more numerous on the basis of spatial configuration. The results suggest that estimates of numerosity, dispersion, and clustering are based on different spatial information, that there are alternative approaches to quantifying clustering, and that in all cases clustering is linked to a decrease in perceived numerosity. The alternative measures have different properties and different practical and computational advantages

Independent sets in Line of Sight networks

Line of Sight (LoS) networks provide a model of wireless communication which incor-porates visibility constraints. Vertices of such networks can be embedded onto the cube{(x1,x2,...,xd):xi∈{1,...,n},1≤i≤d}so that two vertices are adjacent if and onlyif their images lay on a line parallel to one of the cube edges and their distance is lessthan a given range parameterω. In this paper we study large independent sets in LoSnetworks.Weprovethatthecomputationalproblemoffindingamaximumindependentset can be solved optimally in polynomial time for one dimensional LoS networks.However, ford≥2, the (decision version of) the problem becomes NP-complete for anyfixedω≥3. In addition, we show that the problem is APX-hard whenω=nford≥3.On the positive side, we show that LoS networks generalize chordal graphs. This impliesthat there exists a simpled-approximation algorithm for the maximum independent setproblem in LoS networks. Finally, we describe a polynomial time approximation schemefor the maximum independent set problem in LoS networks for the case whenωis aconstantandpresentanimprovedheuristicalgorithmfortheprobleminthecaseω=

On the approximability of the maximum induced matching problem

In this paper we consider the approximability of the maximum induced matching problem (MIM). We give an approximation algorithm with asymptotic performance ratio <i>d</i>-1 for MIM in <i>d</i>-regular graphs, for each <i>d</i>≥3. We also prove that MIM is APX-complete in <i>d</i>-regular graphs, for each <i>d</i>≥3

An abstract argumentation approach for the prediction of analysts’ recommendations following earnings conference calls

Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts' recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions Answers (QA) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts' recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts' prediction about the company's trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis

The Effect of Graph Layout on the Perception of Graph Density: An Empirical Study

The visual representation of a graph is crucial in understanding and analyzing its properties. In this empirical study, we examine the effect of different drawing layouts on our perception of graph density. We treat density as an absolute property of the graph and use a Yes-No design, where participants have to decide whether a graph has a given density or not. We compare a simple grid layout with well-known planar and spring layouts. We also introduce an alternative ‘improved’ grid layout, which reduces the number of crossings while keeping most of the simplicity of the original grid layout. Results show that our ‘improved’ version of the grid layout facilitated performance on the task, compared to the original one. Moreover, participants were biased into judging graphs as denser when drawn with the original grid layout, while tended to perceive graphs as less dense when drawn with the planar and grid layouts. In contrast to previous studies on graph density perception, this is the first indication that the chosen layout can influence our perception of the graph’s density