The application of building information modeling to enhance organisational learning

The construction industry is a knowledge-based industry where various actors with diverse expertise create unique information within different phases of a project. The industry has been criticized by researchers and practitioners as being unable to apply newly created knowledge effectively to innovate. The fragmented nature of the construction industry reduces the opportunity of project participants to learn from each other and absorb knowledge. Building Information Modelling (BIM), referring to digital representations of constructed facilities, is a promising technological advance that has been proposed to assist in the sharing of knowledge and creation of linkages between firms. Previous studies have mainly focused on the technical attributes of BIM and there is little evidence on its capability to enhance learning in construction firms. This conceptual paper identifies six ‘functional attributes’ of BIM that act as triggers to stimulate learning: (1) comprehensibility; (2) predictability; (3) accuracy; (4) transparency; (5) mutual understanding and; (6) integration.

The condensed nearest neighbor rule using the concept of mutual nearest neighborhood (Corresp.)

A two-stage iterative algorithm for selecting a subset of a training set of samples for use in a condensed nearest neighbor (CNN) decision rule is introduced. The proposed method uses the concept of mutual nearest neighborhood for selecting samples close to the decision line. The efficacy of the algorithm is brought out by means of an example.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

The Black Box of Mutual Fund Fees

This paper re-examines the determinants of mutual fund fees paid by mutual fund shareholders for management costs and other expenses. There are two novelties with respect to previous studies. First, each type of fee is explained separately. Second, the paper employs a new dataset consisting of Spanish mutual funds, making it the second paper to study mutual fund fees outside the US market. Furthermore, the Spanish market has three interesting characteristics: (i) both distribution and management are highly dominated by banks and savings banks, which points towards potential conflicts of interest; (ii) Spanish mutual fund law imposes caps on all types of fees; and (iii) Spain ranks first in terms of average mutual fund fees among similar countries. We find significant differences in mutual fund fees not explained by the fund’s investment objective. For instance, management companies owned by banks and savings banks charge higher management fees and redemption fees to nonguaranteed funds. Also, investors in older non-guaranteed funds and non-guaranteed funds with a lower average investment are more likely to end up paying higher management fees. Moreover, there is clear evidence that some mutual funds enjoy better conditions from custodial institutions than others. In contrast to evidence from the US market, larger funds are not associated with lower fees, but with higher custody fees for guaranteed funds and higher redemption fees for both types of funds. Finally, fee-setting by mutual funds is not related to fund before-fee performance.

When strangers pass: processing of mutual and averted social gaze in the superior temporal sulcus.

Using functional magnetic resonance imaging (fMRI), we investigated brain activity evoked by mutual and averted gaze in a compelling and commonly experienced social encounter. Through virtual-reality goggles, subjects viewed a man who walked toward them and shifted his neutral gaze either toward (mutual gaze) or away (averted gaze) from them. Robust activity was evoked in the superior temporal sulcus (STS) and fusiform gyrus (FFG). For both conditions, STS activity was strongly right lateralized. Mutual gaze evoked greater activity in the STS than did averted gaze, whereas the FFG responded equivalently to mutual and averted gaze. Thus, we show that the STS is involved in processing social information conveyed by shifts in gaze within an overtly social context. This study extends understanding of the role of the STS in social cognition and social perception by demonstrating that it is highly sensitive to the context in which a human action occurs.

Do mirrors reflect reality in agonistic encounters? A test of mutual cooperation in displays

Animals frequently engage in mutual displays that may allow or at least help decisions about the outcome of agonistic encounters with mutual benefit to the opponents. In fish these often involve lateral displays, with previous studies finding evidence of population-level lateralization with a marked preference for showing the right side and using the right eye. Because both opponents tend to show this preference a head to tail configuration is formed and is used extensively during the display phase. Here we tested the significance of these lateral displays by comparing displays to a mirror with those to a real opponent behind a transparent barrier. The frequency of displays was lower to a mirror but the individual displays were of greater duration indicating a slower pace of the interaction with a mirror. This suggests that fish respond to initiatives of real opponents but as mirror images do not initiate moves the focal fish only moves when it is ready to change position. However, lateralization was still found with mirrors, indicating that the right-side bias is a feature of the individual and not of the interaction between opponents. We discuss implications for ideas about the evolution of mutual cooperation and information exchange in contests, as well as the utility of the use of mirrors in the study of aggression in fish.

The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks.

We propose and advocate basic principles for the fusion of incomplete or uncertain information items, that should apply regardless of the formalism adopted for representing pieces of information coming from several sources. This formalism can be based on sets, logic, partial orders, possibility theory, belief functions or imprecise probabilities. We propose a general notion of information item representing incomplete or uncertain information about the values of an entity of interest. It is supposed to rank such values in terms of relative plausibility, and explicitly point out impossible values. Basic issues affecting the results of the fusion process, such as relative information content and consistency of information items, as well as their mutual consistency, are discussed. For each representation setting, we present fusion rules that obey our principles, and compare them to postulates specific to the representation proposed in the past. In the crudest (Boolean) representation setting (using a set of possible values), we show that the understanding of the set in terms of most plausible values, or in terms of non-impossible ones matters for choosing a relevant fusion rule. Especially, in the latter case our principles justify the method of maximal consistent subsets, while the former is related to the fusion of logical bases. Then we consider several formal settings for incomplete or uncertain information items, where our postulates are instantiated: plausibility orderings, qualitative and quantitative possibility distributions, belief functions and convex sets of probabilities. The aim of this paper is to provide a unified picture of fusion rules across various uncertainty representation settings.

The Macroeconomic Effects of Infrequent Information with Adjustment Costs

In the last decade, the potential macroeconomic effects of intermittent large adjustments in microeconomic decision variables such as prices, investment, consumption of durables or employment – a behavior which may be justified by the presence of kinked adjustment costs – have been studied in models where economic agents continuously observe the optimal level of their decision variable. In this paper, we develop a simple model which introduces infrequent information in a kinked adjustment cost model by assuming that agents do not observe continuously the frictionless optimal level of the control variable. Periodic releases of macroeconomic statistics or dividend announcements are examples of such infrequent information arrivals. We first solve for the optimal individual decision rule, that is found to be both state and time dependent. We then develop an aggregation framework to study the macroeconomic implications of such optimal individual decision rules. Our model has the distinct characteristic that a vast number of agents tend to act together, and more so when uncertainty is large. The average effect of an aggregate shock is inversely related to its size and to aggregate uncertainty. We show that these results differ substantially from the ones obtained with full information adjustment cost models.

Some Efficient Representations of 3D Information based on Octress

The thesis introduced the octree and addressed the complete nature of problems encountered, while building and imaging system based on octrees. An efficient Bottom-up recursive algorithm and its iterative counterpart for the raster to octree conversion of CAT scan slices, to improve the speed of generating the octree from the slices, the possibility of utilizing the inherent parallesism in the conversion programme is explored in this thesis. The octree node, which stores the volume information in cube often stores the average density information could lead to “patchy”distribution of density during the image reconstruction. In an attempt to alleviate this problem and explored the possibility of using VQ to represent the imformation contained within a cube. Considering the ease of accommodating the process of compressing the information during the generation of octrees from CAT scan slices, proposed use of wavelet transforms to generate the compressed information in a cube. The modified algorithm for generating octrees from the slices is shown to accommodate the eavelet compression easily. Rendering the stored information in the form of octree is a complex task, necessarily because of the requirement to display the volumetric information. The reys traced from each cube in the octree, sum up the density en-route, accounting for the opacities and transparencies produced due to variations in density.

Shape complexity based on mutual information

Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

The macroeconomic effects of infrequent information with adjustment costs

We extend the macroeconomic literature on Sstype rules by introducing infrequent information in a kinked ad justment cost model. We first show that optimal individual decision rules are both state-and -time dependent. We then develop an aggregation framework to study the macroeconomic implications of such optimal individual decision rules. In our model, a vast number of agents act together, and more so when uncertainty is large.The average effect of an aggregate shock is inversely related to its size and to aggregate uncertainty. These results are in contrast with those obtained with full information ad justment cost models.