Invoking a cartesian product structure on social states: new resolutions of Sen's and Gibbard's impossibility theorems

The purpose of this article is to introduce a Cartesian product structure into the social choice theoretical framework and to examine if new possibility results to Gibbard's and Sen's paradoxes can be developed thanks to it. We believe that a Cartesian product structure is a pertinent way to describe individual rights in the social choice theory since it discriminates the personal features comprised in each social state. First we define some conceptual and formal tools related to the Cartesian product structure. We then apply these notions to Gibbard's paradox and to Sen's impossibility of a Paretian liberal. Finally we compare the advantages of our approach to other solutions proposed in the literature for both impossibility theorems.

Interactions between product and labour market reforms

Labour market reforms face very often opposition from the employed workers, because it normally reduces their wages. Also product market regulations are regularly biased towards too much benefitting the firms. As a result there remain many frictions in both the labour and product markets that hinder an optimal functioning of the economy. These issues have recently received a lot of attention in the economics literature and scholars have been looking for politically viable reforms in both markets. However, despite its potential importance, there has been done virtually no research on the interaction between reforms in product and labour markets. We find that when combining reforms, the opposition for reforms decreases considerably. This is because there exist complementarities and the gains in total welfare can be more evenly distributed over the interest groups. Moreover, the interaction of reforms offers a way out for the so-called 'sclerosis' effect.

Multi-product firms and product variety

The goal of this paper is to study the role of multi-product firms in the market provision of product variety. The analysis is conducted using the spokes model of non-localized competition proposed by Chen and Riordan (2007). Firstly, we show that multi-product firms are at a competitive disadvantage vis-a-vis single-product firms and can only emerge if economies of scope are sufficiently strong. Secondly, under duopoly product variety may be higher or lower with respect to both the first best and the monopolistically competitive equilibrium. However, within a relevant range of parameter values duopolists drastically restrict their product range in order to relax price competition, and as a result product variety is far below the efficient level.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Technical progress and product reliability under competition and monopoly

Technical progress lowers costs and prices but appears to have an ambiguous effect on product reliabilty. This paper presents a simple model which explains this observation.

Some variational problems from image processing

"Vegeu el resum a l'inici del document del fitxer adjunt."

How to set up and apply reference levels in fluoroscopy at a national level.

A nationwide survey was launched to investigate the use of fluoroscopy and establish national reference levels (RL) for dose-intensive procedures. The 2-year investigation covered five radiology and nine cardiology departments in public hospitals and private clinics, and focused on 12 examination types: 6 diagnostic and 6 interventional. A total of 1,000 examinations was registered. Information including the fluoroscopy time (T), the number of frames (N) and the dose-area product (DAP) was provided. The data set was used to establish the distributions of T, N and the DAP and the associated RL values. The examinations were pooled to improve the statistics. A wide variation in dose and image quality in fixed geometry was observed. As an example, the skin dose rate for abdominal examinations varied in the range of 10 to 45 mGy/min for comparable image quality. A wide variability was found for several types of examinations, mainly complex ones. DAP RLs of 210, 125, 80, 240, 440 and 110 Gy cm2 were established for lower limb and iliac angiography, cerebral angiography, coronary angiography, biliary drainage and stenting, cerebral embolization and PTCA, respectively. The RL values established are compared to the data published in the literature.

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.