Selective integration of auditory-visual looming cues by humans.

An object's motion relative to an observer can confer ethologically meaningful information. Approaching or looming stimuli can signal threats/collisions to be avoided or prey to be confronted, whereas receding stimuli can signal successful escape or failed pursuit. Using movement detection and subjective ratings, we investigated the multisensory integration of looming and receding auditory and visual information by humans. While prior research has demonstrated a perceptual bias for unisensory and more recently multisensory looming stimuli, none has investigated whether there is integration of looming signals between modalities. Our findings reveal selective integration of multisensory looming stimuli. Performance was significantly enhanced for looming stimuli over all other multisensory conditions. Contrasts with static multisensory conditions indicate that only multisensory looming stimuli resulted in facilitation beyond that induced by the sheer presence of auditory-visual stimuli. Controlling for variation in physical energy replicated the advantage for multisensory looming stimuli. Finally, only looming stimuli exhibited a negative linear relationship between enhancement indices for detection speed and for subjective ratings. Maximal detection speed was attained when motion perception was already robust under unisensory conditions. The preferential integration of multisensory looming stimuli highlights that complex ethologically salient stimuli likely require synergistic cooperation between existing principles of multisensory integration. A new conceptualization of the neurophysiologic mechanisms mediating real-world multisensory perceptions and action is therefore supported.

Relevance of cohort studies for the study of transplant infectious diseases.

The debate on the merits of observational studies as compared with randomized trials is ongoing. We will briefly touch on this subject, and demonstrate the role of cohort studies for the description of infectious disease patterns after transplantation. The potential benefits of cohort studies for the clinical management of patients outside of the expected gain in epidemiological knowledge are reviewed. The newly established Swiss Transplantation Cohort Study and in particular the part focusing on infectious diseases will serve as an illustration. A neglected area of research is the indirect value of large, multicenter cohort studies. These benefits can range from a deepened collaboration to the development of common definitions and guidelines. Unfortunately, very few data exist on the role of such indirect effects on improving quality of patient management. This review postulates an important role for cohort studies, which should not be viewed as inferior but complementary to established research tools, in particular randomized trials. Randomized trials remain the least bias-prone method to establish knowledge regarding the significance of diagnostic or therapeutic measures. Cohort studies have the power to reflect a real-world situation and to pinpoint areas of knowledge as well as of uncertainty. Prerequisite is a prospective design requiring a set of inclusive data coupled with the meticulous insistence on data retrieval and quality.

Electoral competition through issue selection

Politics must tackle multiple issues at once. In a first-best world, political competition constrains parties to prioritize issues according to the voters' true concerns. In the real world, the opposite also happens: parties manipulate voter priorities by emphasizing issues selectively during the political campaign. This phenomenon, known as priming, should allow parties to pay less attention to the issues that they intend to mute. We develop a model of endogenous issue ownership in which two vote-seeking parties (i) invest to attract voters with "better" policy proposals and (ii) choose a communication campaign to focus voter attention on specific issues. We identify novel feedbacks between communication and investment. In particular, we find that stronger priming effects can backfire by constraining parties to invest more resources in all issues, including the ones they would otherwise intend to mute. We also identify under which conditions parties prefer to focus on their "historical issues" or to engage in issue stealing. Typically, the latter happens when priming effects are strong, and historical reputations differentiates parties less.

Embedding Emotional Context in Recommender Systems

Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems

Tugan-Baranovsky on Socialism: From Utopia to the Economic Plan

Tugan-Baranovsky's ideas on socialism are reconstructed with an emphasis on the relation between political economy and utopia. Utopia enters the stage after the critique of capitalism, in the definition of the realm of possibilities in the world of ideas. With the help of ethics, the notion of ideal socialism, unreachable by definition, is defined in the sphere of utopia. Thus, the task of political economy is first to show which of these possible worlds are reachable in the real world, and second to choose the one that conforms better to ideal socialism: this is socialism in practice through the economic plan. Thus, far from considering utopia and science as contradictory, Tugan-Baranovsky saw them as complementary, and his socialism is the result of the dialogue he instituted between them.

Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsets.

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

Agenda d'actes i gestió fotogràfica web

L'objectiu principal d'aquest projecte ha estat aprofundir en la construcció de programari, abordant totes les etapes d'un projecte de construcció de programari des de la perspectiva de l'enginyeria del software (anàlisi, disseny, implementació i proves) i utilitzant el paradigma de programació Orientada a l'Objecte mitjançant l'ús de la tecnologia J2EE, conjuntament amb bastions de programari de gran importància en el mon real i per tant, en l'àmbit de desenvolupament de programari i tecnològic actuals.

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.

Collective Marketing Arrangements for Geographically Differentiated Agricultural Products: Welfare Impacts and Policy Implications, May 2006

This paper examines the incentive of atomistic agricultural producers within a specific geographical region to differentiate and collectively market products. We develop a model that allows us to analyze the market and welfare effects of the main types of real-world producer organizations, using it to derive economic insights regarding the circumstances under which these organizations will evolve, and describing implications of the results obtained in the context of an ongoing debate between the European Union and United States. As the anticipated fixed costs of development and marketing increase and the anticipated size of the market falls, it becomes essential to increase the ability of the producer organization to control supply in order to ensure the coverage of fixed costs. Whenever a collective organization allows a market (with a new product) to exist that otherwise would not have existed there is an increase in societal welfare. Counterintuitively, stronger property right protection for producer organizations may be welfare enhancing even after a differentiated product has been developed. The reason for this somewhat paradoxical result is that legislation aimed at curtailing the market power of producer organizations may induce large technological distortions.

On Consumer Decision Strategies: New Approaches for Studying and Aiding Preferential Choices

This dissertation focuses on the strategies consumers use when making purchase decisions. It is organized in two main parts, one centering on descriptive and the other on applied decision making research. In the first part, a new process tracing tool called InterActive Process Tracing (IAPT) is pre- sented, which I developed to investigate the nature of consumers' decision strategies. This tool is a combination of several process tracing techniques, namely Active Information Search, Mouselab, and retrospective verbal protocol. To validate IAPT, two experiments on mobile phone purchase de- cisions were conducted where participants first repeatedly chose a mobile phone and then were asked to formalize their decision strategy so that it could be used to make choices for them. The choices made by the identified strategies correctly predicted the observed choices in 73% (Experiment 1) and 67% (Experiment 2) of the cases. Moreover, in Experiment 2, Mouselab and eye tracking were directly compared with respect to their impact on information search and strategy description. Only minor differences were found between these two methods. I conclude that IAPT is a useful research tool to identify choice strategies, and that using eye tracking technology did not increase its validity beyond that gained with Mouselab. In the second part, a prototype of a decision aid is introduced that was developed building in particular on the knowledge about consumers' decision strategies gained in Part I. This decision aid, which is called the InterActive Choice Aid (IACA), systematically assists consumers in their purchase decisions. To evaluate the prototype regarding its perceived utility, an experiment was conducted where IACA was compared to two other prototypes that were based on real-world consumer decision aids. All three prototypes differed in the number and type of tools they provided to facilitate the process of choosing, ranging from low (Amazon) to medium (Sunrise/dpreview) to high functionality (IACA). Overall, participants slightly preferred the prototype of medium functionality and this prototype was also rated best on the dimensions of understandability and ease of use. IACA was rated best regarding the two dimensions of ease of elimination and ease of comparison of alternatives. Moreover, participants choices were more in line with the normatively oriented weighted additive strategy when they used IACA than when they used the medium functionality prototype. The low functionality prototype was the least preferred overall. It is concluded that consumers can and will benefit from highly functional decision aids like IACA, but only when these systems are easy to understand and to use.

Intra-Industry Trade, Multilateral Trade Integration, and Invasive Species Risk, December 2006

We analyze the linkage between protectionism and invasive species (IS) hazard in the context of two-way trade and multilateral trade integration, two major features of real-world agricultural trade. Multilateral integration includes the joint reduction of tariffs and trade costs among trading partners. Multilateral trade integration is more likely to increase damages from IS than predicted by unilateral trade opening under the classic Heckscher-Ohlin-Samuelson (HOS) framework because domestic production (the base susceptible to damages) is likely to increase with expanding export markets. A country integrating its trade with a partner characterized by relatively higher tariff and trade costs is also more likely to experience increased IS damages via expanded domestic production for the same reason. We illustrate our analytical results with a stylized model of the world wheat market.

Evaluation of Virtual Reality Snowplow Simulator Training, January 2007

Each winter, Iowa Department of Transportation (Iowa DOT) maintenance operators are responsible for plowing snow off federal and state roads in Iowa. Drivers typically work long shifts under treacherous conditions. In addition to properly navigating the vehicle, drivers are required to operate several plowing mechanisms simultaneously, such as plow controls and salt spreaders. There is little opportunity for practicing these skills in real-world situations. A virtual reality training program would provide operators with the opportunity to practice these skills under realistic yet safe conditions, as well as provide basic training to novice or less-experienced operators. In order to provide such training to snowplow operators in Iowa, the Iowa DOT purchased a snowplow simulator. The Iowa DOT commissioned a study through Iowa State University designed to (1) assess the use of this simulator as a training tool and (2) examine personality and other characteristics associated with being an experienced snowplow operator. The results of this study suggest that Iowa DOT operators of all ages and levels of experience enjoyed and seemed to benefit from virtual reality snowplow simulator training. Simulator sickness ratings were relatively low, implying that the simulator is appropriate for training a wide range of Iowa DOT operators. Many reported that simulator training was the most useful aspect of training for them.

Bubbles and capital flows

This paper presents a stylized model of international trade and asset price bubbles. Its central insight is that bubbles tend to appear and expand in countries where productivity is low relative to the rest of the world. These bubbles absorb local savings, eliminating inefficient investments and liberating resources that are in part used to invest in high productivity countries. Through this channel, bubbles act as a substitute for international capital flows, improving the international allocation of investment and reducing rate-of-return differentials across countries. This view of asset price bubbles could eventually provide a simple account of some real world phenomenae that have been difficult to model before, such as the recurrence and depth of financial crises or their puzzling tendency to propagate across countries.

AWSum-Combining Classification with Knowledge Aquisition

Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the^way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whilst providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.