Why do oil importers diversify their import sources politically? : evidence from U.S. firm-level data

International politics affects oil trade. But why? We construct a firm-level dataset for all U.S. oil-importing companies over 1986-2008 to examine what kinds of firms are more responsive to change in "political distance" between the U.S. and her trading partners, measured by divergence in their UN General Assembly voting patterns. Consistent with previous macro evidence, we first show that individual firms diversify their oil imports politically, even after controlling for unobserved firm heterogeneity. We conjecture that the political pattern of oil imports from these individual firms is driven by hold-up risks, because oil trade is often associated with backward vertical FDI. To test this hold-up risk hypothesis, we investigate heterogeneity in responses by matching transaction-level import data with firm-level worldwide reserves. Our results show that long-run oil import decisions are indeed more elastic for firms with oil reserves overseas than those without, although the reverse is true in the short run. We interpret this empirical regularity as that while firms trade in the spot market can adjust their imports immediately, vertically-integrated firms with investment overseas tend to commit to term contracts in the short run even though they are more responsive to changes in international politics in the long run.

User-centric need-driven affect modeling for spoken conversational agents: design and evaluation

It is easy to get frustrated at spoken conversational agents (SCAs), perhaps because they seem to be callous. By and large, the quality of human-computer interaction is affected due to the inability of the SCAs to recognise and adapt to user emotional state. Now with the mass appeal of artificially-mediated communication, there has been an increasing need for SCAs to be socially and emotionally intelligent, that is, to infer and adapt to their human interlocutors’ emotions on the fly, in order to ascertain an affective, empathetic and naturalistic interaction. An enhanced quality of interaction would reduce users’ frustrations and consequently increase their satisfactions. These reasons have motivated the development of SCAs towards including socio-emotional elements, turning them into affective and socially-sensitive interfaces. One barrier to the creation of such interfaces has been the lack of methods for modelling emotions in a task-independent environment. Most emotion models for spoken dialog systems are task-dependent and thus cannot be used “as-is” in different applications. This Thesis focuses on improving this, in which it concerns computational modeling of emotion, personality and their interrelationship for task-independent autonomous SCAs. The generation of emotion is driven by needs, inspired by human’s motivational systems. The work in this Thesis is organised in three stages, each one with its own contribution. The first stage involved defining, integrating and quantifying the psychological-based motivational and emotional models sourced from. Later these were transformed into a computational model by implementing them into software entities. The computational model was then incorporated and put to test with an existing SCA host, a HiFi-control agent. The second stage concerned automatic prediction of affect, which has been the main challenge towards the greater aim of infusing social intelligence into the HiFi agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. In this stage, we attempted to address part of this challenge by considering the roles of user satisfaction ratings and conversational/dialog features as the respective target and predictors in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. The final stage concerned the evaluation of the emotional model through the HiFi agent. A series of user studies with 70 subjects were conducted in a real-time environment, each in a different phase and with its own conditions. All the studies involved the comparisons between the baseline non-modified and the modified agent. The findings have gone some way towards enhancing our understanding of the utility of emotion in spoken dialog systems in several ways; first, an SCA should not express its emotions blindly, albeit positive. Rather, it should adapt its emotions to user states. Second, low performance in an SCA may be compensated by the exploitation of emotion. Third, the expression of emotion through the exploitation of prosody could better improve users’ perceptions of an SCA compared to exploiting emotions through just lexical contents. Taken together, these findings not only support the success of the emotional model, but also provide substantial evidences with respect to the benefits of adding emotion in an SCA, especially in mitigating users’ frustrations and ultimately improving their satisfactions. Resumen Es relativamente fácil experimentar cierta frustración al interaccionar con agentes conversacionales (Spoken Conversational Agents, SCA), a menudo porque parecen ser un poco insensibles. En general, la calidad de la interacción persona-agente se ve en cierto modo afectada por la incapacidad de los SCAs para identificar y adaptarse al estado emocional de sus usuarios. Actualmente, y debido al creciente atractivo e interés de dichos agentes, surge la necesidad de hacer de los SCAs unos seres cada vez más sociales y emocionalmente inteligentes, es decir, con capacidad para inferir y adaptarse a las emociones de sus interlocutores humanos sobre la marcha, de modo que la interacción resulte más afectiva, empática y, en definitiva, natural. Una interacción mejorada en este sentido permitiría reducir la posible frustración de los usuarios y, en consecuencia, mejorar el nivel de satisfacción alcanzado por los mismos. Estos argumentos justifican y motivan el desarrollo de nuevos SCAs con capacidades socio-emocionales, dotados de interfaces afectivas y socialmente sensibles. Una de las barreras para la creación de tales interfaces ha sido la falta de métodos de modelado de emociones en entornos independientes de tarea. La mayoría de los modelos emocionales empleados por los sistemas de diálogo hablado actuales son dependientes de tarea y, por tanto, no pueden utilizarse "tal cual" en diferentes dominios o aplicaciones. Esta tesis se centra precisamente en la mejora de este aspecto, la definición de modelos computacionales de las emociones, la personalidad y su interrelación para SCAs autónomos e independientes de tarea. Inspirada en los sistemas motivacionales humanos en el ámbito de la psicología, la tesis propone un modelo de generación/producción de la emoción basado en necesidades. El trabajo realizado en la presente tesis está organizado en tres etapas diferenciadas, cada una con su propia contribución. La primera etapa incluyó la definición, integración y cuantificación de los modelos motivacionales de partida y de los modelos emocionales derivados a partir de éstos. Posteriormente, dichos modelos emocionales fueron plasmados en un modelo computacional mediante su implementación software. Este modelo computacional fue incorporado y probado en un SCA anfitrión ya existente, un agente con capacidad para controlar un equipo HiFi, de alta fidelidad. La segunda etapa se orientó hacia el reconocimiento automático de la emoción, aspecto que ha constituido el principal desafío en relación al objetivo mayor de infundir inteligencia social en el agente HiFi. En los últimos años, los estudios sobre reconocimiento de emociones a partir de la voz han pasado de emplear datos actuados a usar datos reales en los que la presencia u observación de emociones se produce de una manera mucho más sutil. El reconocimiento de emociones bajo estas condiciones resulta mucho más complicado y esta dificultad se pone de manifiesto en tareas tales como el etiquetado y el aprendizaje automático. En esta etapa, se abordó el problema del reconocimiento de las emociones del usuario a partir de características o métricas derivadas del propio diálogo usuario-agente. Gracias a dichas métricas, empleadas como predictores o indicadores del grado o nivel de satisfacción alcanzado por el usuario, fue posible discriminar entre satisfacción y frustración, las dos emociones prevalentes durante la interacción usuario-agente. La etapa final corresponde fundamentalmente a la evaluación del modelo emocional por medio del agente Hifi. Con ese propósito se llevó a cabo una serie de estudios con usuarios reales, 70 sujetos, interaccionando con diferentes versiones del agente Hifi en tiempo real, cada uno en una fase diferente y con sus propias características o capacidades emocionales. En particular, todos los estudios realizados han profundizado en la comparación entre una versión de referencia del agente no dotada de ningún comportamiento o característica emocional, y una versión del agente modificada convenientemente con el modelo emocional propuesto. Los resultados obtenidos nos han permitido comprender y valorar mejor la utilidad de las emociones en los sistemas de diálogo hablado. Dicha utilidad depende de varios aspectos. En primer lugar, un SCA no debe expresar sus emociones a ciegas o arbitrariamente, incluso aunque éstas sean positivas. Más bien, debe adaptar sus emociones a los diferentes estados de los usuarios. En segundo lugar, un funcionamiento relativamente pobre por parte de un SCA podría compensarse, en cierto modo, dotando al SCA de comportamiento y capacidades emocionales. En tercer lugar, aprovechar la prosodia como vehículo para expresar las emociones, de manera complementaria al empleo de mensajes con un contenido emocional específico tanto desde el punto de vista léxico como semántico, ayuda a mejorar la percepción por parte de los usuarios de un SCA. Tomados en conjunto, los resultados alcanzados no sólo confirman el éxito del modelo emocional, sino xv que constituyen además una evidencia decisiva con respecto a los beneficios de incorporar emociones en un SCA, especialmente en cuanto a reducir el nivel de frustración de los usuarios y, en última instancia, mejorar su satisfacción.

SSONDE: Semantic Similarity On liNked Data Entities

Linked data offers a promising setting to encode, publish and share metadata of resources. As the matter of fact, it is already adopted by data producers such as European Environment Agency, US and some EU Governs, whose first ambition is to share (meta)data making their processes more effective and transparent. Such as an increasing interest and involvement of data providers surely represents a genuine witness of the web of data success, but in a longer perspective, frameworks supporting linked data consumers in their decision making processes will be a compelling need. In this respect, the talk is introducing SSONDE, a framework enabling in detailed comparison, ranking and selection of linked data resources through the analysis of their RDF ontology driven metadata. SSONDE implements an instance similarity especially designed to support in resource selection, namely the process stakeholders engage to choose a set of resources suitable for a given analysis purpose: (i) it deploys an asymmetric similarity assessment to emphasize information about gains and losses the stakeholders get adopting a resource in place of another; (ii) it relies on an explicit formalization of contexts to tailor the similarity assessment with respect to specific user-defined selection goals. The talk aims at providing an insight on SSONDE instance similarity and it will briefly describe some examples of SSONDE deployment in the context of linked data consumption.

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.

A Coreference Service for Enterprise Application Integration using Linked Data

The use of semantic and Linked Data technologies for Enterprise Application Integration (EAI) is increasing in recent years. Linked Data and Semantic Web technologies such as the Resource Description Framework (RDF) data model provide several key advantages over the current de-facto Web Service and XML based integration approaches. The flexibility provided by representing the data in a more versatile RDF model using ontologies enables avoiding complex schema transformations and makes data more accessible using Web standards, preventing the formation of data silos. These three benefits represent an edge for Linked Data-based EAI. However, work still has to be performed so that these technologies can cope with the particularities of the EAI scenarios in different terms, such as data control, ownership, consistency, or accuracy. The first part of the paper provides an introduction to Enterprise Application Integration using Linked Data and the requirements imposed by EAI to Linked Data technologies focusing on one of the problems that arise in this scenario, the coreference problem, and presents a coreference service that supports the use of Linked Data in EAI systems. The proposed solution introduces the use of a context that aggregates a set of related identities and mappings from the identities to different resources that reside in distinct applications and provide different views or aspects of the same entity. A detailed architecture of the Coreference Service is presented explaining how it can be used to manage the contexts, identities, resources, and applications which they relate to. The paper shows how the proposed service can be utilized in an EAI scenario using an example involving a dashboard that integrates data from different systems and the proposed workflow for registering and resolving identities. As most enterprise applications are driven by business processes and involve legacy data, the proposed approach can be easily incorporated into enterprise applications.

Probabilistic reasoning with an enzyme-driven DNA device

We present a biomolecular probabilistic model driven by the action of a DNA toolbox made of a set of DNA templates and enzymes that is able to perform Bayesian inference. The model will take single-stranded DNA as input data, representing the presence or absence of a specific molecular signal (the evidence). The program logic uses different DNA templates and their relative concentration ratios to encode the prior probability of a disease and the conditional probability of a signal given the disease. When the input and program molecules interact, an enzyme-driven cascade of reactions (DNA polymerase extension, nicking and degradation) is triggered, producing a different pair of single-stranded DNA species. Once the system reaches equilibrium, the ratio between the output species will represent the application of Bayes? law: the conditional probability of the disease given the signal. In other words, a qualitative diagnosis plus a quantitative degree of belief in that diagno- sis. Thanks to the inherent amplification capability of this DNA toolbox, the resulting system will be able to to scale up (with longer cascades and thus more input signals) a Bayesian biosensor that we designed previously.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

Identification and correction of besouro's faults: impact on test‐driven development experiments

Context. This thesis is framed in experimental software engineering. More concretely, it addresses the problems arisen when assessing process conformance in test-driven development experiments conducted by UPM's Experimental Software Engineering group. Process conformance was studied using the Eclipse's plug-in tool Besouro. It has been observed that Besouro does not work correctly in some circumstances. It creates doubts about the correction of the existing experimental data which render it useless. Aim. The main objective of this work is the identification and correction of Besouro's faults. A secondary goal is fixing the datasets already obtained in past experiments to the maximum possible extent. This way, existing experimental results could be used with confidence. Method. (1) Testing Besouro using different sequences of events (creation methods, assertions etc..) to identify the underlying faults. (2) Fix the code and (3) fix the datasets using code specially created for this purpose. Results. (1) We confirmed the existence of several fault in Besouro's code that affected to Test-First and Test-Last episode identification. These faults caused the incorrect identification of 20% of episodes. (2) We were able to fix Besouro's code. (3) The correction of existing datasets was possible, subjected to some restrictions (such us the impossibility of tracing code size increase to programming time. Conclusion. The results of past experiments dependent upon Besouro's data could no be trustable. We have the suspicion that more faults remain in Besouro's code, whose identification requires further analysis.

Feedback-inducible nuclear-receptor-driven reporter gene expression in transgenic mice

Understanding nuclear receptor signaling in vivo would be facilitated by an efficient methodology to determine where a nuclear receptor is active. Herein, we present a feedback-inducible expression system in transgenic mice to detect activated nuclear receptor effector proteins by using an inducible reporter gene. With this approach, reporter gene induction is not limited to a particular tissue, and, thus, this approach provides the opportunity for whole-animal screens. Furthermore, the effector and reporter genes are combined to generate a single strain of transgenic mice, which enables direct and rapid analysis of the offspring. The system was applied to localize sites where the retinoic acid receptor ligand-binding domain is activated in vivo. The results identify previously discovered sources of retinoids in the embryo and indicate the existence of previously undiscovered regions of retinoic acid receptor signaling in vivo. Notably, the feedback-inducible nuclear-receptor-driven assay, combined with an independent in vitro assay, provides evidence for a site of retinoid synthesis in the isthmic mesenchyme. These data illustrate the potential of feedback-inducible nuclear-receptor-driven analyses for assessing in vivo activation patterns of nuclear receptors and for analyzing pharmacological properties of natural and synthetic ligands of potential therapeutic value.

Somatic hypermutation of the new antigen receptor gene (NAR) in the nurse shark does not generate the repertoire: Possible role in antigen-driven reactions in the absence of germinal centers

The new antigen receptor (NAR) gene in the nurse shark diversifies extensively by somatic hypermutation. It is not known, however, whether NAR somatic hypermutation generates the primary repertoire (like in the sheep) or rather is used in antigen-driven immune responses. To address this issue, the sequences of NAR transmembrane (Tm) and secretory (Sec) forms, presumed to represent the primary and secondary repertoires, respectively, were examined from the peripheral blood lymphocytes of three adult nurse sharks. More than 40% of the Sec clones but fewer than 11% of Tm clones contained five mutations or more. Furthermore, more than 75% of the Tm clones had few or no mutations. Mutations in the Sec clones occurred mostly in the complementarity-determining regions (CDR) with a significant bias toward replacement substitutions in CDR1; in Tm clones there was no significant bias toward replacements and only a low level of targeting to the CDRs. Unlike the Tm clones where the replacement mutational pattern was similar to that seen for synonymous changes, Sec replacements displayed a distinct pattern of mutations. The types of mutations in NAR were similar to those found in mouse Ig genes rather than to the unusual pattern reported for shark and Xenopus Ig. Finally, an oligoclonal family of Sec clones revealed a striking trend toward acquisition of glutamic/aspartic acid, suggesting some degree of selection. These data strongly suggest that hypermutation of NAR does not generate the repertoire, but instead is involved in antigen-driven immune responses.

Antigen-driven CD4+ T cell and HIV-1 dynamics: Residual viral replication under highly active antiretroviral therapy

Antigen-induced stimulation of the immune system can generate heterogeneity in CD4+ T cell division rates capable of explaining the temporal patterns seen in the decay of HIV-1 plasma RNA levels during highly active antiretroviral therapy. Posttreatment increases in peripheral CD4+ T cell counts are consistent with a mathematical model in which host cell redistribution between lymph nodes and peripheral blood is a function of viral burden. Model fits to patient data suggest that, although therapy reduces HIV replication below replacement levels, substantial residual replication continues. This residual replication has important consequences for long-term therapy and the evolution of drug resistance and represents a challenge for future treatment strategies.

Collagenase-3 Induction in Rat Lung Fibroblasts Requires the Combined Effects of Tumor Necrosis Factor-α and 12-Lipoxygenase Metabolites: A Model of Macrophage-induced, Fibroblast-driven Extracellular Matrix Remodeling during Inflammatory Lung Injury

The mechanisms responsible for the induction of matrix-degrading proteases during lung injury are ill defined. Macrophage-derived mediators are believed to play a role in regulating synthesis and turnover of extracellular matrix at sites of inflammation. We find a localized increase in the expression of the rat interstitial collagenase (MMP-13; collagenase-3) gene from fibroblastic cells directly adjacent to macrophages within silicotic rat lung granulomas. Conditioned medium from macrophages isolated from silicotic rat lungs was found to induce rat lung fibroblast interstitial collagenase gene expression. Conditioned medium from primary rat lung macrophages or J774 monocytic cells activated by particulates in vitro also induced interstitial collagenase gene expression. Tumor necrosis factor-α (TNF-α) alone did not induce interstitial collagenase expression in rat lung fibroblasts but did in rat skin fibroblasts, revealing tissue specificity in the regulation of this gene. The activity of the conditioned medium was found to be dependent on the combined effects of TNF-α and 12-lipoxygenase-derived arachidonic acid metabolites. The fibroblast response to this conditioned medium was dependent on de novo protein synthesis and involved the induction of nuclear activator protein-1 activity. These data reveal a novel requirement for macrophage-derived 12-lipoxygenase metabolites in lung fibroblast MMP induction and provide a mechanism for the induction of resident cell MMP gene expression during inflammatory lung processes.

Unusual 1H NMR chemical shifts support (His) Cɛ1—H⋅⋅⋅O⩵C H-bond: Proposal for reaction-driven ring flip mechanism in serine protease catalysis

13C-selective NMR, combined with inhibitor perturbation experiments, shows that the Cɛ1—H proton of the catalytic histidine in resting α-lytic protease and subtilisin BPN′ resonates, when protonated, at 9.22 ppm and 9.18 ppm, respectively, which is outside the normal range for such protons and ≈0.6 to 0.8 ppm further downfield than previously reported. They also show that the previous α-lytic protease assignments [Markley, J. L., Neves, D. E., Westler, W. M., Ibanez, I. B., Porubcan, M. A. & Baillargeon, M. W. (1980) Front. Protein Chem. 10, 31–61] were to signals from inactive or denatured protein. Simulations of linewidth vs. pH demonstrate that the true signal is more difficult to detect than corresponding signals from inactive derivatives, owing to higher imidazole pKa values and larger chemical shift differences between protonated and neutral forms. A compilation and analysis of available NMR data indicates that the true Cɛ1—H signals from other serine proteases are similarly displaced downfield, with past assignments to more upfield signals probably in error. The downfield displacement of these proton resonances is shown to be consistent with an H-bond involving the histidine Cɛ1—H as donor, confirming the original hypothesis of Derewenda et al. [Derewenda, Z. S., Derewenda, U. & Kobos, P. M. (1994) J. Mol. Biol. 241, 83–93], which was based on an analysis of literature x-ray crystal structures of serine hydrolases. The invariability of this H-bond among enzymes containing Asp-His-Ser triads indicates functional importance. Here, we propose that it enables a reaction-driven imidazole ring flip mechanism, overcoming a major dilemma inherent in all previous mechanisms, namely how these enzymes catalyze both the formation and productive breakdown of tetrahedral intermediates.

Protein Information Resource: a community resource for expert annotation of protein data

The Protein Information Resource, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the most comprehensive and expertly annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database. To provide timely and high quality annotation and promote database interoperability, the PIR-International employs rule-based and classification-driven procedures based on controlled vocabulary and standard nomenclature and includes status tags to distinguish experimentally determined from predicted protein features. The database contains about 200 000 non-redundant protein sequences, which are classified into families and superfamilies and their domains and motifs identified. Entries are extensively cross-referenced to other sequence, classification, genome, structure and activity databases. The PIR web site features search engines that use sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. The PIR-Inter­national databases and search tools are accessible on the PIR web site at http://pir.georgetown.edu/ and at the MIPS web site at http://www.mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP.