Alliances and industry analysis

Traditionally alliances have been left at of industry analysis. We have been focusing basically on the economic characteristics determining bargaining power on the relationships between the actors in a value system. The paper proposes a methodology to analyze industries from a very different perspective that incorporates alliances as one of the main drivers of industry structure.Alliances; industry structure; networks;

Subsidiary strategy: The embeddedness component

This paper inductively derives a model that develops the concept of subsidiary embeddedness as the canvas within which subsidiary strategy can take place. Our model identifies three hierarchical levels of embeddedness: Operational embeddedness relates to the interlocking day-to-day relations. Capability embeddedness deals with the development of competitive capabilities for the multinational as a whole. Strategic embeddedness deals with subsidiary participation in the MNC strategy setting. We deem these three types of embeddedness as ways to develop subsidiary strategic alternatives. In as such, different types of subsidiary embeddedness imply different subsidiary roles. Embeddedness, as it was inductively derived from a revelatory case study, is not merely an outcome of the institutional setting, but a resource a subsidiary can manage by means of manipulating dependencies or exerting influence over the allocation of critical resources. A subsidiary can modify its embeddedness to change its strategic restraints. Therefore, the development of subsidiary embeddedness becomes an integral part of subsidiary strategy.Multinational management; subsidiary; strategy; organization;

Nuevas formas de trabajo en españa, Las

El presente documento describe los resultados preliminares de un proyecto de investigación orientado a entender los procesos de funcionamiento e implantación de lo que se ha dado en llamar «nuevos sistemas de trabajo». Se describe, en primer lugar, el marco conceptual del que se parte, para seguir con la descripción de la metodología empleada y los resultados del estudio.trabajo;

Focus! Rating XAI methods and finding biases

AI explainability improves the transparency and trustworthiness of models. However, in the domain of images, where deep learning has succeeded the most, explainability is still poorly assessed. In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model’s behavior using visual cues. However, no metrics have been established so far to assess and select these methods objectively. In this paper we propose a consistent evaluation score for feature attribution methods—the Focus—designed to quantify their coherency to the task. While most previous work adds outof-distribution noise to samples, we introduce a methodology to add noise from within the distribution. This is done through mosaics of instances from different classes, and the explanations these generate. On those, we compute a visual pseudo-precision metric, Focus. First, we show the robustness of the approach through a set of randomization experiments. Then we use Focus to compare six popular explainability techniques across several CNN architectures and classification datasets. Our results find some methods to be consistently reliable (LRP, GradCAM), while others produce class-agnostic explanations (SmoothGrad, IG). Finally we introduce another application of Focus, using it for the identification and characterization of biases found in models. This empowers bias-management tools, in another small step towards trustworthy AI.This work is supported by the European Union – H2020 Program under the “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” and by the Dept. de Recerca i Universitats of the Generalitat de Catalunya under the Industrial Doctorate Grant DI 2018-100.Peer ReviewedPostprint (author's final draft

Focus! Rating XAI methods and finding biases

AI explainability improves the transparency and trustworthiness of models. However, in the domain of images, where deep learning has succeeded the most, explainability is still poorly assessed. In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model’s behavior using visual cues. However, no metrics have been established so far to assess and select these methods objectively. In this paper we propose a consistent evaluation score for feature attribution methods—the Focus—designed to quantify their coherency to the task. While most previous work adds outof-distribution noise to samples, we introduce a methodology to add noise from within the distribution. This is done through mosaics of instances from different classes, and the explanations these generate. On those, we compute a visual pseudo-precision metric, Focus. First, we show the robustness of the approach through a set of randomization experiments. Then we use Focus to compare six popular explainability techniques across several CNN architectures and classification datasets. Our results find some methods to be consistently reliable (LRP, GradCAM), while others produce class-agnostic explanations (SmoothGrad, IG). Finally we introduce another application of Focus, using it for the identification and characterization of biases found in models. This empowers bias-management tools, in another small step towards trustworthy AI.This work is supported by the European Union – H2020 Program under the “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” and by the Dept. de Recerca i Universitats of the Generalitat de Catalunya under the Industrial Doctorate Grant DI 2018-100.Peer ReviewedPostprint (author's final draft

Focus and bias: will it blend?

One direct application of explainable AI feature attribution methods is to be used for detecting unwanted biases. To do so, domain experts typically have to review explained inputs, checking for the presence of unwanted biases learnt by the model. However, the huge amount of samples the domain experts must review makes this task more challenging as the size of the dataset grows. In an ideal case, domain experts should be provided only with a small number of selected samples containing potential biases. The recently published Focus score seems a promising tool for the selection of samples containing potential unwanted biases. In this work, we conduct a first study in this direction, analyzing the behavior of the Focus score when applied to a biased model. First, we verified that Focus is indeed sensitive to an induced bias. This is assessed by forcing a spurious correlation, training a model using only cats-indoor and dogs-outdoor. We empirically prove that the model learnt to distinguish the contexts (outdoor vs indoor) instead of cat vs dog classes, so ensuring that the model learnt an unwanted bias. Afterwards, we apply the Focus on this biased model showing how the Focus score decreases when the input contains the aforementioned bias. This analysis sheds light on the Focus behavior when applied to a biased model, highlighting its strengths for its use for bias detection.This work is supported by the European Union – H2020 Program under the “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” and by the Departament de Recerca i Universitats of the Generalitat de Catalunya under the Industrial Doctorate Grant DI 2018-100.Peer ReviewedPostprint (published version

MetH: A family of high-resolution and variable-shape image challenges

High-resolution and variable-shape images have not yet been properly addressed by the AI community. The approach of down-sampling data often used with convolutional neural networks is sub-optimal for many tasks, and has too many drawbacks to be considered a sustainable alternative. In sight of the increasing importance of problems that can benefit from exploiting high-resolution (HR) and variable-shape, and with the goal of promoting research in that direction, we introduce a new family of datasets (MetH). The four proposed problems include two image classification, one image regression and one super resolution task. Each of these datasets contains thousands of art pieces captured by HR and variable-shape images, labeled by experts at the Metropolitan Museum of Art. We perform an analysis, which shows how the proposed tasks go well beyond current public alternatives in both pixel size and aspect ratio variance. At the same time, the performance obtained by popular architectures on these tasks shows that there is ample room for improvement. To wrap up the relevance of the contribution we review the fields, both in AI and high-performance computing, that could benefit from the proposed challenges.This work is partially supported by the Intel-BSC Exascale Lab agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2017-SGR-1414).Preprin

An ingress and a complete transit of HD 80606 b

We have used four telescopes at different longitudes to obtain near-continuous lightcurve coverage of the star HD 80606 as it was transited by its \sim 4-MJup planet. The observations were performed during the predicted transit windows around the 25th of October 2008 and the 14th of February 2009. Our data set is unique in that it simultaneously constrains the duration of the transit and the planet's period. Our Markov-Chain Monte Carlo analysis of the light curves, combined with constraints from radial-velocity data, yields system parameters consistent with previously reported values. We find a planet-to-star radius ratio marginally smaller than previously reported, corresponding to a planet radius of Rp = 0.921 \pm 0.036RJup .Comment: 6 pages, 2 figures, MNRAS accepte

Observation of the full 12-hour-long transit of the exoplanet HD80606b. Warm-Spitzer photometry and SOPHIE spectroscopy

We present new observations of a transit of the 111-day-period exoplanet HD80606b. Using the Spitzer Space Telescope and its IRAC camera on the post-cryogenic mission, we performed a 19-hour-long photometric observation of HD80606 that covers the full transit of 13-14 January 2010. We complement this photometric data by new spectroscopic observations that we simultaneously performed with SOPHIE at Haute-Provence Observatory. This provides radial velocity measurements of the first half of the transit that was previously uncovered with spectroscopy. This new data set allows the parameters of this singular planetary system to be significantly refined. We obtained a planet-to-star radius ratio R_p/R_* = 0.1001 +/- 0.0006 that is slightly lower than the one measured from previous ground observations. We detected a feature in the Spitzer light curve that could be due to a stellar spot. We also found a transit timing about 20 minutes earlier than the ephemeris prediction; this could be caused by actual TTVs due to an additional body in the system or by underestimated systematic uncertainties. The sky-projected angle between the spin-axis of HD80606 and the normal to the planetary orbital plane is found to be lambda = 42 +/- 8 degrees thanks to the fit of the Rossiter-McLaughlin anomaly. This allows scenarios with aligned spin-orbit to be definitively rejected. Over the twenty planetary systems with measured spin-orbit angles, a few of them are misaligned; this is probably the signature of two different evolution scenarios for misaligned and aligned systems, depending if they experienced or not gravitational interaction with a third body. As in the case of HD80606b, most of the planetary systems including a massive planet are tilted; this could be the signature of a separate evolution scenario for massive planets in comparison with Jupiter-mass planets.Comment: 14 pages, 9 figures, 2 tables, accepted for publication in A&