A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models

We study dynamic discrete choice models, where a commonly studied problem involves estimating parameters of agent reward functions (also known as "structural" parameters), using agent behavioral data. Maximum likelihood estimation for such models requires dynamic programming, which is limited by the curse of dimensionality. In this work, we present a novel algorithm that provides a data-driven method for selecting and aggregating states, which lowers the computational and sample complexity of estimation. Our method works in two stages. In the first stage, we use a flexible inverse reinforcement learning approach to estimate agent Q-functions. We use these estimated Q-functions, along with a clustering algorithm, to select a subset of states that are the most pivotal for driving changes in Q-functions. In the second stage, with these selected "aggregated" states, we conduct maximum likelihood estimation using a commonly used nested fixed-point algorithm. The proposed two-stage approach mitigates the curse of dimensionality by reducing the problem dimension. Theoretically, we derive finite-sample bounds on the associated estimation error, which also characterize the trade-off of computational complexity, estimation error, and sample complexity. We demonstrate the empirical performance of the algorithm in two classic dynamic discrete choice estimation applications

Asynchronous and Synchronous Teaching and Learning in High-School Distance Education

This paper presents the results of an inductive, interpretive analysis of the perspectives of 42 Canadian high school distance education (DE) teachers on asynchronous and synchronous online teaching. The paper includes a conceptual overview of the affordances and constraints of each form of teaching. Findings provided insight into the following aspects of asynchronous and synchronous online teaching: degree of use; the tools used; the contexts in which each occur; students’ preferences; and limitations. Pedagogy emerged as more important than media for both asynchronous and synchronous online teaching. Synchronous online teaching relied on teacher- rather than student-centred approaches. Asynchronous online teaching provided support for selfpaced, highly independent forms of secondary DE supplemented by synchronous online teaching for answering questions and troubleshooting

Enantioselective synthesis of cyclobutylboronates via a copper-catalyzed desymmetrization approach

This is the peer reviewed version of the following article: Guisán-Ceinos, M., Parra, A., Martín-Heras, V. and Tortosa, M. (2016), Enantioselective Synthesis of Cyclobutylboronates via a Copper-Catalyzed Desymmetrization Approach. Angew. Chem., which has been published in final form at http://dx.doi.org/10.1002/ange.201601976. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In this report, the first catalytic enantioselective synthesis of cyclobutylboronates, using a chiral copper(I) complex, is disclosed. A broad variety of cyclobutanes have been prepared with consistently high levels of diastereo- and enantiocontrol. Moreover, this method constitutes the first report of an enantioselective desymmetrization of meso-cyclobutenes to prepare chiral cyclobutanesWe thank the European Research Council (ERC-337776) and MINECO (CTQ2012-35957) for financial support. M. T. and A. P. thank MICINN for RyC and JdC contract

L-Rhamnose induction of Aspergillus nidulans α-L-rhamnosidase genes is glucose repressed via a CreA-independent mechanism acting at the level of inducer uptake

<p>Abstract</p> <p>Background</p> <p>Little is known about the structure and regulation of fungal α-L-rhamnosidase genes despite increasing interest in the biotechnological potential of the enzymes that they encode. Whilst the paradigmatic filamentous fungus <it>Aspergillus nidulans </it>growing on L-rhamnose produces an α-L-rhamnosidase suitable for oenological applications, at least eight genes encoding putative α-L-rhamnosidases have been found in its genome. In the current work we have identified the gene (<it>rhaE</it>) encoding the former activity, and characterization of its expression has revealed a novel regulatory mechanism. A shared pattern of expression has also been observed for a second α-L-rhamnosidase gene, (AN10277/<it>rhaA</it>).</p> <p>Results</p> <p>Amino acid sequence data for the oenological α-L-rhamnosidase were determined using MALDI-TOF mass spectrometry and correspond to the amino acid sequence deduced from AN7151 (<it>rhaE</it>). The cDNA of <it>rhaE </it>was expressed in <it>Saccharomyces cerevisiae </it>and yielded <it>p</it>NP-rhamnohydrolase activity. Phylogenetic analysis has revealed this eukaryotic α-L-rhamnosidase to be the first such enzyme found to be more closely related to bacterial rhamnosidases than other α-L-rhamnosidases of fungal origin. Northern analyses of diverse <it>A. nidulans </it>strains cultivated under different growth conditions indicate that <it>rhaA </it>and <it>rhaE </it>are induced by L-rhamnose and repressed by D-glucose as well as other carbon sources, some of which are considered to be non-repressive growth substrates. Interestingly, the transcriptional repression is independent of the wide domain carbon catabolite repressor CreA. Gene induction and glucose repression of these <it>rha </it>genes correlate with the uptake, or lack of it, of the inducing carbon source L-rhamnose, suggesting a prominent role for inducer exclusion in repression.</p> <p>Conclusions</p> <p>The <it>A. nidulans rhaE </it>gene encodes an α-L-rhamnosidase phylogenetically distant to those described in filamentous fungi, and its expression is regulated by a novel CreA-independent mechanism. The identification of <it>rhaE </it>and the characterization of its regulation will facilitate the design of strategies to overproduce the encoded enzyme - or homologs from other fungi - for industrial applications. Moreover, <it>A. nidulans </it>α-L-rhamnosidase encoding genes could serve as prototypes for fungal genes coding for plant cell wall degrading enzymes regulated by a novel mechanism of CCR.</p

Targeted Community Merging provides an efficient comparison between collaboration clusters and departmental partitions

Community detection theory is vital for the structural analysis of many types of complex networks, especially for human-like collaboration networks. In this work, we present a new community detection algorithm, the Targeted Community Merging algorithm, based on the well-known Girvan–Newman algorithm, which allows obtaining community partitions with high values of modularity and a small number of communities. We then perform an analysis and comparison between the departmental and community structure of scientific collaboration networks within the University of Zaragoza. Thus, we draw valuable conclusions from the inter- and intra-departmental collaboration structure that could be useful to take decisions on an eventual departmental restructuring

Estimation of Fiber Orientations Using Neighborhood Information

Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter. Estimation of fiber orientations (FOs) is a crucial step in the reconstruction process and these estimates can be corrupted by noise. In this paper, a new method called Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is described and shown to reduce the effects of noise and improve FO estimation performance by incorporating spatial consistency. FORNI uses a fixed tensor basis to model the diffusion weighted signals, which has the advantage of providing an explicit relationship between the basis vectors and the FOs. FO spatial coherence is encouraged using weighted l1-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is encouraged using a squared error between the observed and reconstructed diffusion weighted signals. After appropriate weighting of these competing objectives, the resulting objective function is minimized using a block coordinate descent algorithm, and a straightforward parallelization strategy is used to speed up processing. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for both qualitative and quantitative evaluation. The results demonstrate that FORNI improves the quality of FO estimation over other state of the art algorithms.Comment: Journal paper accepted in Medical Image Analysis. 35 pages and 16 figure