Suchergebnisse

Las políticas económicas requieren ser evaluadas para respaldar aciertos o para revertir errores. En México se implementó una legislación que flexibilizó el mercado laboral y ha pasado tiempo suficiente para evaluar qué efecto salarial tuvo. El objetivo del artículo es estimar el impacto de la legislación laboral en la brecha salarialentre trabajadores (permanentes y temporales) con contrato escrito y trabajadores sin él. Para tal efecto, se empleó el método de diferencias en diferencias (DID, por sus siglas en inglés) para detectar el impacto causal de la legislación laboral. Los resultados se interpretan en una vertiente reconocida por Jaime Ros (Márquezy Ros, 1990; Ros, 2004 y 2019), ....

En este artículo se lleva a cabo la conceptualización teórica y la estimación de la elasticidad producto del empleo industrial para México en el periodo 1987-2001. Adicionalmente, se incorpora la variable de prestaciones sociales que contribuye a explicar los cambios en las magnitudes de la elasticidad producto del empleo. En el análisis también se incluyen coeficientes de correlación parcial. El resultado principal muestra que el crecimiento de la producción, en años recientes, tiende a generar menos empleos, lo cual se refleja en un menor valor de la elasticidad. Sin embargo, la alta correlación entre el empleo y las prestaciones sociales sugiere que éstas sirven como incentivo para incorporar a los trabajadores al empleo formal.

Se analizan los proveedores especializados en México, en el periodo 1990-2004. Conceptualmente, el sector tecnológico es una fuente de innovaciones para las industrias de una economía. Un determinante crucial de los cambios en proveedores especializados es la fuerte presencia del sector externo, mediante las importaciones de maquinaria y equipo, la alta participación de maquiladoras en las exportaciones y la baja cobertura de la balanza de pagos tecnológica. Al interior del conjunto de dichos proveedores se encuentran distintos patrones tecnológicos y productivos, perfiles que se validan estadísticamente. Los rubros de distinción son la propensión a exportar y el esfuerzo tecnológico, encontrándose que sólo las industrias con baja propensión a exportar han aumentado la proporción de gasto en transferencia tecnológica y se ha incrementado el coeficiente de valor agregado a ventas.

Las habilidades laborales y la especialización productiva pueden crear una dinámica económica favorable. A nivel subnacional, pueden observarse distintas dinámicas de crecimiento provocadas por una causación circular acumulativa;. pPara estimar la relación entre la reconfiguración de las habilidades laborales, la migración laboral y el desempeño regional, se utilizaóron el censo de población y la encuesta de empleo de México. Se encontró que (1) la migración laboral hacia las entidades con aglomeraciones promovía una mayor creación de empleos y (2) que las regiones expulsoras de población registraban una menor creación de empleo, principalmente, en empleos altamente calificados.

En el presente artículo se empleó un índice de accesibilidad de interacción espacial para evaluar los efectos que sobre la cohesión territorial tendría el añadir un servicio de alta velocidad ferroviaria a los medios de transporte existentes. Se estudió la ruta desde Guadalajara al Puerto de Veracruz pasando por la Ciudad de México y otras ciudades importantes. Se evaluó el incremento de la cohesión territorial entre regiones en base al aumento de la productividad que tendría cada par de ciudades en donde se incluiría el tren de alta velocidad de pasajeros. La cohesión territorial se estimó evaluando la demanda de pasajeros que tendría el tren de alta velocidad, el índice de accesibilidad territorial y usando una función de producción agregada. Se halló evidencia de que el tren de alta de velocidad estaría asociado con mayores tasas de intercambio económico regional y consecuentemente con un mayor nivel de productividad.

[EN] Optical Music Recognition is the technology that allows computers to read music notation, which is also referred to as Handwritten Music Recognition when it is applied over handwritten notation. This technology aims at efficiently transcribing written music into a representation that can be further processed by a computer. This is of special interest to transcribe the large amount of music written in early notations, such as the Mensural notation, since they represent largely unexplored heritage for the musicological community. Traditional approaches to this problem are based on complex strategies with many explicit rules that only work for one particular type of manuscript. Machine learning approaches offer the promise of generalizable solutions, based on learning from just labelled examples. However, previous research has not achieved sufficiently acceptable results for handwritten Mensural notation. In this work we propose the use of deep neural networks, namely convolutional recurrent neural networks, which have proved effective in other similar domains such as handwritten text recognition. Our experimental results achieve, for the first time, recognition results that can be considered effective for transcribing handwritten Mensural notation, decreasing the symbol-level error rate of previous approaches from 25.7% to 7.0%. (C) 2019 Elsevier B.V. All rights reserved. ; First author thanks the support from the Spanish Ministry "HISPAMUS" project (TIN2017-86576-R), partially funded by the EU. The other authors were supported by the European Union's H2020 grant "Recognition and Enrichment of Archival Documents" (Ref. 674943), by the BBVA Foundacion through the 2017-2018 and 2018-2019 Digital Humanities research grants "Carabela" and "HistWeather - Dos Siglos de Datos Cilmaticos", and by EU JPICH project "HOME - History Of Medieval Europe"(Spanish PEICTI Ref. PCI2018-093122). ; Calvo-Zaragoza, J.; Toselli, AH.; Vidal, E. (2019). Handwritten Music Recognition for Mensural notation with convolutional recurrent ...

Optical Music Recognition is the technology that allows computers to read music notation, which is also referred to as Handwritten Music Recognition when it is applied over handwritten notation. This technology aims at efficiently transcribing written music into a representation that can be further processed by a computer. This is of special interest to transcribe the large amount of music written in early notations, such as the Mensural notation, since they represent largely unexplored heritage for the musicological community. Traditional approaches to this problem are based on complex strategies with many explicit rules that only work for one particular type of manuscript. Machine learning approaches offer the promise of generalizable solutions, based on learning from just labelled examples. However, previous research has not achieved sufficiently acceptable results for handwritten Mensural notation. In this work we propose the use of deep neural networks, namely convolutional recurrent neural networks, which have proved effective in other similar domains such as handwritten text recognition. Our experimental results achieve, for the first time, recognition results that can be considered effective for transcribing handwritten Mensural notation, decreasing the symbol-level error rate of previous approaches from 25.7% to 7.0%. ; First author thanks the support from the Spanish Ministry "HISPAMUS"project (TIN2017-86576-R), partially funded by the EU. The other authors were supported by the European Union 's H2020 grant "Recognition and Enrichment of Archival Documents"(Ref. 674943 ), by the BBVA Foundacion through the 2017–2018 and 2018–2019 Digital Humanities research grants "Carabela"and "HistWeather –Dos Siglos de Datos Cilmáticos", and by EU JPICH project "HOME - History Of Medieval Europe"(Spanish PEICTI Ref. PCI2018-093122).

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; [EN] Existing transcripts for historic manuscripts are a very valuable resource for training models useful for automatic recognition, aided transcription, and/or indexing of the remaining untranscribed parts of these collections. However, these existing transcripts generally exhibit two main problems which hinder their convenience: a) text of the transcripts is seldom aligned with manuscript lines, and b) text often deviate very significantly from what can be seen in the manuscript, either because writing style has been modernized or abbreviations have been expanded, or both. This work presents an analysis of these problems and discusses possible solutions for minimizing human effort needed to adapt existing transcripts in order to render them usable. Empirical results presented show the huge performance gain that can be obtained by adequately adapting the transcripts, thus motivating future development of the proposed solutions. ; We are very grateful to Carlos Lechner and Celio Hernández who helped in the creation of the ground truth of the Alcaraz dataset. This work has been partially supported by the European Union (EU) Horizon 2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943), EU project HIMANIS (JPICH programme, Spanish grant Ref: PCIN-2015-068) and MINECO/FEDER, UE under project TIN2015-70924-C2-1-R. ; Villegas, M.; Toselli, AH.; Romero Gómez, V.; Vidal, E. (2016). Exploiting Existing Modern Transcripts for Historical Handwritten Text Recognition. IEEE. https://doi.org/10.1109/ICFHR.2016.22 ; S

[EN] This paper presents a novel application of pattern recognition to the provenance classification of archae- ological ceramics. This is a challenging problem for archaeologists, which involves assigning a making location to a fragment of archaeological pottery that was found along with other fragments of pieces made in different distant locations from the find. The pieces look very similar to each other and, often, other contextual information about the use of the pieces cannot be used due to the small size of the fragments. Current standard methods to solve this problem are limited since they are time consuming, require costly equipment, and can lead to the destruction of a part of the pieces. The proposed method overcome those limitations using non-destructive ultrasonic testing and incorporates versatile data anal- ysis through advanced pattern recognition techniques. Those techniques include the following: feature ranking, sample augmentation, semi-supervision based on active learning; and optimal fusion. This latter is based in the concept of alpha integration, which allows optimal fitting of the fusion model parameters. Different provenance classification problems are showcased: provenance classification of terra sigillata ce- ramic pieces from Aretina, Northern Italy and Sud-Gaul origins; and provenance classification of Iberian ceramic pieces from archaeological sites of Paterna, and Les Jovaes in Valencia, Spain. We demonstrate that the proposed fusion-based method achieves the best results, in terms of balanced classification ac- curacy and F1 score, compared with competitive methods like linear discriminant analysis, random forest, and support vector machine. Experiments for simulating small sample sizes and uncertainty in labeling of the pieces are included. In addition, the paper provides a design of a practical specialized device that could be used in different applications of archaeological ceramic classification. ; This work was supported by Spanish Administration and European Union under ...

Chromosome conformation capture techniques, such as Hi-C, are fundamental in characterizing genome organization. These methods have revealed several genomic features, such as chromatin loops, whose disruption can have dramatic effects in gene regulation. Unfortunately, their detection is difficult; current methods require that the users choose the resolution of interaction maps based on dataset quality and sequencing depth. Here, we introduce Binless, a resolution-agnostic method that adapts to the quality and quantity of available data, to detect both interactions and differences. Binless relies on an alternate representation of Hi-C data, which leads to a more detailed classification of paired-end reads. Using a large-scale benchmark, we demonstrate that Binless is able to call interactions with higher reproducibility than other existing methods. Binless, which is freely available, can thus reliably be used to identify chromatin loops as well as for differential analysis of chromatin interaction maps. ; This work has been partially supported by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Synergy grant agreement 609989 (4Dgenome), the European Union's Horizon 2020 research and innovation programme (agreement 676556) as well as the Spanish MINECO (BFU2017-85926-P). We acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme / Generalitat de Catalunya. We also acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) through the Instituto de Salud Carlos III, the Generalitat de Catalunya through Departament de Salut and Departament d'Empresa i Coneixement and the Co-financing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth Operating Program

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; [EN] This paper describes the Handwritten Text Recognition (HTR) competition on the READ dataset that has been held in the context of the International Conference on Frontiers in Handwriting Recognition 2016. This competition aims to bring together researchers working on off-line HTR and provide them a suitable benchmark to compare their techniques on the task of transcribing typical historical handwritten documents. Two tracks with different conditions on the use of training data were proposed. Ten research groups registered in the competition but finally five submitted results. The handwritten images for this competition were drawn from the German document Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805, used in the READ project. The selected dataset is written by several hands and entails significant variabilities and difficulties. The five participants achieved good results with transcriptions word error rates ranging from 21% to 47% and character error rates rating from 5% to 19%. ; This work has been partially supported through the European Union's H2020 grant READ (Recognition and Enrichment of Archival Documents) (Ref: 674943), and the MINECO/FEDER UE project TIN2015-70924-C2-1-R. ; Sánchez Peiró, JA.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2016). ICFHR2016 Competition on Handwritten Text Recognition on the READ Dataset. IEEE. https://doi.org/10.1109/ICFHR.2016.0120 ; S

"The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-21257-4_85 ; To date, automatic handwriting text recognition systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. As an alternative, an interactive framework that integrates the human knowledge into the transcription process has been presented in previous works. In this work, multimodal interaction at character-level is studied. Until now, multimodal interaction had been studied only at whole-word level. However, character-level pen-stroke interactions may lead to more ergonomic and friendly interfaces. Empirical tests show that this approach can save significant amounts of user effort with respect to both fully manual transcription and non-interactive post-editing correction. ; Work supported by the Spanish Government (MICINN and "Plan E") under the MITTRAL (TIN2009-14633-C03-01) research project and under the research programme Consolider Ingenio 2010: MIPRCV (CSD2007-00018), and by the Generalitat Valenciana under grant Prometeo/2009/014. ; Martín-Albo Simón, D.; Romero Gómez, V.; Toselli ., AH.; Vidal, E. (2011). Character-level interaction in multimodal computer-assisted transcription of text images. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 684-691. https://doi.org/10.1007/978-3-642-21257-4 ; S ; 684 ; 691