Examining the Impact of Innovation Forms on Sustainable Economic Performance: The Influence of Family Management

The aim of this research is to explore the effect that innovation, as a potential source of sustained competitive advantage and firm growth, has on the achievement of sustainable economic performance. In particular, this paper empirically examines the influence of four innovation forms (intramural R&D, extramural R&D, product innovation, and process innovation) on firms’ sustainable economic performance, considering the moderating effect of family involvement in management. To test the hypotheses, random-effects regression analyses are applied to a longitudinal sample of 598 Spanish private manufacturing firms throughout the 2006–2015 period. The results show a negative effect of intramural and extramural R&D on sustainable economic performance and a positive effect of process innovation on sustainable economic performance. Moreover, a reinforced relationship between process innovation and sustainable economic performance is also revealed when family involvement in management acts as a moderator. The findings make several contributions to research and practice

Learning with con gurable operators and RL-based heuristics

In this paper, we push forward the idea of machine learning systems for which the operators can be modi ed and netuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators a ect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is de ned as a choice of operator and rule. As a result, the architecture can be seen as a `system for writing machine learning systems' or to explore new operators.This work was supported by the MEC projects CONSOLIDER-INGENIO 26706 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, and the REFRAME project granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Econom´ıa y Competitividad in Spain. Also, F. Mart´ınez-Plumed is supported by FPI-ME grant BES-2011-045099Martínez Plumed, F.; Ferri Ramírez, C.; Hernández Orallo, J.; Ramírez Quintana, MJ. (2013). Learning with con gurable operators and RL-based heuristics. En New Frontiers in Mining Complex Patterns. Springer Verlag (Germany). 7765:1-16. https://doi.org/10.1007/978-3-642-37382-4_1S1167765Armstrong, J.: A history of erlang. In: Proceedings of the Third ACM SIGPLAN Conf. on History of Programming Languages, HOPL III, pp. 1–26. ACM (2007)Brazdil, P., Giraud-Carrier: Metalearning: Concepts and systems. In: Metalearning. Cognitive Technologies, pp. 1–10. Springer, Heidelberg (2009)Daumé III, H., Langford, J.: Search-based structured prediction (2009)Dietterich, T., Domingos, P., Getoor, L., Muggleton, S., Tadepalli, P.: Structured machine learning: the next ten years. Machine Learning 73, 3–23 (2008)Dietterich, T.G., Lathrop, R., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89, 31–71 (1997)Džeroski, S.: Towards a general framework for data mining. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 259–300. Springer, Heidelberg (2007)Dzeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Machine Learning 43, 7–52 (2001), 10.1023/A:1007694015589Dzeroski, S., Lavrac, N. (eds.): Relational Data Mining. Springer (2001)Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Similarity functions for structured data. an application to decision trees. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 10(29), 109–121 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Web categorisation using distance-based decision trees. ENTCS 157(2), 35–40 (2006)Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Bridging the Gap between Distance and Generalisation. Computational Intelligence (2012)Ferri-Ramírez, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Incremental learning of functional logic programs. In: Kuchen, H., Ueda, K. (eds.) FLOPS 2001. LNCS, vol. 2024, pp. 233–247. Springer, Heidelberg (2001)Gärtner, T.: Kernels for Structured Data. PhD thesis, Universitat Bonn (2005)Holland, J.H., Booker, L.B., Colombetti, M., Dorigo, M., Goldberg, D.E., Forrest, S., Riolo, R.L., Smith, R.E., Lanzi, P.L., Stolzmann, W., Wilson, S.W.: What is a learning classifier system? In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 1999. LNCS (LNAI), vol. 1813, pp. 3–32. Springer, Heidelberg (2000)Holmes, J.H., Lanzi, P., Stolzmann, W.: Learning classifier systems: New models, successful applications. Information Processing Letters (2002)Kitzelmann, E.: Inductive programming: A survey of program synthesis techniques. In: Schmid, U., Kitzelmann, E., Plasmeijer, R. (eds.) AAIP 2009. LNCS, vol. 5812, pp. 50–73. Springer, Heidelberg (2010)Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997, pp. 170–178. Morgan Kaufmann Publishers Inc., San Francisco (1997)Lafferty, J., McCallum, A.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001, pp. 282–289 (2001)Lloyd, J.W.: Knowledge representation, computation, and learning in higher-order logic (2001)Maes, F., Denoyer, L., Gallinari, P.: Structured prediction with reinforcement learning. Machine Learning Journal 77(2-3), 271–301 (2009)Martínez-Plumed, F., Estruch, V., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J.: Newton trees. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 174–183. Springer, Heidelberg (2010)Muggleton, S.: Inverse entailment and Progol. New Generation Computing (1995)Muggleton, S.H.: Inductive logic programming: Issues, results, and the challenge of learning language in logic. Artificial Intelligence 114(1-2), 283–296 (1999)Plotkin, G.: A note on inductive generalization. Machine Intelligence 5 (1970)Schmidhuber, J.: Optimal ordered problem solver. Maching Learning 54(3), 211–254 (2004)Srinivasan, A.: The Aleph Manual (2004)Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (1998)Tadepalli, P., Givan, R., Driessens, K.: Relational reinforcement learning: An overview. In: Proc. of the Workshop on Relational Reinforcement Learning (2004)Tamaddoni-Nezhad, A., Muggleton, S.: A genetic algorithms approach to ILP. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 285–300. Springer, Heidelberg (2003)Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML (2004)Wallace, C.S., Dowe, D.L.: Refinements of MDL and MML coding. Comput. J. 42(4), 330–337 (1999)Watkins, C., Dayan, P.: Q-learning. Machine Learning 8, 279–292 (1992

Propuesta arquitectónica habitacional de condición social evolutiva para el sector urbano en el municipio de San Miguel

Proponer una alternativa habitacional que genere un proceso evolutivo en beneficio al desarrollo de la clase social media-baja en el sector urbano del municipio de San Miguel. El método de investigación a implementar en este proceso es el Método Descriptivo, este permitirá analizar los datos reunidos para descubrir los indicadores que estén relacionados entre sí, generando una respuesta viable para la realización de la propuesta final. Tras finalizar este documento de investigación, se confirma concepto de la importancia de haber elegido la problemática de vivienda social, ya que se ha podido realizar un diseño integral que cumple con los objetivos y se proponen nuevas formas de abordar la vivienda social. Para avalara la propuesta de diseño se ha desarrollado un amplio proceso conformado en cinco etapas en las que se aborda el tema evolutivo desde varias perspectivas. Proponer una vivienda social evolutiva para el futuro ayudaría en gran medida a la población del sector urbano del municipio de San Miguel ya que posee muchas ventajas como el aprovechar al máximo el área del terreno, la utilización del mismo sistema constructivo popular, la variedad de familia y otras más. Es por esto se tuvo la iniciativa de incorporar al glosario arquitectónico un nuevo concepto como lo es la vivienda social evolutiv

A computational analysis of general intelligence tests for evaluating cognitive development

[EN] The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the fluid or crystallised intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven s Progressive Matrices and Thurstone s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive perational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.This work has been partially supported by the EU (FEDER) and the Spanish MINECO under grants TIN 2015-69175-C4-1-R and TIN 2013-45732-C4-1-P, and by Generalitat Valenciana under grant PROMETEOII/2015/013.Martínez-Plumed, F.; Ferri Ramírez, C.; Hernández-Orallo, J.; Ramírez Quintana, MJ. (2017). A computational analysis of general intelligence tests for evaluating cognitive development. Cognitive Systems Research. 43:100-118. https://doi.org/10.1016/j.cogsys.2017.01.006S1001184

Proposal of new trace elements classification to be used in nutrition, oligotherapy and other therapeutics strategies

Objetivos: 1) Proponer una nueva clasificación de los oligoelementos fundamentada en el estudio detallado de las investigaciones más recientes sobre los mismos; 2) ofrecer información detallada y actualizada sobre todos los oligoelementos. Resultados: el análisis de todos los resultados de investigación consultados pone de manifiesto que los avances en las técnicas de análisis molecular permiten dilucidar la importancia que presentan ciertos oligoelementos para la salud humana. Se ofrece un análisis detallado de la función catalítica que podrían tener determinados elementos no considerados hasta ahora como esenciales o posiblemente esenciales, gracias al uso de plataformas informáticas que permiten el análisis integrado de datos sobre enzimas. Asimismo se presenta información integrada y actualizada del papel fisiológico, cinéticas y metabolismo, fuentes dietéticas y factores que propician la carencia o la toxicidad de cada uno de los oligoelementos. Conclusiones: La Oligoterapia plantea el uso de oligoelementos catalíticamente activos con fines terapéuticos. La nueva clasificación de oligoelementos planteada en este trabajo será de interés para diversos sectores profesionales: médicos y demás personal sanitario, nutricionistas, farmacéuticos, etc. Así podrán diseñarse nuevas estrategias terapéuticas que permitan paliar la sintomatología de diversas patologías, en particular las enfermedades carenciales y metabólicas.Objectives: 1) to propose a new classification of the trace elements based on a study of the recently reported research; 2) to offer detailed and actualized information about trace elements. Results: the analysis of the research results recently reported reveals that the advances of the molecular analysis techniques point out the importance of certain trace elements in human health. A detailed analysis of the catalytic function related to several elements not considered essential o probably essentials up to now is also offered. To perform the integral analysis of the enzymes containing trace elements informatics tools have been used. Actualized information about physiological role, kinetics, metabolism, dietetic sources and factors promoting trace elements scarcity or toxicity is also presented. Results: Oligotherapy uses catalytic active trace elements with therapeutic proposals. The new trace element classification here presented will be of high interest for different professional sectors: doctors and other professions related to medicine; nutritionist, pharmaceutics, etc. Using this new classification and approaches, new therapeutic strategies could be designed to mitigate symptomatology related to several pathologies, particularly carential and metabolic diseases

FVF-Based Low-Dropout Voltage Regulator with Fast Charging/Discharging Paths for Fast Line and Load Regulation

A new internally compensated low drop-out voltage regulator based on the cascoded flipped voltage follower is presented in this paper. Adaptive biasing current and fast charging/discharging paths have been added to rapidly charge and discharge the parasitic capacitance of the pass transistor gate, thus improving the transient response. The proposed regulator was designed with standard 65-nm CMOS technology. Measurements show load and line regulations of 433.80 μV/mA and 5.61 mV/V, respectively. Furthermore, the output voltage spikes are kept under 76 mV for 0.1 mA to 100 mA load variations and 0.9 V to 1.2 V line variations with rise and fall times of 1 μs. The total current consumption is 17.88 μA (for a 0.9 V supply voltage).Ministerio de Economía y Competitividad TEC2015-71072-C3-3-RConsejería de Economía, Innovación y Ciencia. Junta de Andalucía P12-TIC-186

Can language models automate data wrangling?

[EN] The automation of data science and other data manipulation processes depend on the integration and formatting of 'messy' data. Data wrangling is an umbrella term for these tedious and time-consuming tasks. Tasks such as transforming dates, units or names expressed in different formats have been challenging for machine learning because (1) users expect to solve them with short cues or few examples, and (2) the problems depend heavily on domain knowledge. Interestingly, large language models today (1) can infer from very few examples or even a short clue in natural language, and (2) can integrate vast amounts of domain knowledge. It is then an important research question to analyse whether language models are a promising approach for data wrangling, especially as their capabilities continue growing. In this paper we apply different variants of the language model Generative Pre-trained Transformer (GPT) to five batteries covering a wide range of data wrangling problems. We compare the effect of prompts and few-shot regimes on their results and how they compare with specialised data wrangling systems and other tools. Our major finding is that they appear as a powerful tool for a wide range of data wrangling tasks. We provide some guidelines about how they can be integrated into data processing pipelines, provided the users can take advantage of their flexibility and the diversity of tasks to be addressed. However, reliability is still an important issue to overcome.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was funded by the Future of Life Institute, FLI, under grant RFP2-152, the MIT-Spain - INDITEX Sustainability Seed Fund under project COST-OMIZE, the EU (FEDER) and Spanish MINECO under RTI2018-094403-B-C32 and PID2021-122830OB-C42, Generalitat Valenciana under PROMETEO/2019/098 and INNEST/2021/317, EU's Horizon 2020 research and innovation programme under grant agreement No. 952215 (TAILOR) and US DARPA HR00112120007 ReCOG-AI. AcknowledgementsWe thank Lidia Contreras for her help with the Data Wrangling Dataset Repository. We thank the anonymous reviewers from ECMLPKDD Workshop on Automating Data Science (ADS2021) and the anonymous reviewers of this special issue for their comments.Jaimovitch-López, G.; Ferri Ramírez, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez Quintana, MJ. (2023). Can language models automate data wrangling?. Machine Learning. 112(6):2053-2082. https://doi.org/10.1007/s10994-022-06259-920532082112