What Makes a Language Easy to Deep-Learn?

Neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to produce forms for new meanings systematically. However, unlike humans, neural networks notoriously struggle with systematic generalization, and do not necessarily benefit from compositional structure in emergent communication simulations. This poses a problem for using neural networks to simulate human language learning and evolution, and suggests crucial differences in the biases of the different learning systems. Here, we directly test how neural networks compare to humans in learning and generalizing different input languages that vary in their degree of structure. We evaluate the memorization and generalization capabilities of a pre-trained language model GPT-3.5 (analagous to an adult second language learner) and recurrent neural networks trained from scratch (analaogous to a child first language learner). Our results show striking similarities between deep neural networks and adult human learners, with more structured linguistic input leading to more systematic generalization and to better convergence between neural networks and humans. These findings suggest that all the learning systems are sensitive to the structure of languages in similar ways with compositionality being advantageous for learning. Our findings draw a clear prediction regarding children's learning biases, as well as highlight the challenges of automated processing of languages spoken by small communities. Notably, the similarity between humans and machines opens new avenues for research on language learning and evolution.Comment: 32 pages, major update: improved text, added new analyses, added supplementary materia

Assistive Technology and Biomechatronics Engineering

This Special Issue will focus on assistive technology (AT) to address biomechanical and control of movement issues in individuals with impaired health, whether as a result of disability, disease, or injury. All over the world, technologies are developed that make human life richer and more comfortable. However, there are people who are not able to benefit from these technologies. Research can include development of new assistive technology to promote more effective movement, the use of existing technology to assess and treat movement disorders, the use and effectiveness of virtual rehabilitation, or theoretical issues, such as modeling, which underlie the biomechanics or motor control of movement disorders. This Special Issue will also cover Internet of Things (IoT) sensing technology and nursing care robot applications that can be applied to new assistive technologies. IoT includes data, more specifically gathering them efficiently and using them to enable intelligence, control, and new applications

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Computational Study of the Injection Process in Gasoline Direct Injection (GDI) Engines

[ES] La creciente preocupación por los problemas medioambientales, la disponibilidad de combustibles fósiles unido a la gran demanda de vehículos, han llevado a los gobiernos a regular las emisiones emitidas a la atmósfera. Existen propuestas de adoptar fuentes de energía renovables. Sin embargo, la sustitución de los combustibles derivados del petróleo no será fácil, rápida o rentable, y el transporte propulsado por motores de combustión interna (ICE) seguirá destacando en los próximos años. La eficiencia de la combustión y el rendimiento del motor están influenciados por el complejo proceso de inyección. La inyección directa de gasolina (GDI) aumenta el ahorro de combustible y cumple los requisitos de emisiones contaminantes, aunque queda potencial por descubrir. Por ello, ha sido objeto de estudio en los últimos años y, en consecuencia, de la presente Tesis. Este trabajo tiene como motivación mejorar el entendimiento en el campo del GDI. La compleja naturaleza transitoria del proceso de inyección hace que el estudio experimental sea un desafío. La Mecánica de Fluidos Computacional (CFD) surge como una potente alternativa a los experimentos y ha sido adoptada para esta investigación. Bajo este contexto, el objetivo de la presente Tesis es desarrollar una metodología predictiva para la caracterización hidráulica del inyector, capaz de ser aplicada a las actuales y futuras generaciones de inyectores GDI, independientemente de las características del inyector y del software de estudio. Una vez validada, el objetivo posterior es utilizar los resultados para analizar el comportamiento del chorro. Este enfoque busca seguir los pasos de la comunidad científica sustituyendo la práctica experimental. La validación de la metodología se lleva a cabo mediante su aplicación en dos inyectores GDI solenoides multi-orificio diferentes. Además, se han utilizado dos códigos CFD comerciales: CONVERGE y StarCCM+. La metodología predictiva se centra en el estudio del flujo interno y el campo cercano para caracterizar hidráulicamente el inyector. El problema a tratar se define como un sistema multifásico en un marco Euleriano y considerando un único fluido. El tratamiento del flujo multifásico se realiza mediante el enfoque Volume-of-Fluid (VOF). Además, se emplea el Homogeneous Relaxation Model (HRM) para considerar el intercambio de masa entre las fases líquida y vapor debido a cavitación y flash boiling. La turbulencia se ha tratado a partir de los enfoques Reynolds-Averaged Navier-Stokes (RANS) y Large Eddy Simulations (LES). Por otro lado, en cuanto al estudio del flujo externo, se ha adoptado el Discrete Droplet Model (DDM). La atomización y el chorro están influenciados por la geometría de la tobera, por lo que la estrategia de acoplamiento del flujo interno y externo complementa los análisis. Se han adoptado enfoques de acoplamiento unidireccional y mapeado, utilizando como parámetros de entrada los datos de flujo interno de la validada metodología. Esta Tesis aporta una nueva y valiosa metodología predictiva con una elevada precisión a la hora de caracterizar el proceso de inyección en comparativa con datos experimentales. Por otro lado, es directamente trasferible a distintos códigos de cálculo así como aplicable a inyectores con características dispares sin perjudicar las exigencias del modelo. La correcta caracterización del flujo interno ha permitido emplear los datos obtenidos para analizar el comportamiento del chorro eliminando la necesidad de usar datos experimentales. Los resultados obtenidos capturan el comportamiento macroscópico del chorro con una precisión comparable a los experimentos. Aunque todavía hay muchos retos que afrontar, la presente Tesis supone un gran avance en el campo del GDI. El remarcable progreso se debe al desarrollo y uso de una metodología totalmente predictiva, que permite prescindir de la mayoría de los experimentos para contribuir a una mayor y más amplia visión de la física del proceso de inyección.[CA] La creixent preocupació pels problemes ambientals, la limitada disponibilitat de combustibles fòssils, acompanyat a la gran demanda de vehicles, ha portat el govern a regular els nivells d'emissions emesos a l'atmosfera. Existeixen propostes d'adoptar fonts d'energia renovables. Tanmateix, la substitució dels combustibles líquids derivats del petroli no es durà a terme de forma fàcil, ràpida o rentable, i el transport propulsat per motors de combustió interna (ICE) continuarà destacant en els pròxims anys. L'eficiència de la combustió i el rendiment del motor són fortament influenciats pel complex procés d'injecció. La injecció directa de gasolina (GDI) augmenta l'estalvi de combustible i complix amb els requisits d'emissions, encara que queda molt potencial per descobrir. Per això, aquest ha sigut objecte d'investigació en els últims anys i, com a conseqüència, d'aquesta Tesi. Aquest treball té com a motivació millorar l'enteniment en el camp del GDI. La complexa natura transitòria de la injecció fa que l'estudi experimental siga força complex. La Mecànica de Fluids Computacional (CFD) sorgeix com una potent alternativa als experiments, i ha sigut adoptada per aquesta investigació. Baix aquest mateix context, es proposa com a objectiu principal d'aquesta Tesi el desenvolupament d'una metodologia predictiva per a la caracterització hidràulica de l'injector, capaç de ser aplicada a les actuals i futures generacions d'injectors GDI (independentment de les característiques de l'injector i del software d'estudi). Una vegada validada, el posterior objectiu és analitzar el comportament de l'esprai. Aquest enfocament busca seguir els passos de la comunitat científica substituint la pràctica experimental. La validació de la metodologia ha sigut duta a terme mitjançant la seva aplicació en dos injectors GDI solenoides multi-orifici. A més, s'han utilitzat dos software CFD comercials: CONVERGE i StarCCM+. La metodologia predictiva se centra en l'estudi del flux intern i el camp proper per tal de caracteritzar hidràulicament l'injector. El problema a tractar es defineix en base a un sistema multi-fàsic en un marc Eulerià i considerant un únic fluid. El tractament del fluid multi-fàsic es realitza mitjançant l'aproximació Volume-of-Fluid (VOF). A més, s'utilitza el Homogeneous Relaxation Model (HRM) per tal de considerar l'intercambi de massa entre les fases líquida i vapor degut als fenòmens de cavitació i flash boiling. La turbulència s'ha tractac a través dels enfocaments Reynolds-Averaged Navier-Stokes (RANS) i Large Eddy Simulations (LES). Pel que fa a l'estudi del fluix extern, s'ha adoptat el Discrete Droplet Model (DDM). Sent conscients que el comportament l'atomització i l'esprai estan influenciats per la geometria de la tovera, l'estratègia d'acoblament del flux intern i extern complementa les anàlisis. S'han adoptat els enfocaments d'acoblament unidireccional i mapejat, utilitzant com a paràmetres d'entrada les dades del flux intern obtingudes amb la validada metodologia. Aquesta Tesi aporta una nova i valuosa metodologia predictiva amb una elevada precisió a l'hora de caracteritzar el procés d'injecció en comparativa amb dades experimentals. És directament transferible a diversos codis de càlcul així com aplicable a injectors amb característiques dispars sense perjudicar les exigències del model. La correcta caracterització del flux intern ha permès utilitzar les dades obtingudes per tal d'analitzar el comportament de l'esprai, eliminant la necessitat d'emprar dades experimentals. Els resultats obtinguts d'aquest estudi capturen el comportament macroscòpic de l'esprai amb una precisió comparable als experiments. Encara que queden molts reptes per afrontar, aquesta Tesi aporta un important avanç al camp del GDI. La ruptura prové del desenvolupament i ús d'una metodologia completament predictiva, que substitueix els experiments requerits i així contribueix a una millor i més ampla visió de la física del procés d'injecció.[EN] Concerns about climate change, availability of fuel resources and the high demand for vehicles, have led governments to regulate the level of pollution emitted by engines into the atmosphere. There is a strong desire to adopt renewable and sustainable energy sources. However, the substitution of liquid fuels derived from petroleum will not emerge easily, quickly or economically, and Internal Combustion Engines (ICE) will continue to excel for the next few years. Combustion efficiency and engine performance are strongly influenced by the complex fuel injection process. Gasoline Direct Injection (GDI) strategies increase fuel economy and meet emission requirements, although many challenges remain, which has therefore been one of the main research objectives in recent years and of this Thesis. The present research aims to provide a better understanding in the field of GDI. The transient and complex nature of the injection process makes the experimental study of GDI quite challenging. Therefore, Computational Fluid Dynamics (CFD) emerges as a powerful alternative adopted for this research. In this context, the main objective of the present Thesis is to develop a predictive methodology capable of being applied to current and future generations of GDI injectors, regardless of the injector features and the software employed, for the hydraulic characterization of the injector. Once validated, the subsequent goal is to employ the obtained results to analyze the behavior of the spray downstream of the injector. The approach attempts to follow the footsteps of the research community to avoid experimental practice. The predictive methodology has been validated through its application to two multi-hole solenoid GDI injectors with different features. In addition, the mentioned methodology has been evaluated using diverse commercial software: CONVERGE and StarCCM+. The methodology focuses on the study of the internal and near-field flow to hydraulically characterize the injector. So the problem to be addressed is a multi-phase system, performed in an Eulerian framework, modeled through a single-fluid approach. The multi-phase flow is treated by means of the Volume-of-Fluid (VOF) approach. Homogeneous Relaxation Model (HRM) is employed to consider the mass exchange between liquid and vapor fuel phases, due to cavitation and flash boiling. The turbulence treatment has been performed from both Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulations (LES) approaches. Regarding the external flow study, the Discrete Droplet Model (DDM) has been adopted. In addition, being aware that atomization and spray behavior is greatly influenced by the nozzle geometry, the coupling strategy of the internal and external flow complements the analyses. One-way coupling and mapping approaches have been adopted, using as input parameters the internal flow data obtained from the already validated methodology. Accordingly, this Thesis provides a new and valuable predictive methodology, which has demonstrated a high accuracy in characterizing the flow behavior during the injection process through comparison with experimental data. It has also proven to be directly transferable to different CFD software and applicable to injectors with dissimilar characteristics without compromising the requirements of the model. The correct internal flow characterization has made it possible to employ the obtained data to analyze the spray patterns, which eliminates the need to consider experimental data. The outcomes of this study macroscopically capture the jet behavior with an accuracy comparable to experiments under different operating conditions. Although there are still many challenges to face, the present Thesis brings a breakthrough in the field of GDI. The quantum leap arises from the development and use of a fully predictive methodology, allowing to avoid most experiments to contribute to a greater and broader vision of the injection process physics.María Martínez García has been founded through a grant from the Government of Generalitat Valenciana with reference ACIF/2018/118 and financial support from the European Union. These same institutions, Government of Generalitat Valenciana and the European Union, supported through a grant for pre-doctoral stays out of the Comunitat Valenciana with reference BEFPI/2020/057 the research carried out during the stay at Aerothermochemistry and Combustion Systems Laboratory, Swiss Federal Institute of Technology, ETH Zurich, Switzerland. Special gratitude from the author to both institutions, Government of Generalitat Valenciana and the European Union, for making this dream possibleMartínez García, M. (2022). Computational Study of the Injection Process in Gasoline Direct Injection (GDI) Engines [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185180TESI

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc