Project acronym ANIMETRICS
Project Measurement-Based Modeling and Animation of Complex Mechanical Phenomena
Researcher (PI) Miguel Angel Otaduy Tristan
Host Institution (HI) UNIVERSIDAD REY JUAN CARLOS
Summary Computer animation has traditionally been associated with applications in virtual-reality-based training, video games or feature films. However, interactive animation is gaining relevance in a more general scope, as a tool for early-stage analysis, design and planning in many applications in science and engineering. The user can get quick and visual feedback of the results, and then proceed by refining the experiments or designs. Potential applications include nanodesign, e-commerce or tactile telecommunication, but they also reach as far as, e.g., the analysis of ecological, climate, biological or physiological processes. The application of computer animation is extremely limited in comparison to its potential outreach due to a trade-off between accuracy and computational efficiency. Such trade-off is induced by inherent complexity sources such as nonlinear or anisotropic behaviors, heterogeneous properties, or high dynamic ranges of effects. The Animetrics project proposes a modeling and animation methodology, which consists of a multi-scale decomposition of complex processes, the description of the process at each scale through combination of simple local models, and fitting the parameters of those local models using large amounts of data from example effects. The modeling and animation methodology will be explored on specific problems arising in complex mechanical phenomena, including viscoelasticity of solids and thin shells, multi-body contact, granular and liquid flow, and fracture of solids.
Computer animation has traditionally been associated with applications in virtual-reality-based training, video games or feature films. However, interactive animation is gaining relevance in a more general scope, as a tool for early-stage analysis, design and planning in many applications in science and engineering. The user can get quick and visual feedback of the results, and then proceed by refining the experiments or designs. Potential applications include nanodesign, e-commerce or tactile telecommunication, but they also reach as far as, e.g., the analysis of ecological, climate, biological or physiological processes. The application of computer animation is extremely limited in comparison to its potential outreach due to a trade-off between accuracy and computational efficiency. Such trade-off is induced by inherent complexity sources such as nonlinear or anisotropic behaviors, heterogeneous properties, or high dynamic ranges of effects. The Animetrics project proposes a modeling and animation methodology, which consists of a multi-scale decomposition of complex processes, the description of the process at each scale through combination of simple local models, and fitting the parameters of those local models using large amounts of data from example effects. The modeling and animation methodology will be explored on specific problems arising in complex mechanical phenomena, including viscoelasticity of solids and thin shells, multi-body contact, granular and liquid flow, and fracture of solids.
Project acronym CEAD
Project Contextualizing Evidence for Action on Diabetes in low-resource Settings: A mixed-methods case study in Quito and Esmeraldas, Ecuador.
Researcher (PI) Lucy Anne Parker
Host Institution (HI) UNIVERSIDAD MIGUEL HERNANDEZ DE ELCHE
Summary The relentless rise in diabetes is one of the greatest global health emergencies of the 21st century. The increase is most pronounced in low and middle income countries where today three quarters of people with diabetes live and over 80% of the deaths attributed to non-communicable diseases occur. In light of the wealth of knowledge already available about how to tackle the problem, most major international organizations call for the adoption healthy public policies and initiatives to strengthening health systems. However, implementation of recommended action remains limited in many settings. Most evidence comes from high-income settings and may generate recommendations that cannot be successfully implemented in other settings without careful consideration and contextualization. I propose here that this "know-do" gap can be reduced by revealing the barriers to implementing evidence-based recommendations, engaging local stakeholders in developing context-led innovations and developing a tool-kit for contextualizing and implementing diabetes recommendations in low-resource settings. I plan the research in two carefully selected settings in Ecuador, with mixed-methods combining quantitative epidemiological research and qualitative methodology to generate the rich and varied knowledge that is required to trigger policy action and/or changes in care models. Furthermore, I will engage patients, community members, health workers and decision makers in the process of knowledge generation, interpretation and use. The overarching objective is hence, to explore the process by which global recommendations can be translated into context-specific, evidence-informed action for diabetes prevention in low-resource settings. The findings will support the global endeavour to bridge the global "know-do" gap, one of the most important public health challenges this century and a great opportunity for strengthening health systems and achieving health equity.
The relentless rise in diabetes is one of the greatest global health emergencies of the 21st century. The increase is most pronounced in low and middle income countries where today three quarters of people with diabetes live and over 80% of the deaths attributed to non-communicable diseases occur. In light of the wealth of knowledge already available about how to tackle the problem, most major international organizations call for the adoption healthy public policies and initiatives to strengthening health systems. However, implementation of recommended action remains limited in many settings. Most evidence comes from high-income settings and may generate recommendations that cannot be successfully implemented in other settings without careful consideration and contextualization. I propose here that this "know-do" gap can be reduced by revealing the barriers to implementing evidence-based recommendations, engaging local stakeholders in developing context-led innovations and developing a tool-kit for contextualizing and implementing diabetes recommendations in low-resource settings. I plan the research in two carefully selected settings in Ecuador, with mixed-methods combining quantitative epidemiological research and qualitative methodology to generate the rich and varied knowledge that is required to trigger policy action and/or changes in care models. Furthermore, I will engage patients, community members, health workers and decision makers in the process of knowledge generation, interpretation and use. The overarching objective is hence, to explore the process by which global recommendations can be translated into context-specific, evidence-informed action for diabetes prevention in low-resource settings. The findings will support the global endeavour to bridge the global "know-do" gap, one of the most important public health challenges this century and a great opportunity for strengthening health systems and achieving health equity.
Project acronym CHAMELEON
Project Intuitive editing of visual appearance from real-world datasets
Researcher (PI) Diego Gutierrez Pérez
Host Institution (HI) UNIVERSIDAD DE ZARAGOZA
Summary Computer-generated imagery is now ubiquitous in our society, spanning fields such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the field towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance. Unfortunately, this leads to a series of problems, identified in this proposal: First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real-world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unfit to editing operations, which limits the creative process for scientists, engineers, artists and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the flexibility to edit such data. We believe that the current research path leads to a fragmented space of isolated solutions, each tailored to a particular dataset and problem. We propose a research plan at the theoretical, algorithmic and application levels, putting the user at the core. We will learn key relevant appearance features in terms humans understand, from which intuitive, predictable editing spaces, algorithms, and workflows will be defined. In order to ensure usability and foster creativity, we will also extend our research to efficient simulation of visual appearance, exploiting the extra dimensionality of the captured datasets. Achieving our goals will finally enable us to reach the true potential of real-world captured datasets in many aspects of society.
Computer-generated imagery is now ubiquitous in our society, spanning fields such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the field towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance. Unfortunately, this leads to a series of problems, identified in this proposal: First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real-world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unfit to editing operations, which limits the creative process for scientists, engineers, artists and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the flexibility to edit such data. We believe that the current research path leads to a fragmented space of isolated solutions, each tailored to a particular dataset and problem. We propose a research plan at the theoretical, algorithmic and application levels, putting the user at the core. We will learn key relevant appearance features in terms humans understand, from which intuitive, predictable editing spaces, algorithms, and workflows will be defined. In order to ensure usability and foster creativity, we will also extend our research to efficient simulation of visual appearance, exploiting the extra dimensionality of the captured datasets. Achieving our goals will finally enable us to reach the true potential of real-world captured datasets in many aspects of society.
Project acronym CLOCK
Project CLIMATE ADAPTATION TO SHIFTING STOCKS
Researcher (PI) Elena Ojea
Host Institution (HI) UNIVERSIDAD DE VIGO
Summary Management of marine fisheries is still far from incorporating adaptation to climate change, even though global stocks are heavily overexploited and climate change is adding additional pressure to the resource. In fact, there is growing evidence that current fisheries management systems may no longer be effective under climate change, and this will translate into both ecological and socioeconomic impacts. This research project argues that the combination of fisheries management science and socio-ecological systems thinking is necessary in order to advance in fisheries adaptation to climate change. To this end, the main objectives are set to: 1) Identify and understand the new challenges raised by climate change for current sustainable fisheries management; 2) Develop a novel approach to fisheries adaptation within a socio-ecological framework; 3) Provide empirical evidence on potential solutions for the adaptation of fisheries management systems; and 4) Help introduce fisheries adaptation at the top of the regional and international adaptation policy agendas. To do this, I will combine model and simulation approaches to fisheries with specific case studies where both biophysical and economic variables will be studied an modelled, but also individuals will be given the opportunity to participate in an active way, learning from participatory methods their preferences towards adaptation and the consequences of the new scenarios climate change poses. Three potential case studies are identified for property rights over stocks, property rights over space, and Marine Reserves in two European and one international case study areas. As a result, I expect to develop a new Adaptation Framework for fisheries management that can be scalable, transferable and easily operationalized, and a set of case study examples on how to integrate theory and participatory processes with the aim of increasing social, ecological and institutional resilience to climate change.
Management of marine fisheries is still far from incorporating adaptation to climate change, even though global stocks are heavily overexploited and climate change is adding additional pressure to the resource. In fact, there is growing evidence that current fisheries management systems may no longer be effective under climate change, and this will translate into both ecological and socioeconomic impacts. This research project argues that the combination of fisheries management science and socio-ecological systems thinking is necessary in order to advance in fisheries adaptation to climate change. To this end, the main objectives are set to: 1) Identify and understand the new challenges raised by climate change for current sustainable fisheries management; 2) Develop a novel approach to fisheries adaptation within a socio-ecological framework; 3) Provide empirical evidence on potential solutions for the adaptation of fisheries management systems; and 4) Help introduce fisheries adaptation at the top of the regional and international adaptation policy agendas. To do this, I will combine model and simulation approaches to fisheries with specific case studies where both biophysical and economic variables will be studied an modelled, but also individuals will be given the opportunity to participate in an active way, learning from participatory methods their preferences towards adaptation and the consequences of the new scenarios climate change poses. Three potential case studies are identified for property rights over stocks, property rights over space, and Marine Reserves in two European and one international case study areas. As a result, I expect to develop a new Adaptation Framework for fisheries management that can be scalable, transferable and easily operationalized, and a set of case study examples on how to integrate theory and participatory processes with the aim of increasing social, ecological and institutional resilience to climate change.
Project acronym CoCoUnit
Project CoCoUnit: An Energy-Efficient Processing Unit for Cognitive Computing
Researcher (PI) Antonio Maria Gonzalez Colas
Summary There is a fast-growing interest in extending the capabilities of computing systems to perform human-like tasks in an intelligent way. These technologies are usually referred to as cognitive computing. We envision a next revolution in computing in the forthcoming years that will be driven by deploying many "intelligent" devices around us in all kind of environments (work, entertainment, transportation, health care, etc.) backed up by "intelligent" servers in the cloud. These cognitive computing systems will provide new user experiences by delivering new services or improving the operational efficiency of existing ones, and altogether will enrich our lives and our economy. A key characteristic of cognitive computing systems will be their capability to process in real time large amounts of data coming from audio and vision devices, and other type of sensors. This will demand a very high computing power but at the same time an extremely low energy consumption. This very challenging energy-efficiency requirement is a sine qua non to success not only for mobile and wearable systems, where power dissipation and cost budgets are very low, but also for large data centers where energy consumption is a main component of the total cost of ownership. Current processor architectures (including general-purpose cores and GPUs) are not a good fit for this type of systems since they keep the same basic organization as early computers, which were mainly optimized for "number crunching". CoCoUnit will take a disruptive direction by investigating unconventional architectures that can offer orders of magnitude better efficiency in terms of performance per energy and cost for cognitive computing tasks. The ultimate goal of this project is to devise a novel processing unit that will be integrated with the existing units of a processor (general-purpose cores and GPUs) and altogether will be able to deliver cognitive computing user experiences with extremely high energy-efficiency.
There is a fast-growing interest in extending the capabilities of computing systems to perform human-like tasks in an intelligent way. These technologies are usually referred to as cognitive computing. We envision a next revolution in computing in the forthcoming years that will be driven by deploying many "intelligent" devices around us in all kind of environments (work, entertainment, transportation, health care, etc.) backed up by "intelligent" servers in the cloud. These cognitive computing systems will provide new user experiences by delivering new services or improving the operational efficiency of existing ones, and altogether will enrich our lives and our economy. A key characteristic of cognitive computing systems will be their capability to process in real time large amounts of data coming from audio and vision devices, and other type of sensors. This will demand a very high computing power but at the same time an extremely low energy consumption. This very challenging energy-efficiency requirement is a sine qua non to success not only for mobile and wearable systems, where power dissipation and cost budgets are very low, but also for large data centers where energy consumption is a main component of the total cost of ownership. Current processor architectures (including general-purpose cores and GPUs) are not a good fit for this type of systems since they keep the same basic organization as early computers, which were mainly optimized for "number crunching". CoCoUnit will take a disruptive direction by investigating unconventional architectures that can offer orders of magnitude better efficiency in terms of performance per energy and cost for cognitive computing tasks. The ultimate goal of this project is to devise a novel processing unit that will be integrated with the existing units of a processor (general-purpose cores and GPUs) and altogether will be able to deliver cognitive computing user experiences with extremely high energy-efficiency.
Project acronym DIDONE
Project The Sources of Absolute Music: Mapping Emotions in Eighteenth-Century Italian Opera
Researcher (PI) Álvaro TORRENTE SANCHEZ GUISANDE
Host Institution (HI) UNIVERSIDAD COMPLUTENSE DE MADRID
Summary The belief that 'the end of music is to move human affections' (Descartes, Compendium musicae) has been a central issue in European musical thought since Plato. Opera was invented to recover the power of Ancient music to move the human heart, and its history is a permanent exploration of the capacity of action, words and music to convey emotions. In the eighteenth century a new type of opera consolidated with the chief concern of expressing the character's emotions as they changed throughout the drama, inspired by Descartes' theory of human passions. The key expressive medium was the aria col da capo, where a single, distinct passion was represented, like a concentrated pill of emotional meaning. The ideal corpus to study this issue are the 900 operas set to music by 300 composers on the 27 dramas by Pietro Metastasio (1698-1782). It contains a comprehensive catalogue of emotions in music, a unique window of opportunity to scrutinize conventions that defined music expression and meaning for over a century, paving the way for the emergence of 'absolute' instrumental music, autonomous from any other art form. DIDONE presents an innovative approach to unveil these conventions: the creation of a corpus of 4,000 digitized arias from 200 opera scores based on Metastasio's eight most popular dramas, to be analysed using traditional methods and big data computer technology. The comparative scrutiny of dozens of different musical settings of the same librettos will reveal how composers correlate specific dramatic circumstances and emotions with distinct poetic and musical features. The results will be applicable to three main fields: (i) opera performance; (ii) analysis and interpretation of other types of music; and (iii) composition in several scenarios, from film soundtracks to creation by Artificial Intelligence. An opera festival will be designed to recover and disseminate this hitherto ignored repertoire, which was essential to define the European musical identity.
The belief that 'the end of music is to move human affections' (Descartes, Compendium musicae) has been a central issue in European musical thought since Plato. Opera was invented to recover the power of Ancient music to move the human heart, and its history is a permanent exploration of the capacity of action, words and music to convey emotions. In the eighteenth century a new type of opera consolidated with the chief concern of expressing the character's emotions as they changed throughout the drama, inspired by Descartes' theory of human passions. The key expressive medium was the aria col da capo, where a single, distinct passion was represented, like a concentrated pill of emotional meaning. The ideal corpus to study this issue are the 900 operas set to music by 300 composers on the 27 dramas by Pietro Metastasio (1698-1782). It contains a comprehensive catalogue of emotions in music, a unique window of opportunity to scrutinize conventions that defined music expression and meaning for over a century, paving the way for the emergence of 'absolute' instrumental music, autonomous from any other art form. DIDONE presents an innovative approach to unveil these conventions: the creation of a corpus of 4,000 digitized arias from 200 opera scores based on Metastasio's eight most popular dramas, to be analysed using traditional methods and big data computer technology. The comparative scrutiny of dozens of different musical settings of the same librettos will reveal how composers correlate specific dramatic circumstances and emotions with distinct poetic and musical features. The results will be applicable to three main fields: (i) opera performance; (ii) analysis and interpretation of other types of music; and (iii) composition in several scenarios, from film soundtracks to creation by Artificial Intelligence. An opera festival will be designed to recover and disseminate this hitherto ignored repertoire, which was essential to define the European musical identity.
Project acronym ECHO
Project Extending Coherence for Hardware-Driven Optimizations in Multicore Architectures
Researcher (PI) Alberto ROS BARDISA
Host Institution (HI) UNIVERSIDAD DE MURCIA
Summary Multicore processors are present nowadays in most digital devices, from smartphones to high-performance servers. The increasing computational power of these processors is essential for enabling many important emerging application domains such as big-data, media, medical, or scientific modeling. A fundamental technique to improve performance is speculation, a technique that consists in executing work before it is known if it is actually needed. In hardware, speculation significantly increases energy consumption by performing unnecessary operations, while speculation in software (e.g., compilers) is not the default thus preventing performance optimizations. Since performance in current multicores is limited by their power budget, it is imperative to make multicores as energy-efficient as possible to increase performance even further. In a multicore architecture, the cache coherence protocol is an essential component since its unique but challenging role is to offer a simple and unified view of the memory hierarchy. This project envisions that extending the role of the coherence protocol to simplify other system components will be the key to overcome the performance and energy limitations of current multicores. In particular, ECHO proposes to add simple but effective extensions to the cache coherence protocol in order to (i) reduce and even eliminate misspeculations at the processing cores and synchronization mechanisms and to (ii) enable speculative optimizations at compile time. The goal of this innovative approach is to improve the performance and energy efficiency of future multicore architectures. To accomplish the objectives proposed in this project, I will build on my 14 years expertise in cache coherence, documented in over 40 publications of high impact.
Multicore processors are present nowadays in most digital devices, from smartphones to high-performance servers. The increasing computational power of these processors is essential for enabling many important emerging application domains such as big-data, media, medical, or scientific modeling. A fundamental technique to improve performance is speculation, a technique that consists in executing work before it is known if it is actually needed. In hardware, speculation significantly increases energy consumption by performing unnecessary operations, while speculation in software (e.g., compilers) is not the default thus preventing performance optimizations. Since performance in current multicores is limited by their power budget, it is imperative to make multicores as energy-efficient as possible to increase performance even further. In a multicore architecture, the cache coherence protocol is an essential component since its unique but challenging role is to offer a simple and unified view of the memory hierarchy. This project envisions that extending the role of the coherence protocol to simplify other system components will be the key to overcome the performance and energy limitations of current multicores. In particular, ECHO proposes to add simple but effective extensions to the cache coherence protocol in order to (i) reduce and even eliminate misspeculations at the processing cores and synchronization mechanisms and to (ii) enable speculative optimizations at compile time. The goal of this innovative approach is to improve the performance and energy efficiency of future multicore architectures. To accomplish the objectives proposed in this project, I will build on my 14 years expertise in cache coherence, documented in over 40 publications of high impact.
Project acronym EnvJustice
Project A GLOBAL MOVEMENT FOR ENVIRONMENTAL JUSTICE: The EJAtlas
Researcher (PI) Joan MARTÍNEZ ALIER
Summary "The Environmental Justice Atlas (www.ejatlas.org) is a global database built by us, drawing on activist and academic knowledge. It maps 1500 conflicts. To improve geographical and thematic coverage it will grow to 3000 by 2019. It systematizes conflicts across 100+ fields documenting the commodities at stake, the actors involved, impacts, forms of mobilizations and outcomes allowing analyses that will lead to a general theory of ecological distribution conflicts. We shall research the links between changes in social metabolism and resource extraction conflicts at the "commodity frontiers". Also other questions in political ecology and social movement theory such as the effectiveness of direct action by grassroots protesters compared to institutional forms of contention. Does the involvement of different actors, e.g. indigenous groups, relate to different conflict outcomes? How often does the IUCN ally itself to ""the environmentalism of the poor""? Do mobilizations and outcomes vary across sectors (mining, hydroelectric dams, waste incinerators) according to project differences in economic and biophysical dimensions, environmental and health risks? Are conflicts on point resources (mining, oil extraction) regularly different from conflicts in agriculture? Can we track networked resistances against Western companies, compared to those from China or other countries? Resistance to environmental damage has brought into being many local and some international EJOs pushing for alternative social transformations. We shall study the Vocabulary of Environmental Justice they deploy: climate justice, water justice, food sovereignty, biopiracy, sacrifice zones, and other terms specific to countries: Chinese "cancer villages", Indian "sand mafias", Brazilian "green deserts" (eucalyptus plantations). Finally, are there signs of an alliance between the Global Environmental Justice Movement and the small European movement for "prosperity without growth", décroissance, Post-Wachstum?"
"The Environmental Justice Atlas (www.ejatlas.org) is a global database built by us, drawing on activist and academic knowledge. It maps 1500 conflicts. To improve geographical and thematic coverage it will grow to 3000 by 2019. It systematizes conflicts across 100+ fields documenting the commodities at stake, the actors involved, impacts, forms of mobilizations and outcomes allowing analyses that will lead to a general theory of ecological distribution conflicts. We shall research the links between changes in social metabolism and resource extraction conflicts at the "commodity frontiers". Also other questions in political ecology and social movement theory such as the effectiveness of direct action by grassroots protesters compared to institutional forms of contention. Does the involvement of different actors, e.g. indigenous groups, relate to different conflict outcomes? How often does the IUCN ally itself to ""the environmentalism of the poor""? Do mobilizations and outcomes vary across sectors (mining, hydroelectric dams, waste incinerators) according to project differences in economic and biophysical dimensions, environmental and health risks? Are conflicts on point resources (mining, oil extraction) regularly different from conflicts in agriculture? Can we track networked resistances against Western companies, compared to those from China or other countries? Resistance to environmental damage has brought into being many local and some international EJOs pushing for alternative social transformations. We shall study the Vocabulary of Environmental Justice they deploy: climate justice, water justice, food sovereignty, biopiracy, sacrifice zones, and other terms specific to countries: Chinese "cancer villages", Indian "sand mafias", Brazilian "green deserts" (eucalyptus plantations). Finally, are there signs of an alliance between the Global Environmental Justice Movement and the small European movement for "prosperity without growth", décroissance, Post-Wachstum?"
Project acronym EQUALIZE
Project Equalizing or disequalizing? Opposing socio-demographic determinants of the spatial distribution of welfare.
Researcher (PI) Iñaki Permanyer Ugartemendia
Host Institution (HI) CENTRO DE ESTUDIOS DEMOGRAFICOS
Summary This project aims to investigate the extent to which current trends in family formation, living arrangements and gender-specific education levels are related to the spatial distribution of welfare and the emergence of jobless households in contemporary societies. Inter alia, we aim to explore whether the welfare disequalizing, impoverishing and polarizing effects that are currently associated with recent patterns in assortative mating, lone parenthood and household composition are offset by an unprecedented phenomenon that is sweeping the world during the last decades: the rapid process education expansion in tandem with a reversal of the gender gap in education. The extent to which these two opposing forces occur and which of them is more influential in shaping the distribution of welfare between and within countries is among the main goals of this project. To this end, we will draw upon a variety of household surveys and the world largest sources of census microdata: the Integrated Public Use Microdata Series (IPUMS) project and the Latin American and Caribbean Demographic Centre. Because of their unparalleled geographical coverage and detail, these sources of data constitute exceptional instruments to study socio-demographic phenomena that have been vastly underutilized by the international research community. Triangulating our analysis at the micro, meso and macro levels, we will establish formal linkages between welfare distributions and its socio-demographic correlates to unveil insightful relationships that have been unsatisfactorily explored so far because of the lack of appropriately harmonized, sufficiently detailed and georeferenced datasets. We will strongly emphasize the spatial distribution of variables to unravel local patterns that might take place at highly disaggregated levels, therefore not being discernible to traditional (not as finely-grained) approaches.
This project aims to investigate the extent to which current trends in family formation, living arrangements and gender-specific education levels are related to the spatial distribution of welfare and the emergence of jobless households in contemporary societies. Inter alia, we aim to explore whether the welfare disequalizing, impoverishing and polarizing effects that are currently associated with recent patterns in assortative mating, lone parenthood and household composition are offset by an unprecedented phenomenon that is sweeping the world during the last decades: the rapid process education expansion in tandem with a reversal of the gender gap in education. The extent to which these two opposing forces occur and which of them is more influential in shaping the distribution of welfare between and within countries is among the main goals of this project. To this end, we will draw upon a variety of household surveys and the world largest sources of census microdata: the Integrated Public Use Microdata Series (IPUMS) project and the Latin American and Caribbean Demographic Centre. Because of their unparalleled geographical coverage and detail, these sources of data constitute exceptional instruments to study socio-demographic phenomena that have been vastly underutilized by the international research community. Triangulating our analysis at the micro, meso and macro levels, we will establish formal linkages between welfare distributions and its socio-demographic correlates to unveil insightful relationships that have been unsatisfactorily explored so far because of the lack of appropriately harmonized, sufficiently detailed and georeferenced datasets. We will strongly emphasize the spatial distribution of variables to unravel local patterns that might take place at highly disaggregated levels, therefore not being discernible to traditional (not as finely-grained) approaches.
Project acronym Hi-EST
Project Holistic Integration of Emerging Supercomputing Technologies
Researcher (PI) David Carrera Perez
Host Institution (HI) BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION
Summary Hi-EST aims to address a new class of placement problem, a challenge for computational sciences that consists in mapping workloads on top of hardware resources with the goal to maximise the performance of workloads and the utilization of resources. The objective of the placement problem is to perform a more efficient management of the computing infrastructure by continuously adjusting the number and type of resources allocated to each workload. Placement, in this context, is well known for being NP-hard, and resembles the multi-dimensional knapsack problem. Heuristics have been used in the past for different domains, providing vertical solutions that cannot be generalised. When the workload mix is heterogeneous and the infrastructure hybrid, the problem becomes even more challenging. This is the problem that Hi-EST plans to address. The approach followed will build on top of four research pillars: supervised learning of the placement properties, placement algorithms for tasks, placement algorithms for data, and software defined environments for placement enforcement. Hi-EST plans to advance research frontiers in four different areas: 1) Adaptive Learning Algorithms: by proposing the first known use of Deep Learning techniques for guiding task and data placement decisions; 2) Task Placement: by proposing the first known algorithm to map heterogeneous sets of tasks on top of systems enabled with Active Storage capabilities, and by extending unifying performance models for heterogeneous workloads to cover and unprecedented number of workload types; 3) Data Placement: by proposing the first known algorithm used to map data on top of heterogeneous sets of key/value stores connected to Active Storage technologies; and 4) Software Defined Environments (SDE): by extending SDE description languages with a still inexistent vocabulary to describe Supercomputing workloads that will be leveraged to combine data and task placement into one single decision-making process.
Hi-EST aims to address a new class of placement problem, a challenge for computational sciences that consists in mapping workloads on top of hardware resources with the goal to maximise the performance of workloads and the utilization of resources. The objective of the placement problem is to perform a more efficient management of the computing infrastructure by continuously adjusting the number and type of resources allocated to each workload. Placement, in this context, is well known for being NP-hard, and resembles the multi-dimensional knapsack problem. Heuristics have been used in the past for different domains, providing vertical solutions that cannot be generalised. When the workload mix is heterogeneous and the infrastructure hybrid, the problem becomes even more challenging. This is the problem that Hi-EST plans to address. The approach followed will build on top of four research pillars: supervised learning of the placement properties, placement algorithms for tasks, placement algorithms for data, and software defined environments for placement enforcement. Hi-EST plans to advance research frontiers in four different areas: 1) Adaptive Learning Algorithms: by proposing the first known use of Deep Learning techniques for guiding task and data placement decisions; 2) Task Placement: by proposing the first known algorithm to map heterogeneous sets of tasks on top of systems enabled with Active Storage capabilities, and by extending unifying performance models for heterogeneous workloads to cover and unprecedented number of workload types; 3) Data Placement: by proposing the first known algorithm used to map data on top of heterogeneous sets of key/value stores connected to Active Storage technologies; and 4) Software Defined Environments (SDE): by extending SDE description languages with a still inexistent vocabulary to describe Supercomputing workloads that will be leveraged to combine data and task placement into one single decision-making process.