Project acronym 3DV
Project Sensor for 3D Vision
Researcher (PI) Alberto BROGGI
Host Institution (HI) UNIVERSITA DEGLI STUDI DI PARMA
Call Details Proof of Concept (PoC), PC1, ERC-2011-PoC
Summary "A low-cost sensor able to perceive 3D information would be a breakthrough for a number of applications. Automotive applications would benefit from a low-cost obstacle detector to increase road safety; agricultural vehicles would be able to sense the environment and perform precise (and even autonomous) maneuvers improving their effectiveness; efficient sensing would be a key also to future building automation: elevators doors would close just after boarding and keep open when detecting people's intention to enter, automatic doors would not open when individuals would move in their sensed area but without the intention to cross the door. Even the entertainment industry, which lately invested massively on innovative and interactive sensors, would benefit from precise 3D sensors working even outdoor or in combination with multiple identical sensors.
This proposal is aimed at preparing an engineered version of the current stereo-based system developed for vehicles within the OFAV ERC-funded Advanced Grant and currently under test in many other application domains. It is based on two microcameras and a smart software reconstructing the 3D environment; the software will be ported on a low-cost FPGA+DSP integrated into the sensor box, providing a small and light passive sensor for a variety of applications that nowadays either use other technologies (laser based) or are not able to reach the performance provided by this sensor (e.g. IR-based elevators' door control which is not working in highly illuminated sites and covers only smaller areas).
The algorithm which is now working on a PC-based platform is owned by the team working for the OFAV Project and delivers superb results in terms of accuracy. This proposal is intended to provide resources to implement this solution in hardware and produce a low-cost, small-sized, and high performance sensor to be used in a very wide range of applications."
Summary
"A low-cost sensor able to perceive 3D information would be a breakthrough for a number of applications. Automotive applications would benefit from a low-cost obstacle detector to increase road safety; agricultural vehicles would be able to sense the environment and perform precise (and even autonomous) maneuvers improving their effectiveness; efficient sensing would be a key also to future building automation: elevators doors would close just after boarding and keep open when detecting people's intention to enter, automatic doors would not open when individuals would move in their sensed area but without the intention to cross the door. Even the entertainment industry, which lately invested massively on innovative and interactive sensors, would benefit from precise 3D sensors working even outdoor or in combination with multiple identical sensors.
This proposal is aimed at preparing an engineered version of the current stereo-based system developed for vehicles within the OFAV ERC-funded Advanced Grant and currently under test in many other application domains. It is based on two microcameras and a smart software reconstructing the 3D environment; the software will be ported on a low-cost FPGA+DSP integrated into the sensor box, providing a small and light passive sensor for a variety of applications that nowadays either use other technologies (laser based) or are not able to reach the performance provided by this sensor (e.g. IR-based elevators' door control which is not working in highly illuminated sites and covers only smaller areas).
The algorithm which is now working on a PC-based platform is owned by the team working for the OFAV Project and delivers superb results in terms of accuracy. This proposal is intended to provide resources to implement this solution in hardware and produce a low-cost, small-sized, and high performance sensor to be used in a very wide range of applications."
Max ERC Funding
148 061 €
Duration
Start date: 2012-06-01, End date: 2013-10-31
Project acronym A-DATADRIVE-B
Project Advanced Data-Driven Black-box modelling
Researcher (PI) Johan Adelia K Suykens
Host Institution (HI) KATHOLIEKE UNIVERSITEIT LEUVEN
Call Details Advanced Grant (AdG), PE7, ERC-2011-ADG_20110209
Summary Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.
Summary
Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.
Max ERC Funding
2 485 800 €
Duration
Start date: 2012-04-01, End date: 2017-03-31
Project acronym ABACUS
Project Advancing Behavioral and Cognitive Understanding of Speech
Researcher (PI) Bart De Boer
Host Institution (HI) VRIJE UNIVERSITEIT BRUSSEL
Call Details Starting Grant (StG), SH4, ERC-2011-StG_20101124
Summary I intend to investigate what cognitive mechanisms give us combinatorial speech. Combinatorial speech is the ability to make new words using pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, I will find out how we deal with combinatorial speech.
The experimental part will study individual and cultural learning. Experimental cultural learning is a new technique that simulates cultural evolution in the laboratory. Two types of cultural learning will be used: iterated learning, which simulates language transfer across generations, and social coordination, which simulates emergence of norms in a language community. Using the two types of cultural learning together with individual learning experiments will help to zero in, from three angles, on how humans deal with combinatorial speech. In addition it will make a methodological contribution by comparing the strengths and weaknesses of the three methods.
The computer modeling part will formalize hypotheses about how our brains deal with combinatorial speech. Two models will be built: a high-level model that will establish the basic algorithms with which combinatorial speech is learned and reproduced, and a neural model that will establish in more detail how the algorithms are implemented in the brain. In addition, the models, through increasing understanding of how humans deal with speech, will help bridge the performance gap between human and computer speech recognition.
The project will advance science in four ways: it will provide insight into how our unique ability for using combinatorial speech works, it will tell us how this is implemented in the brain, it will extend the novel methodology of experimental cultural learning and it will create new computer models for dealing with human speech.
Summary
I intend to investigate what cognitive mechanisms give us combinatorial speech. Combinatorial speech is the ability to make new words using pre-existing speech sounds. Humans are the only apes that can do this, yet we do not know how our brains do it, nor how exactly we differ from other apes. Using new experimental techniques to study human behavior and new computational techniques to model human cognition, I will find out how we deal with combinatorial speech.
The experimental part will study individual and cultural learning. Experimental cultural learning is a new technique that simulates cultural evolution in the laboratory. Two types of cultural learning will be used: iterated learning, which simulates language transfer across generations, and social coordination, which simulates emergence of norms in a language community. Using the two types of cultural learning together with individual learning experiments will help to zero in, from three angles, on how humans deal with combinatorial speech. In addition it will make a methodological contribution by comparing the strengths and weaknesses of the three methods.
The computer modeling part will formalize hypotheses about how our brains deal with combinatorial speech. Two models will be built: a high-level model that will establish the basic algorithms with which combinatorial speech is learned and reproduced, and a neural model that will establish in more detail how the algorithms are implemented in the brain. In addition, the models, through increasing understanding of how humans deal with speech, will help bridge the performance gap between human and computer speech recognition.
The project will advance science in four ways: it will provide insight into how our unique ability for using combinatorial speech works, it will tell us how this is implemented in the brain, it will extend the novel methodology of experimental cultural learning and it will create new computer models for dealing with human speech.
Max ERC Funding
1 276 620 €
Duration
Start date: 2012-02-01, End date: 2017-01-31
Project acronym ACCOPT
Project ACelerated COnvex OPTimization
Researcher (PI) Yurii NESTEROV
Host Institution (HI) UNIVERSITE CATHOLIQUE DE LOUVAIN
Call Details Advanced Grant (AdG), PE1, ERC-2017-ADG
Summary The amazing rate of progress in the computer technologies and telecommunications presents many new challenges for Optimization Theory. New problems are usually very big in size, very special in structure and possibly have a distributed data support. This makes them unsolvable by the standard optimization methods. In these situations, old theoretical models, based on the hidden Black-Box information, cannot work. New theoretical and algorithmic solutions are urgently needed. In this project we will concentrate on development of fast optimization methods for problems of big and very big size. All the new methods will be endowed with provable efficiency guarantees for large classes of optimization problems, arising in practical applications. Our main tool is the acceleration technique developed for the standard Black-Box methods as applied to smooth convex functions. However, we will have to adapt it to deal with different situations.
The first line of development will be based on the smoothing technique as applied to a non-smooth functions. We propose to substantially extend this approach to generate approximate solutions in relative scale. The second line of research will be related to applying acceleration techniques to the second-order methods minimizing functions with sparse Hessians. Finally, we aim to develop fast gradient methods for huge-scale problems. The size of these problems is so big that even the usual vector operations are extremely expensive. Thus, we propose to develop new methods with sublinear iteration costs. In our approach, the main source for achieving improvements will be the proper use of problem structure.
Our overall aim is to be able to solve in a routine way many important problems, which currently look unsolvable. Moreover, the theoretical development of Convex Optimization will reach the state, when there is no gap between theory and practice: the theoretically most efficient methods will definitely outperform any homebred heuristics.
Summary
The amazing rate of progress in the computer technologies and telecommunications presents many new challenges for Optimization Theory. New problems are usually very big in size, very special in structure and possibly have a distributed data support. This makes them unsolvable by the standard optimization methods. In these situations, old theoretical models, based on the hidden Black-Box information, cannot work. New theoretical and algorithmic solutions are urgently needed. In this project we will concentrate on development of fast optimization methods for problems of big and very big size. All the new methods will be endowed with provable efficiency guarantees for large classes of optimization problems, arising in practical applications. Our main tool is the acceleration technique developed for the standard Black-Box methods as applied to smooth convex functions. However, we will have to adapt it to deal with different situations.
The first line of development will be based on the smoothing technique as applied to a non-smooth functions. We propose to substantially extend this approach to generate approximate solutions in relative scale. The second line of research will be related to applying acceleration techniques to the second-order methods minimizing functions with sparse Hessians. Finally, we aim to develop fast gradient methods for huge-scale problems. The size of these problems is so big that even the usual vector operations are extremely expensive. Thus, we propose to develop new methods with sublinear iteration costs. In our approach, the main source for achieving improvements will be the proper use of problem structure.
Our overall aim is to be able to solve in a routine way many important problems, which currently look unsolvable. Moreover, the theoretical development of Convex Optimization will reach the state, when there is no gap between theory and practice: the theoretically most efficient methods will definitely outperform any homebred heuristics.
Max ERC Funding
2 090 038 €
Duration
Start date: 2018-09-01, End date: 2023-08-31
Project acronym ADAPTEM
Project Adaptive transmission electron microscopy: development of a programmable phase plate
Researcher (PI) Johan VERBEECK
Host Institution (HI) UNIVERSITEIT ANTWERPEN
Call Details Proof of Concept (PoC), ERC-2017-PoC
Summary Adaptive optics, the technology to dynamically program the phase of optical waves has sparked an avalanche of scientific discoveries and innovations in light optics applications. Nowadays, the phase of optical waves can be dynamically programmed providing research on exotic optical beams and unprecedented control over the performance of optical instruments. Although electron waves carry many similarities in comparison to their optical counterparts, a generic programmable phase plate for electrons is still missing. This project aims at developing a prototype of a programmable electrostatic phase plate that allows the user to freely change the phase of electron waves and demonstrate it to potential licensees for further upscaling and introduction to the market. The target of this POC project is the realization of a tunable easy-to-use 5x5-pixel prototype that will demonstrate the potential of adaptive optics in electron microscopy. Its realization will be based on lithographic technology to allow for future upscaling. It is expected that such a phase plate can dramatically increase the information obtained at a given electron dose, limiting the detrimental effects of beam damage that currently hinders the use of electron microscopy in e.g. life sciences. As such, it has the potential to disrupt the electron microscopy market with novel applications while at the same time reducing cost and complexity and increasing the potential for fully automated instruments.
Summary
Adaptive optics, the technology to dynamically program the phase of optical waves has sparked an avalanche of scientific discoveries and innovations in light optics applications. Nowadays, the phase of optical waves can be dynamically programmed providing research on exotic optical beams and unprecedented control over the performance of optical instruments. Although electron waves carry many similarities in comparison to their optical counterparts, a generic programmable phase plate for electrons is still missing. This project aims at developing a prototype of a programmable electrostatic phase plate that allows the user to freely change the phase of electron waves and demonstrate it to potential licensees for further upscaling and introduction to the market. The target of this POC project is the realization of a tunable easy-to-use 5x5-pixel prototype that will demonstrate the potential of adaptive optics in electron microscopy. Its realization will be based on lithographic technology to allow for future upscaling. It is expected that such a phase plate can dramatically increase the information obtained at a given electron dose, limiting the detrimental effects of beam damage that currently hinders the use of electron microscopy in e.g. life sciences. As such, it has the potential to disrupt the electron microscopy market with novel applications while at the same time reducing cost and complexity and increasing the potential for fully automated instruments.
Max ERC Funding
148 500 €
Duration
Start date: 2018-03-01, End date: 2019-08-31
Project acronym ADMIRE
Project A holographic microscope for the immersive exploration of augmented micro-reality
Researcher (PI) Roberto DI LEONARDO
Host Institution (HI) UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA
Call Details Proof of Concept (PoC), ERC-2017-PoC
Summary Virtual reality, augmented reality and mixed reality are beginning to transform the way we explore and acquire information from the macroscopic world around us. At the same time, recent advances in holographic microscopy are providing new tools for the 3D imaging of physical and biological phenomena occurring at the micron scale. Project ADMIRE will combine this two emerging technologies into the first prototype of an AugmenteD MIcro-REality system for the immersive exploration and the quantitative analysis of three-dimensional processes at the micron scale.
The core of the proposed system will be the three-axis holographic microscope (3DHM) developed within the ERC Project SMART to investigate fast 3D dynamics of swimming bacteria.
ADMIRE project will transform 3DHM from a laboratory technique, targeted to a specific application and operated by highly specialised researchers into a general purpose instrument composed of a compact add-on module for commercial optical microscopes and a virtual reality interface allowing for a direct and intuitive use. Through the ADMIRE Holographic Microscope (ADMIRE-HM) the user will be “shrunk” a million times and virtually sent into a live 3D reconstruction of the real microscopic world contained in the glass slide. There he will find himself surrounded by micro-particles or moving cells that could be inspected from multiple directions and characterized by shape parameters (e.g. size, volume, aspect-ratio) or dynamical features (e.g. flagellar motility, sedimentation velocity, transport in a flow) obtained by means of simple and direct gestures.
The expected outcome of the project is to bring to a development stage TRL 6-7 a technology that could change the way we experience the microscopic world in basic research, biomedical applications and education.
Summary
Virtual reality, augmented reality and mixed reality are beginning to transform the way we explore and acquire information from the macroscopic world around us. At the same time, recent advances in holographic microscopy are providing new tools for the 3D imaging of physical and biological phenomena occurring at the micron scale. Project ADMIRE will combine this two emerging technologies into the first prototype of an AugmenteD MIcro-REality system for the immersive exploration and the quantitative analysis of three-dimensional processes at the micron scale.
The core of the proposed system will be the three-axis holographic microscope (3DHM) developed within the ERC Project SMART to investigate fast 3D dynamics of swimming bacteria.
ADMIRE project will transform 3DHM from a laboratory technique, targeted to a specific application and operated by highly specialised researchers into a general purpose instrument composed of a compact add-on module for commercial optical microscopes and a virtual reality interface allowing for a direct and intuitive use. Through the ADMIRE Holographic Microscope (ADMIRE-HM) the user will be “shrunk” a million times and virtually sent into a live 3D reconstruction of the real microscopic world contained in the glass slide. There he will find himself surrounded by micro-particles or moving cells that could be inspected from multiple directions and characterized by shape parameters (e.g. size, volume, aspect-ratio) or dynamical features (e.g. flagellar motility, sedimentation velocity, transport in a flow) obtained by means of simple and direct gestures.
The expected outcome of the project is to bring to a development stage TRL 6-7 a technology that could change the way we experience the microscopic world in basic research, biomedical applications and education.
Max ERC Funding
150 000 €
Duration
Start date: 2017-11-01, End date: 2019-04-30
Project acronym AfricanWomen
Project Women in Africa
Researcher (PI) catherine GUIRKINGER
Host Institution (HI) UNIVERSITE DE NAMUR ASBL
Call Details Starting Grant (StG), SH1, ERC-2017-STG
Summary Rates of domestic violence and the relative risk of premature death for women are higher in sub-Saharan Africa than in any other region. Yet we know remarkably little about the economic forces, incentives and constraints that drive discrimination against women in this region, making it hard to identify policy levers to address the problem. This project will help fill this gap.
I will investigate gender discrimination from two complementary perspectives. First, through the lens of economic history, I will investigate the forces driving trends in women’s relative well-being since slavery. To quantify the evolution of well-being of sub-Saharan women relative to men, I will use three types of historical data: anthropometric indicators (relative height), vital statistics (to compute numbers of missing women), and outcomes of formal and informal family law disputes. I will then investigate how major economic developments and changes in family laws differentially affected women’s welfare across ethnic groups with different norms on women’s roles and rights.
Second, using intra-household economic models, I will provide new insights into domestic violence and gender bias in access to crucial resources in present-day Africa. I will develop a new household model that incorporates gender identity and endogenous outside options to explore the relationship between women’s empowerment and the use of violence. Using the notion of strategic delegation, I will propose a new rationale for the separation of budgets often observed in African households and generate predictions of how improvements in women’s outside options affect welfare. Finally, with first hand data, I will investigate intra-household differences in nutrition and work effort in times of food shortage from the points of view of efficiency and equity. I will use activity trackers as an innovative means of collecting high quality data on work effort and thus overcome data limitations restricting the existing literature
Summary
Rates of domestic violence and the relative risk of premature death for women are higher in sub-Saharan Africa than in any other region. Yet we know remarkably little about the economic forces, incentives and constraints that drive discrimination against women in this region, making it hard to identify policy levers to address the problem. This project will help fill this gap.
I will investigate gender discrimination from two complementary perspectives. First, through the lens of economic history, I will investigate the forces driving trends in women’s relative well-being since slavery. To quantify the evolution of well-being of sub-Saharan women relative to men, I will use three types of historical data: anthropometric indicators (relative height), vital statistics (to compute numbers of missing women), and outcomes of formal and informal family law disputes. I will then investigate how major economic developments and changes in family laws differentially affected women’s welfare across ethnic groups with different norms on women’s roles and rights.
Second, using intra-household economic models, I will provide new insights into domestic violence and gender bias in access to crucial resources in present-day Africa. I will develop a new household model that incorporates gender identity and endogenous outside options to explore the relationship between women’s empowerment and the use of violence. Using the notion of strategic delegation, I will propose a new rationale for the separation of budgets often observed in African households and generate predictions of how improvements in women’s outside options affect welfare. Finally, with first hand data, I will investigate intra-household differences in nutrition and work effort in times of food shortage from the points of view of efficiency and equity. I will use activity trackers as an innovative means of collecting high quality data on work effort and thus overcome data limitations restricting the existing literature
Max ERC Funding
1 499 313 €
Duration
Start date: 2018-08-01, End date: 2023-07-31
Project acronym AGEnTh
Project Atomic Gauge and Entanglement Theories
Researcher (PI) Marcello DALMONTE
Host Institution (HI) SCUOLA INTERNAZIONALE SUPERIORE DI STUDI AVANZATI DI TRIESTE
Call Details Starting Grant (StG), PE2, ERC-2017-STG
Summary AGEnTh is an interdisciplinary proposal which aims at theoretically investigating atomic many-body systems (cold atoms and trapped ions) in close connection to concepts from quantum information, condensed matter, and high energy physics. The main goals of this programme are to:
I) Find to scalable schemes for the measurements of entanglement properties, and in particular entanglement spectra, by proposing a shifting paradigm to access entanglement focused on entanglement Hamiltonians and field theories instead of probing density matrices;
II) Show how atomic gauge theories (including dynamical gauge fields) are ideal candidates for the realization of long-sought, highly-entangled states of matter, in particular topological superconductors supporting parafermion edge modes, and novel classes of quantum spin liquids emerging from clustering;
III) Develop new implementation strategies for the realization of gauge symmetries of paramount importance, such as discrete and SU(N)xSU(2)xU(1) groups, and establish a theoretical framework for the understanding of atomic physics experiments within the light-from-chaos scenario pioneered in particle physics.
These objectives are at the cutting-edge of fundamental science, and represent a coherent effort aimed at underpinning unprecedented regimes of strongly interacting quantum matter by addressing the basic aspects of probing, many-body physics, and implementations. The results are expected to (i) build up and establish qualitatively new synergies between the aforementioned communities, and (ii) stimulate an intense theoretical and experimental activity focused on both entanglement and atomic gauge theories.
In order to achieve those, AGEnTh builds: (1) on my background working at the interface between atomic physics and quantum optics from one side, and many-body theory on the other, and (2) on exploratory studies which I carried out to mitigate the conceptual risks associated with its high-risk/high-gain goals.
Summary
AGEnTh is an interdisciplinary proposal which aims at theoretically investigating atomic many-body systems (cold atoms and trapped ions) in close connection to concepts from quantum information, condensed matter, and high energy physics. The main goals of this programme are to:
I) Find to scalable schemes for the measurements of entanglement properties, and in particular entanglement spectra, by proposing a shifting paradigm to access entanglement focused on entanglement Hamiltonians and field theories instead of probing density matrices;
II) Show how atomic gauge theories (including dynamical gauge fields) are ideal candidates for the realization of long-sought, highly-entangled states of matter, in particular topological superconductors supporting parafermion edge modes, and novel classes of quantum spin liquids emerging from clustering;
III) Develop new implementation strategies for the realization of gauge symmetries of paramount importance, such as discrete and SU(N)xSU(2)xU(1) groups, and establish a theoretical framework for the understanding of atomic physics experiments within the light-from-chaos scenario pioneered in particle physics.
These objectives are at the cutting-edge of fundamental science, and represent a coherent effort aimed at underpinning unprecedented regimes of strongly interacting quantum matter by addressing the basic aspects of probing, many-body physics, and implementations. The results are expected to (i) build up and establish qualitatively new synergies between the aforementioned communities, and (ii) stimulate an intense theoretical and experimental activity focused on both entanglement and atomic gauge theories.
In order to achieve those, AGEnTh builds: (1) on my background working at the interface between atomic physics and quantum optics from one side, and many-body theory on the other, and (2) on exploratory studies which I carried out to mitigate the conceptual risks associated with its high-risk/high-gain goals.
Max ERC Funding
1 055 317 €
Duration
Start date: 2018-05-01, End date: 2023-04-30
Project acronym AI-CU
Project Automated Improvement of Continuous User interfaces
Researcher (PI) BART GERBEN DE BOER
Host Institution (HI) VRIJE UNIVERSITEIT BRUSSEL
Call Details Proof of Concept (PoC), ERC-2017-PoC
Summary We propose to develop two tools for creating, in a systematic way, better user interfaces based on continuous, non-symbolic actions, such as swipes on a touch screen, 3-D motions with a hand-held device, or breath patterns in a user interface for otherwise paralyzed patients. The tools are based on two experimental/computational techniques developed in the ABACUS project: iterated learning and social coordination.
In iterated learning, sets of signals produced by one user are learned and reproduced by another user. The reproductions are then in turn learned by the next user. In the ABACUS project, it has been shown that this results in more learnable sets of signals. We propose to show how this can be applied to creating learnable and usable signals in a systematic way when design a user interface for a device that allows continuous actions.
In social coordination, it has been shown that signals become simplified and more abstract when people communicate over an extended period of time. The ABACUS project has developed techniques to detect and quantify this. We propose to show how these can be used for a user interface that adapts to its user. This will allow novice users to use more extended and therefore more learnable versions of actions, while the system adapts when users become more adept at using the interface and reduce their actions. Because the system is adaptive, the user is not constrained in how they do this.
Concretely, we propose to implement these two tools, investigate how they can be used optimally and advertise them to
interested companies, starting with ones with which we have contact, but extending our network at the start of the project through a business case development. In order to disseminate the results we propose to involve a user committee and organize one or more workshops.
Summary
We propose to develop two tools for creating, in a systematic way, better user interfaces based on continuous, non-symbolic actions, such as swipes on a touch screen, 3-D motions with a hand-held device, or breath patterns in a user interface for otherwise paralyzed patients. The tools are based on two experimental/computational techniques developed in the ABACUS project: iterated learning and social coordination.
In iterated learning, sets of signals produced by one user are learned and reproduced by another user. The reproductions are then in turn learned by the next user. In the ABACUS project, it has been shown that this results in more learnable sets of signals. We propose to show how this can be applied to creating learnable and usable signals in a systematic way when design a user interface for a device that allows continuous actions.
In social coordination, it has been shown that signals become simplified and more abstract when people communicate over an extended period of time. The ABACUS project has developed techniques to detect and quantify this. We propose to show how these can be used for a user interface that adapts to its user. This will allow novice users to use more extended and therefore more learnable versions of actions, while the system adapts when users become more adept at using the interface and reduce their actions. Because the system is adaptive, the user is not constrained in how they do this.
Concretely, we propose to implement these two tools, investigate how they can be used optimally and advertise them to
interested companies, starting with ones with which we have contact, but extending our network at the start of the project through a business case development. In order to disseminate the results we propose to involve a user committee and organize one or more workshops.
Max ERC Funding
150 000 €
Duration
Start date: 2018-06-01, End date: 2019-11-30
Project acronym AMDROMA
Project Algorithmic and Mechanism Design Research in Online MArkets
Researcher (PI) Stefano LEONARDI
Host Institution (HI) UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA
Call Details Advanced Grant (AdG), PE6, ERC-2017-ADG
Summary Online markets currently form an important share of the global economy. The Internet hosts classical markets (real-estate, stocks, e-commerce) as well allowing new markets with previously unknown features (web-based advertisement, viral marketing, digital goods, crowdsourcing, sharing economy). Algorithms play a central role in many decision processes involved in online markets. For example, algorithms run electronic auctions, trade stocks, adjusts prices dynamically, and harvest big data to provide economic information. Thus, it is of paramount importance to understand the algorithmic and mechanism design foundations of online markets.
The algorithmic research issues that we consider involve algorithmic mechanism design, online and approximation algorithms, modelling uncertainty in online market design, and large-scale data analysisonline and approximation algorithms, large-scale optimization and data mining. The aim of this research project is to combine these fields to consider research questions that are central for today's Internet economy. We plan to apply these techniques so as to solve fundamental algorithmic problems motivated by web-basedInternet advertisement, Internet market designsharing economy, and crowdsourcingonline labour marketplaces. While my planned research is focussedcentered on foundational work with rigorous design and analysis of in algorithms and mechanismsic design and analysis, it will also include as an important component empirical validation on large-scale real-life datasets.
Summary
Online markets currently form an important share of the global economy. The Internet hosts classical markets (real-estate, stocks, e-commerce) as well allowing new markets with previously unknown features (web-based advertisement, viral marketing, digital goods, crowdsourcing, sharing economy). Algorithms play a central role in many decision processes involved in online markets. For example, algorithms run electronic auctions, trade stocks, adjusts prices dynamically, and harvest big data to provide economic information. Thus, it is of paramount importance to understand the algorithmic and mechanism design foundations of online markets.
The algorithmic research issues that we consider involve algorithmic mechanism design, online and approximation algorithms, modelling uncertainty in online market design, and large-scale data analysisonline and approximation algorithms, large-scale optimization and data mining. The aim of this research project is to combine these fields to consider research questions that are central for today's Internet economy. We plan to apply these techniques so as to solve fundamental algorithmic problems motivated by web-basedInternet advertisement, Internet market designsharing economy, and crowdsourcingonline labour marketplaces. While my planned research is focussedcentered on foundational work with rigorous design and analysis of in algorithms and mechanismsic design and analysis, it will also include as an important component empirical validation on large-scale real-life datasets.
Max ERC Funding
1 780 150 €
Duration
Start date: 2018-07-01, End date: 2023-06-30