ERC FUNDED PROJECTS

Even after three decades of research on human-computer interaction (HCI), current general-purpose user interfaces (UI) still lack the ability to attribute mental states to their users, i.e. they fail to understand users' intentions and needs and to anticipate their actions. This drastically restricts their interactive capabilities. ANTICIPATE aims to establish the scientific foundations for a new generation of user interfaces that pro-actively adapt to users' future input actions by monitoring their attention and predicting their interaction intentions - thereby significantly improving the naturalness, efficiency, and user experience of the interactions. Realising this vision of anticipatory human-computer interaction requires groundbreaking advances in everyday sensing of user attention from eye and brain activity. We will further pioneer methods to predict entangled user intentions and forecast interactive behaviour with fine temporal granularity during interactions in everyday stationary and mobile settings. Finally, we will develop fundamental interaction paradigms that enable anticipatory UIs to pro-actively adapt to users' attention and intentions in a mindful way. The new capabilities will be demonstrated in four challenging cases: 1) mobile information retrieval, 2) intelligent notification management, 3) Autism diagnosis and monitoring, and 4) computer-based training. Anticipatory human-computer interaction offers a strong complement to existing UI paradigms that only react to user input post-hoc. If successful, ANTICIPATE will deliver the first important building blocks for implementing Theory of Mind in general-purpose UIs. As such, the project has the potential to drastically improve the billions of interactions we perform with computers every day, to trigger a wide range of follow-up research in HCI as well as adjacent areas within and outside computer science, and to act as a key technical enabler for new applications, e.g. in healthcare and education.

1 499 625 €

Start date: 2019-02-01, End date: 2024-01-31

"The attine fungus-growing ants are prime models for understanding phenotypic adaptations in social evolution and symbiosis. The mutualism has many hallmarks of advanced cooperation in its mating system commitments and functional complementarity between multiple symbiont partners, but potential conflicts between sexes and castes over reproductive priorities, and between hosts and symbionts over symbiont mixing have also been documented. With collaborators at BGI-Shenzhen and the Smithsonian Institution my group has obtained six reference genomes representing all genus-level branches of the higher attine ants and a lower attine outgroup. With collaborators in Denmark and Australia we have pioneered proteomic approaches to understand the preservation of sperm viability in spite of sperm competition and the enzymatic decomposition of plant substrates that the ants use to make their fungus gardens grow. Here, I propose an integrated study focusing on four major areas of attine ant biology that are particularly inviting for in depth molecular approaches: 1. The protein-level networks that secure life-time (up to 20 years) sperm storage in specialized ant-queen organs and the genetic mechanisms that shape and adjust these “sexual symbiome” networks. 2. The ant-fungal symbiome, i.e. the dynamics of fungal enzyme production for plant substrate degradation and the redistribution of these enzymes in fungus gardens through fecal deposition after they are ingested but not digested by the ants. 3. The microbial symbiome of ant guts and other tissues with obligate bacterial mutualists, of which we have identified some and will characterize a wider collection across the different branches of the attine ant phylogeny. 4. The genome-wide frequency of genomic imprinting and the significance of these imprints for the expression of caste phenotypes and the regulation of potential reproductive conflicts."

2 290 102 €

Start date: 2013-05-01, End date: 2018-04-30

Within a 12 month period, 20% of adults will meet criteria for one or more clinical anxiety disorders (ADs). These disorders are hugely disruptive, placing an emotional burden on individuals and their families. While both cognitive behavioural therapy and pharmacological treatment are widely viewed as effective strategies for managing ADs, systematic review of the literature reveals that only 30–45% of patients demonstrate a marked response to treatment (anxiety levels being reduced into the nonaffected range). In addition, a significant proportion of initial responders relapse after treatment is discontinued. There is hence a real and marked need to improve upon current approaches to AD treatment. One possible avenue for improving response rates is through optimizing initial treatment selection. Specifically, it is possible that certain individuals might respond better to cognitive interventions while others might respond better to pharmacological treatment. Recently it has been suggested that there may be two or more distinct biological pathways disrupted in anxiety. If this is the case, then specification of these pathways may be an important step in predicting which individuals are likely to respond to which treatment. Few studies have focused upon this issue and, in particular, upon identifying neural markers that might predict response to cognitive (as opposed to pharmacological) intervention. The proposed research aims to address this. Specifically, it tests the hypothesis that there are at least two mechanisms disrupted in ADs, one entailing amygdala hyper-responsivity to cues that signal threat, the other impoverished recruitment of frontal regions that support cognitive and emotional regulation. Two series of functional magnetic resonance imaging experiments will be conducted. These will investigate differences in amygdala and frontal function during (a) attentional processing and (b) fear conditioning. Initial clinical experiments will investigate whether Generalised Anxiety Disorder and Specific Phobia involve differing degrees of disruption to frontal versus amygdala function during these tasks. This work will feed into training studies, the goal being to characterize AD patient subgroups that benefit from cognitive training.

1 708 407 €

Start date: 2011-04-01, End date: 2016-08-31

Optimization problems are ubiquitous in computer science. Almost every problem involves the optimization of some objective function. However a major part of these problems cannot be solved to optimality. Therefore, algorithms that achieve provably good performance guarantees are of immense importance. Considerable progress has already been made, but great challenges remain: Some fundamental problems are not well understood. Moreover, for central problems arising in new applications, no solutions are known at all. The goal of APEG is to significantly advance the state of the art on algorithmic performance guarantees. Specifically, the project has two missions: First, it will develop new algorithmic techniques, breaking new ground in the areas of online algorithms, approximations algorithms and algorithmic game theory. Second, it will apply these techniques to solve fundamental problems that are central in these algorithmic disciplines. APEG will attack long-standing open problems, some of which have been unresolved for several decades. Furthermore, it will formulate and investigate new algorithmic problems that arise in modern applications. The research agenda encompasses a broad spectrum of classical and timely topics including (a) resource allocation in computer systems, (b) data structuring, (c) graph problems, with relations to Internet advertising, (d) complex networks and (e) massively parallel systems. In addition to basic optimization objectives, the project will also study the new performance metric of energy minimization in computer systems. Overall, APEG pursues cutting-edge algorithms research, focusing on both foundational problems and applications. Any progress promises to be a breakthrough or significant contribution.

2 404 250 €

Start date: 2016-10-01, End date: 2021-09-30