Uncertainty Quantification for Linear Hyperbolic Equations with Stochastic Process or Random Field Coefficients

In this paper hyperbolic partial differential equations with random coefficients are discussed. Such random partial differential equations appear for instance in traffic flow problems as well as in many physical processes in random media. Two types of models are presented: The first has a time-dependent coefficient modeled by the Ornstein--Uhlenbeck process. The second has a random field coefficient with a given covariance in space. For the former a formula for the exact solution in terms of moments is derived. In both cases stable numerical schemes are introduced to solve these random partial differential equations. Simulation results including convergence studies conclude the theoretical findings

ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

Physical intuition is pivotal for intelligent agents to perform complex tasks. In this paper we investigate the passive acquisition of an intuitive understanding of physical principles as well as the active utilisation of this intuition in the context of generalised object stacking. To this end, we provide: a simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability. We train visual classifiers for binary stability prediction on the ShapeStacks data and scrutinise their learned physical intuition. Due to the richness of the training data our approach also generalises favourably to real-world scenarios achieving state-of-the-art stability prediction on a publicly available benchmark of block towers. We then leverage the physical intuition learned by our model to actively construct stable stacks and observe the emergence of an intuitive notion of stackability - an inherent object affordance - induced by the active stacking task. Our approach performs well even in challenging conditions where it considerably exceeds the stack height observed during training or in cases where initially unstable structures must be stabilised via counterbalancing.Comment: revised version to appear at ECCV 201

Eltern-Kind-Interaktionen mit Bilderbüchern und / oder Tablet PC?

Mit der zunehmenden Mediatisierung unserer Gesellschaft wird auch die Gruppe der 1-3 Jährigen als Zielgruppe für digitale Mediennutzung (Tablet-PCs, Smart-Phones etc.) entdeckt. Im Beitrag werden bestehende Studien zur Nutzung und insbesondere zur Qualität der Nutzung von e-books und Tablet PCs und deren Einfluss auf die Entwicklung von Fähigkeiten der „Literacy“ vorgestellt und diskutiert. Im anglo-amerikanischen / kanadischen Raum konnte in verschiedenen Studien festgestellt werden, dass Eltern auch mit Kindern der jüngeren Altersgruppe bereits digitale Medien nutzen. Für die Erstellung von e-books wurden bereits Leitlinien zu „best practice“ entwickelt. Darüber hinaus finden sich in Studien zum Leseverhalten mit e-books kontroverse Forschungsergebnisse. In einigen Studien konnte festgestellt werden, dass es zwar zu einem unterschiedlichen Leseverhalten bei e-books, insbesondere bei „enhanced e-books“ und klassischen Bilderbüchern kommt, dass aber bezogen auf den Erwerb verschiedener Aspekte der Literacy keine Unterschiede zu erwarten sind, bzw. dass sogar bei „enhanced e-books“ ein besseres Geschichtenverständnis erreicht werden kann und mehr Informationen zum Verhältnis Schrift - Sprache für die Kinder vermittelt wird. Im Gegensatz dazu konnte in anderen Studien festgestellt werden, dass gerade die übermäßige Verwendung von „hotspots“ in „enhanced e-books“ dazu führen kann, dass ein Verständnis der gesamten Geschichte bei den Kindern erschwert wird und dass das gemeinsame Lesen gegenüber der Aufmerksamkeit auf Spiele und andere Ablenker verloren geht. So scheint die Qualität der verwendeten e-books einen erheblichen Einfluss auf die zu erwartenden Ergebnisse zu haben. Im Beitrag wird das Nutzungsverhalten und die Qualität der angebotenen Medien (Apps, e-books...) auch im deutschsprachigen Raum, insbesondere für die Zielgruppe der 1-3jährgen diskutiert

Optimizing delegation between human and AI collaborative agents

In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance deficiencies. Additionally, we cannot always expect the various agents to operate within the same underlying model of the environment. It is possible to encounter cases where the actions and transitions would vary between agents. Therefore, our framework provides a manager model which learns through observations of team performance without restricting agents to matching dynamics. Our results show our manager learns to perform delegation decisions with teams of agents operating under differing representations of the environment, significantly outperforming alternative methods to manage the team.Comment: This work has been accepted to the 'Towards Hybrid Human-Machine Learning and Decision Making (HLDM)' workshop at ECML PKDD 202

Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems

Given an increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g. perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human-AI teaming case where a managing agent is tasked with identifying when to perform a delegation assignment and whether the human or autonomous system should gain control. In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent failure as a result of their sensing capabilities and possible deficiencies. We model how the environmental context can contribute to, or exacerbate, the sensing deficiencies. These contexts provide cases where the manager must learn to attribute capabilities to suitability for decision-making. As such, we demonstrate how a Reinforcement Learning (RL) manager can correct the context-delegation association and assist the hybrid team of agents in outperforming the behavior of any agent working in isolation

Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes

Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be guided. To this end, we introduce neural stethoscopes as a general purpose framework for quantifying the degree of importance of specific factors of influence in deep neural networks as well as for actively promoting and suppressing information as appropriate. In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. We apply neural stethoscopes to analyse the state-of-the-art neural network for stability prediction. We show that the baseline model is susceptible to being misled by incorrect visual cues. This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability. Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy. Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset. Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%

Short-term synaptic plasticity regulates the level of olivocochlear inhibition to auditory hair cells

In the mammalian inner ear, the gain control of auditory inputs is exerted by medial olivocochlear (MOC) neurons that innervate cochlear outer hair cells (OHCs). OHCs mechanically amplify the incoming sound waves by virtue of their electromotile properties while the MOC system reduces the gain of auditory inputs by inhibiting OHC function. How this process is orchestrated at the synaptic level remains unknown. In the present study, MOC firing was evoked by electrical stimulation in an isolated mouse cochlear preparation, while OHCs postsynaptic responses were monitored by whole-cell recordings. These recordings confirmed that electrically evoked IPSCs (eIPSCs) are mediated solely by α9β10 nAChRs functionally coupled to calcium-activated SK2 channels. Synaptic release occurred with low probability when MOC-OHC synapses were stimulated at 1 Hz. However, as the stimulation frequency was raised, the reliability of release increased due to presynaptic facilitation. In addition, the relatively slow decay of eIPSCs gave rise to temporal summation at stimulation frequencies >10 Hz. The combined effect of facilitation and summation resulted in a frequency-dependent increase in the average amplitude of inhibitory currents in OHCs. Thus, we have demonstrated that short-term plasticity is responsible for shaping MOC inhibition and, therefore, encodes the transfer function from efferent firing frequency to the gain of the cochlear amplifier.Fil: Ballestero, Jimena Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Zorrilla de San Martín, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Goutman, Juan Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Elgoyhen, Ana Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentina. Universidad de Buenos Aires. Facultad de Medicina; ArgentinaFil: Fuchs, Paul A.. The Johns Hopkins University School of Medicine; Estados UnidosFil: Katz, Eleonora. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; Argentin

Zur Entstehung der Sprache bei Kindern : Diskussion aktueller Konzepte zur spezifischen Sprachentwicklungsstörung und zum Dysgrammatismus

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