Functional relations modulate the responsiveness to affordances despite the impact of conflicting stimulus-response mappings

The study investigated how conflicting stimulus-response mappings influenced affordance processing given a manipulation of the functional relations. Participants performed a task involving consistent-inconsistent stimulus-response mappings: Implicit Relational Assessment Procedure (IRAP). They were instructed to confirm or to deny a relation between words and tool-objects (consistent blocks) or to provide non-conventional responses (inconsistent blocks). The relations between stimuli could functionally match (e.g., Kitchen- Spatula) or not (e.g., Kitchen- Hammer), as well as the spatial relations (e.g., a match or a mismatch between participants' hand response and the tool-object orientation). The results showed faster reaction times (RTs) when functional relations between stimuli matched both in consistent and inconsistent blocks. Differences in RTs and accuracy between consistent and inconsistent blocks were only found when the functional relation between stimuli matched. No modulation of the performance was observed for mismatching functional relations and spatial relations between blocks. These results support the hypothesis that the responsiveness to affordances is strongly modulated by matching functional relations, despite the impact of conflicting stimulus-response mappings

Knowledge-aware Complementary Product Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers

Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.Comment: To appear in 2nd Conference on Robot Learning (CoRL) 201

Let's Eat Better Breakfasts

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Fibrillar Elastomeric Micropatterns Create Tunable Adhesion Even to Rough Surfaces

Acknowledgements V.B., N.K.G., and E.A. contributed with conception and experimental design. V.B. performed the experiments. V.B., R.H., A.G., and R.M.M. carried out analysis and interpretation of data. V.B., R.H., A.G., and E.A. wrote the manuscript. V.B. and R.H. contributed equally to this work. V.B. acknowledges funding by SPP 1420 of the German Science Foundation DFG. E.A., N.K.G., and R.H. acknowledge funding from the European Research Council under the European Union/ERC Advanced Grant “Switch2Stick,” Agreement No. 340929.Peer reviewedPublisher PD

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively.Comment: 11 pages, published in EMNLP 201

Dilatancy, Jamming, and the Physics of Granulation

Granulation is a process whereby a dense colloidal suspension is converted into pasty granules (surrounded by air) by application of shear. Central to the stability of the granules is the capillary force arising from the interfacial tension between solvent and air. This force appears capable of maintaining a solvent granule in a jammed solid state, under conditions where the same amount of solvent and colloid could also exist as a flowable droplet. We argue that in the early stages of granulation the physics of dilatancy, which requires that a powder expand on shearing, is converted by capillary forces into the physics of arrest. Using a schematic model of colloidal arrest under stress, we speculate upon various jamming and granulation scenarios. Some preliminary experimental results on aspects of granulation in hard-sphere colloidal suspensions are also reported.Comment: Original article intended for J Phys Cond Mat special issue on Granular Materials (M Nicodemi, Ed.