RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction

RoboJam is a machine-learning system for generating music that assists users of a touchscreen music app by performing responses to their short improvisations. This system uses a recurrent artificial neural network to generate sequences of touchscreen interactions and absolute timings, rather than high-level musical notes. To accomplish this, RoboJam's network uses a mixture density layer to predict appropriate touch interaction locations in space and time. In this paper, we describe the design and implementation of RoboJam's network and how it has been integrated into a touchscreen music app. A preliminary evaluation analyses the system in terms of training, musical generation and user interaction

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Lateral specialization in unilateral spatial neglect : a cognitive robotics model

In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans

Adaptive Forgetting Curves for Spaced Repetition Language Learning

The forgetting curve has been extensively explored by psychologists, educationalists and cognitive scientists alike. In the context of Intelligent Tutoring Systems, modelling the forgetting curve for each user and knowledge component (e.g. vocabulary word) should enable us to develop optimal revision strategies that counteract memory decay and ensure long-term retention. In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language. We evaluate the impact of the models and their features using data from an online vocabulary teaching platform and find that word complexity is a highly informative feature which may be successfully learned by a neural network model.Cambridge Assessmen

Simulating hemispatial neglect with virtual reality

<p>Abstract</p> <p>Background</p> <p>Hemispatial neglect is a cognitive disorder defined as a lack of attention for stimuli contra-lateral to the brain lesion. The assessment is traditionally done with basic pencil and paper tests and the rehabilitation programs are generally not well adapted. We propose a virtual reality system featuring an eye-tracking device for a better characterization of the neglect that will lead to new rehabilitation techniques.</p> <p>Methods</p> <p>This paper presents a comparison of eye-gaze patterns of healthy subjects, patients and healthy simulated patients on a virtual line bisection test. The task was also executed with a reduced visual field condition hoping that fewer stimuli would limit the neglect.</p> <p>Results</p> <p>We found that patients and healthy simulated patients had similar eye-gaze patterns. However, while the reduced visual field condition had no effect on the healthy simulated patients, it actually had a negative impact on the patients. We discuss the reasons for these differences and how they relate to the limitations of the neglect simulation.</p> <p>Conclusion</p> <p>We argue that with some improvements the technique could be used to determine the potential of new rehabilitation techniques and also help the rehabilitation staff or the patient's relatives to better understand the neglect condition.</p

Neural models that convince: Model hierarchies and other strategies to bridge the gap between behavior and the brain.

Computational modeling of the brain holds great promise as a bridge from brain to behavior. To fulfill this promise, however, it is not enough for models to be &apos;biologically plausible&apos;: models must be structurally accurate. Here, we analyze what this entails for so-called psychobiological models, models that address behavior as well as brain function in some detail. Structural accuracy may be supported by (1) a model&apos;s a priori plausibility, which comes from a reliance on evidence-based assumptions, (2) fitting existing data, and (3) the derivation of new predictions. All three sources of support require modelers to be explicit about the ontology of the model, and require the existence of data constraining the modeling. For situations in which such data are only sparsely available, we suggest a new approach. If several models are constructed that together form a hierarchy of models, higher-level models can be constrained by lower-level models, and low-level models can be constrained by behavioral features of the higher-level models. Modeling the same substrate at different levels of representation, as proposed here, thus has benefits that exceed the merits of each model in the hierarchy on its own

Letter processing and font information during reading: beyond distinctiveness, where vision meets design

Letter identification is a critical front end of the reading process. In general, conceptualizations of the identification process have emphasized arbitrary sets of distinctive features. However, a richer view of letter processing incorporates principles from the field of type design, including an emphasis on uniformities across letters within a font. The importance of uniformities is supported by a small body of research indicating that consistency of font increases letter identification efficiency. We review design concepts and the relevant literature, with the goal of stimulating further thinking about letter processing during reading

Precise measurement of the W-boson mass with the CDF II detector

We have measured the W-boson mass MW using data corresponding to 2.2/fb of integrated luminosity collected in proton-antiproton collisions at 1.96 TeV with the CDF II detector at the Fermilab Tevatron collider. Samples consisting of 470126 W->enu candidates and 624708 W->munu candidates yield the measurement MW = 80387 +- 12 (stat) +- 15 (syst) = 80387 +- 19 MeV. This is the most precise measurement of the W-boson mass to date and significantly exceeds the precision of all previous measurements combined

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns