Large-Scale Neural Systems for Vision and Cognition

— Consideration of how people respond to the question What is this? has suggested new problem frontiers for pattern recognition and information fusion, as well as neural systems that embody the cognitive transformation of declarative information into relational knowledge. In contrast to traditional classification methods, which aim to find the single correct label for each exemplar (This is a car), the new approach discovers rules that embody coherent relationships among labels which would otherwise appear contradictory to a learning system (This is a car, that is a vehicle, over there is a sedan). This talk will describe how an individual who experiences exemplars in real time, with each exemplar trained on at most one category label, can autonomously discover a hierarchy of cognitive rules, thereby converting local information into global knowledge. Computational examples are based on the observation that sensors working at different times, locations, and spatial scales, and experts with different goals, languages, and situations, may produce apparently inconsistent image labels, which are reconciled by implicit underlying relationships that the network’s learning process discovers. The ARTMAP information fusion system can, moreover, integrate multiple separate knowledge hierarchies, by fusing independent domains into a unified structure. In the process, the system discovers cross-domain rules, inferring multilevel relationships among groups of output classes, without any supervised labeling of these relationships. In order to self-organize its expert system, the ARTMAP information fusion network features distributed code representations which exploit the model’s intrinsic capacity for one-to-many learning (This is a car and a vehicle and a sedan) as well as many-to-one learning (Each of those vehicles is a car). Fusion system software, testbed datasets, and articles are available from http://cns.bu.edu/techlab.Defense Advanced Research Projects Research Agency (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC subcontract 801881-BS under prime contract HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (SBE-0354378

Guiding InfoGAN with Semi-Supervision

In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts of labeled data speeds-up training convergence. The architecture maintains the ability to disentangle latent variables for which no labels are available. Finally, we contribute an information-theoretic reasoning on how introducing semi-supervision increases mutual information between synthetic and real data

Transition from an electron solid to the sequence of fractional quantum Hall states at very low Landau level filling factor

At low Landau level filling of a two-dimensional electron system, typically associated with the formation of an electron crystal, we observe local minima in Rxx at filling factors nu=2/11, 3/17, 3/19, 2/13, 1/7, 2/15, 2/17, and 1/9. Each of these developing fractional quantum Hall (FQHE) states appears only above a filling factor-specific temperature. This can be interpreted as the melting of an electron crystal and subsequent FQHE liquid formation. The observed sequence of FQHE states follow the series of composite fermion states emanating from nu=1/6 and nu=1/8

Revealing accretion onto black holes: X-ray reflection throughout three outbursts of GX 339-4

Understanding the dynamics behind black hole state transitions and the changes they reflect in outbursts has become long-standing problem. The X-ray reflection spectrum describes the interaction between the hard X-ray source (the power-law continuum) and the cool accretion disc it illuminates, and thus permits an indirect view of how the two evolve. We present a systematic analysis of the reflection spectrum throughout three outbursts (500+ observations) of the black hole binary GX 339-4, representing the largest study applying a self-consistent treatment of reflection to date. Particular attention is payed to the coincident evolution of the power-law and reflection, which can be used to determine the accretion geometry. The hard state is found to be distinctly reflection weak, however the ratio of reflection to power-law gradually increases as the source luminosity rises. In contrast the reflection is found dominate the power-law throughout most of the soft state, with increasing supremacy as the source decays. We discuss potential dynamics driving this, favouring inner disc truncation and decreasing coronal height for the hard and soft states respectively. Evolution of the ionisation parameter, power-law slope and high-energy cut-off also agree with this interpretation.Comment: Accepted for publication in MNRA