Learning Structured Generative Concepts

Many real world concepts, such as “car”, “house”, and “tree”, are more than simply a collection of features. These objects are richly structured, defined in terms of systems of relations, subparts, and recursive embeddings. We describe an approach to concept representation and learning that attempts to capture such structured objects. This approach builds on recent proba- bilistic approaches, viewing concepts as generative processes, and on recent rule-based approaches, constructing concepts in- ductively from a language of thought. Concepts are modeled as probabilistic programs that describe generative processes; these programs are described in a compositional language. In an exploratory concept learning experiment, we investigate hu- man learning from sets of tree-like objects generated by pro- cesses that vary in their abstract structure, from simple proto- types to complex recursions. We compare human categoriza- tion judgements to predictions of the true generative process as well as a variety of exemplar-based heuristics

A Bayesian framework for cross-situational word-learning

For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use the social context of an utterance to infer the intended referent of a word. Here we present a Bayesian model of cross-situational word learning, and an extension of this model that also learns which social cues are relevant to determining reference. We test our model on a small corpus of mother-infant interaction and find it performs better than competing models. Finally, we show that our model accounts for experimental phenomena including mutual exclusivity, fast-mapping, and generalization from social cues

Beyond Boolean logic: exploring representation languages for learning complex concepts

We study concept learning for semantically-motivated, set-theoretic concepts. We first present an experiment in which we show that subjects learn concepts which cannot be represented by a simple Boolean logic. We then present a computational model which is similarly capable of learning these concepts,and show that it provides a good fit to human learning curves. Additionally, we compare the performance of several potential representation languages which are richer than Boolean logic in predicting human response distributions. Keywords: Rule-based concept learning; probabilistic model;semantics

The Infinite Latent Events Model

We present the Infinite Latent Events Model, a nonparametric hierarchical Bayesian distribution over infinite dimensional Dynamic Bayesian Networks with binary state representations and noisy-OR-like transitions. The distribution can be used to learn structure in discrete timeseries data by simultaneously inferring a set of latent events, which events fired at each timestep, and how those events are causally linked. We illustrate the model on a sound factorization task, a network topology identification task, and a video game task.NTT Communication Science LaboratoriesUnited States. Air Force Office of Scientific Research (AFOSR FA9550-07-1-0075)United States. Office of Naval Research (ONR N00014-07-1-0937)National Science Foundation (U.S.) (Graduate Research Fellowship)United States. Army Research Office (ARO W911NF-08-1-0242)James S. McDonnell Foundation (Causal Learning Collaborative Initiative

Assessing the Causal Impact of Chinese Aid on Vegetative Land Cover in Burundi and Rwanda Under Conditions of Spatial Imprecision

There has been considerable debate regarding the efficacy of international aid in meeting the dual goals of human development and environmental sustainability. Many donors have sought to engage with this challenge by introducing environmental safeguard and monitoring initiatives; however, evidence on the success of these interventions is limited. Evaluating aid is a particular challenge in the case of donors that do not disclose information on the nature, geographic location, or extents of their interventions. In such cases, new methods that extract and geoparse data on the activities of opaque donors through the manual interpretation of thousands of news and other articles allow us to investigate the impacts of these activities. However, residual spatial uncertainty in these data remains a potential source of bias. In this article, we apply and discuss a Geographic Simulation and Extrapolation (GeoSIMEX) approach to mitigate the spatial imprecision inherent in geoparsed data. In conjunction with GeoSIMEX, we test and contrast multiple approaches to reducing the imprecision of aid, including high-assumption cases in which other covariates (i.e., nighttime lights) are leveraged to allocate aid. In our application, we find that methods which do not account for spatial imprecision find statistically significant relationships between Chinese aid and vegetation change; after accounting for spatial uncertainty, findings are similar for Rwanda and inconclusive for Burundi

The Parallel Worm Tracker: A Platform for Measuring Average Speed and Drug-Induced Paralysis in Nematodes

Background Caenorhabditis elegans locomotion is a simple behavior that has been widely used to dissect genetic components of behavior, synaptic transmission, and muscle function. Many of the paradigms that have been created to study C. elegans locomotion rely on qualitative experimenter observation. Here we report the implementation of an automated tracking system developed to quantify the locomotion of multiple individual worms in parallel. Methodology/Principal Findings Our tracking system generates a consistent measurement of locomotion that allows direct comparison of results across experiments and experimenters and provides a standard method to share data between laboratories. The tracker utilizes a video camera attached to a zoom lens and a software package implemented in MATLAB®. We demonstrate several proof-of-principle applications for the tracker including measuring speed in the absence and presence of food and in the presence of serotonin. We further use the tracker to automatically quantify the time course of paralysis of worms exposed to aldicarb and levamisole and show that tracker performance compares favorably to data generated using a hand-scored metric. Conclusions/Signficance Although this is not the first automated tracking system developed to measure C. elegans locomotion, our tracking software package is freely available and provides a simple interface that includes tools for rapid data collection and analysis. By contrast with other tools, it is not dependent on a specific set of hardware. We propose that the tracker may be used for a broad range of additional worm locomotion applications including genetic and chemical screening

Enabling multiplexed testing of pooled donor cells through whole-genome sequencing

We describe a method that enables the multiplex screening of a pool of many different donor cell lines. Our method accurately predicts each donor proportion from the pool without requiring the use of unique DNA barcodes as markers of donor identity. Instead, we take advantage of common single nucleotide polymorphisms, whole-genome sequencing, and an algorithm to calculate the proportions from the sequencing data. By testing using simulated and real data, we showed that our method robustly predicts the individual proportions from a mixed-pool of numerous donors, thus enabling the multiplexed testing of diverse donor cells en masse.National Human Genome Research Institute (U.S.) (Grant RM1HG008525)Robert Wood Johnson Foundation (Grant 74178

Theory of Parabolic Arcs in Interstellar Scintillation Spectra

Our theory relates the secondary spectrum, the 2D power spectrum of the radio dynamic spectrum, to the scattered pulsar image in a thin scattering screen geometry. Recently discovered parabolic arcs in secondary spectra are generic features for media that scatter radiation at angles much larger than the rms scattering angle. Each point in the secondary spectrum maps particular values of differential arrival-time delay and fringe rate (or differential Doppler frequency) between pairs of components in the scattered image. Arcs correspond to a parabolic relation between these quantities through their common dependence on the angle of arrival of scattered components. Arcs appear even without consideration of the dispersive nature of the plasma. Arcs are more prominent in media with negligible inner scale and with shallow wavenumber spectra, such as the Kolmogorov spectrum, and when the scattered image is elongated along the velocity direction. The arc phenomenon can be used, therefore, to constrain the inner scale and the anisotropy of scattering irregularities for directions to nearby pulsars. Arcs are truncated by finite source size and thus provide sub micro arc sec resolution for probing emission regions in pulsars and compact active galactic nuclei. Multiple arcs sometimes seen signify two or more discrete scattering screens along the propagation path, and small arclets oriented oppositely to the main arc persisting for long durations indicate the occurrence of long-term multiple images from the scattering screen.Comment: 22 pages, 11 figures, submitted to the Astrophysical Journa

Rational optimization of tolC as a powerful dual selectable marker for genome engineering

Selection has been invaluable for genetic manipulation, although counter-selection has historically exhibited limited robustness and convenience. TolC, an outer membrane pore involved in transmembrane transport in E. coli, has been implemented as a selectable/counter-selectable marker, but counter-selection escape frequency using colicin E1 precludes using tolC for inefficient genetic manipulations and/or with large libraries. Here, we leveraged unbiased deep sequencing of 96 independent lineages exhibiting counter-selection escape to identify loss-of-function mutations, which offered mechanistic insight and guided strain engineering to reduce counter-selection escape frequency by ∼40-fold. We fundamentally improved the tolC counter-selection by supplementing a second agent, vancomycin, which reduces counter-selection escape by 425-fold, compared colicin E1 alone. Combining these improvements in a mismatch repair proficient strain reduced counter-selection escape frequency by 1.3E6-fold in total, making tolC counter-selection as effective as most selectable markers, and adding a valuable tool to the genome editing toolbox. These improvements permitted us to perform stable and continuous rounds of selection/counter-selection using tolC, enabling replacement of 10 alleles without requiring genotypic screening for the first time. Finally, we combined these advances to create an optimized E. coli strain for genome engineering that is ∼10-fold more efficient at achieving allelic diversity than previous best practices