Learning deep dynamical models from image pixels

Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement mapping and the transition mapping (system dynamics) in latent space can be challenging. For linear system dynamics and measurement mappings efficient solutions for system identification are available. However, in practical applications, the linearity assumptions does not hold, requiring non-linear system identification techniques. If additionally the observations are high-dimensional (e.g., images), non-linear system identification is inherently hard. To address the problem of non-linear system identification from high-dimensional observations, we combine recent advances in deep learning and system identification. In particular, we jointly learn a low-dimensional embedding of the observation by means of deep auto-encoders and a predictive transition model in this low-dimensional space. We demonstrate that our model enables learning good predictive models of dynamical systems from pixel information only.Comment: 10 pages, 11 figure

Accounting for spatial substitution patterns and bioeconomic feedback loops: an economic approach to managing inland recreational fisheries

2011 Summer.Includes bibliographical references.This dissertation consists of three papers which address separate but related issues in recreational fisheries management. Paper one estimates the economic contribution of the private, recreation-based aquaculture industry in the Western United States. Paper two presents a method for combining models of site selection with input-output models in order to better estimate the true economic impacts of augmentation or deterioration of recreational sites. Finally, paper three presents a dynamic, bioeconomic model of a recreational fishery and uses that model to simulate what would happen over time to anglers and fish populations (as well as value to anglers) if fish stocking were to be halted at a single recreational fishery. All three papers are policy relevant today given the increased pressure from (and litigation filed by) environmental groups to reduce fish stocking due to conflicts with native and endangered species. Paper one explores the economic contribution of the private, recreation-based aquaculture industry in the Western United States. New sectors are constructed in IMPLAN input-output software using data gathered between 2007 and 2010 from producers and their direct customers (stocked fisheries). Information from a third survey of anglers in Colorado and California is integrated to predict the short-term shocks that would occur to various industries if anglers at privately stocked fisheries were to discontinue fishing (simulating a hypothetical collapse of the industry). Accounting for both the backward and forward linkages of the private, recreation-based aquaculture industry's production, model results indicate that for every dollar of fish stocking, $36 dollars of recreational angler-related expenditures are supported, and that the total economic contribution of this industry in the Western United States is roughly$2 billion annually. This is the first study addressing the forward linkages and total economic contribution of this industry in the Western United States. Paper two addresses a similar issue as paper one, but goes further to account for substitution patterns among anglers. Using information from a survey of anglers in 2009, a repeated nested logit (RNL) model of angler spatial substitution behavior is estimated. Then, the RNL is used to predict changes in angler days associated with changes in fishery attributes. By linking the RNL and input-output model, better insight is gained into the economic losses associated with augmentation or deterioration of stocked fishing sites. Results indicate that if a single site is closed within the region of analysis, of the 29,500 anglers that will no longer fish at that site, only 6,500 anglers will leave the region of analysis (the rest substituting to other in-region sites). Standard impact analysis would therefore overestimate the economic impacts of such a policy by 450%. Results are similar when catch rates are reduced by 50% at one site, with 14,000 anglers leaving that site but only 3,000 leaving the region. The third and final paper of this dissertation presents a means by which managers may manage inland recreational fisheries from a dynamic bioeconomic perspective. A discrete-time, discrete-space, infinite time horizon numerical model of a fishery is built in GAMS software to reflect responses of anglers to the fishery and responses of the fishery to anglers over time. A data-driven random utility model is used to inform angler response and value functions in this dynamic bioeconomic model. Results from one region in California indicate that a) current fish stocking levels may be inefficiently high, and b) elimination of fish stocking programs at popular lakes may not lead to a crash in fishery populations, since anglers will simply substitute to other nearby fisheries (rather than "fish-out" the lake). Managers who can predict the intertemporal effects of fishery management alternatives in this way will be able to better meet the demands of recreational anglers

Total economic contribution of the private, recreation-based aquaculture industry in the West: a summary, The

April 2012.Includes bibliographical references (page 8).The private, recreation-based aquaculture industry (often referred to as the aquacultural suppliers of recreational fish, or ASRF industry) provides fish for recreational outlets such as private fishing clubs and dude ranches, public reservoirs and streams, and private backyard ponds. Although most people know about the public stocking agencies such as the United States Fish and Wildlife Service and state-level wild-life agencies, no information about the economic scope or contribution of the ASRF in the Western United States has been documented. In order to address this gap in the literature, between 2007 and 2010, researchers in the Department of Agricultural and Resource Economics at Colorado State University and at UC Davis collected data from ASRF producers, their direct customers (recreational fisheries), and recreational anglers in the Western -- United States This data was compiled and, in conjunction with IMPLAN input-output software, was used to estimate the economic contribution of the ASRF industry in that region. The results of this exercise are summarized here in order to increase exposure to the general public

July 2011 Environmental management and policy report, no. 1

July 2011.Includes bibliographical references

Orthogonally Decoupled Variational Gaussian Processes

Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities have presented a long-standing challenge. State-of-the-art sparse variational inference methods trade modeling accuracy against complexity. However, the complexities of these methods still scale superlinearly in the number of basis functions, implying that that sparse GP methods are able to learn from large datasets only when a small model is used. Recently, a decoupled approach was proposed that removes the unnecessary coupling between the complexities of modeling the mean and the covariance functions of a GP. It achieves a linear complexity in the number of mean parameters, so an expressive posterior mean function can be modeled. While promising, this approach suffers from optimization difficulties due to ill-conditioning and non-convexity. In this work, we propose an alternative decoupled parametrization. It adopts an orthogonal basis in the mean function to model the residues that cannot be learned by the standard coupled approach. Therefore, our method extends, rather than replaces, the coupled approach to achieve strictly better performance. This construction admits a straightforward natural gradient update rule, so the structure of the information manifold that is lost during decoupling can be leveraged to speed up learning. Empirically, our algorithm demonstrates significantly faster convergence in multiple experiments

Bayesian Optimization with Dimension Scheduling: Application to Biological Systems

Bayesian Optimization (BO) is a data-efficient method for global black-box optimization of an expensive-to-evaluate fitness function. BO typically assumes that computation cost of BO is cheap, but experiments are time consuming or costly. In practice, this allows us to optimize ten or fewer critical parameters in up to 1,000 experiments. But experiments may be less expensive than BO methods assume: In some simulation models, we may be able to conduct multiple thousands of experiments in a few hours, and the computational burden of BO is no longer negligible compared to experimentation time. To address this challenge we introduce a new Dimension Scheduling Algorithm (DSA), which reduces the computational burden of BO for many experiments. The key idea is that DSA optimizes the fitness function only along a small set of dimensions at each iteration. This DSA strategy (1) reduces the necessary computation time, (2) finds good solutions faster than the traditional BO method, and (3) can be parallelized straightforwardly. We evaluate the DSA in the context of optimizing parameters of dynamic models of microalgae metabolism and show faster convergence than traditional BO

Probabilistic movement modeling for intention inference in human-robot interaction.

Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.

OGFOD1 catalyzes prolyl hydroxylation of RPS23 and is involved in translation control and stress granule formation

2-Oxoglutarate (2OG) and Fe(II)-dependent oxygenase domain-containing protein 1 (OGFOD1) is predicted to be a conserved 2OG oxygenase, the catalytic domain of which is related to hypoxia-inducible factor prolyl hydroxylases. OGFOD1 homologs in yeast are implicated in diverse cellular functions ranging from oxygen-dependent regulation of sterol response genes (Ofd1, Schizosaccharomyces pombe) to translation termination/mRNA polyadenylation (Tpa1p, Saccharomyces cerevisiae). However, neither the biochemical activity of OGFOD1 nor the identity of its substrate has been defined. Here we show that OGFOD1 is a prolyl hydroxylase that catalyzes the posttranslational hydroxylation of a highly conserved residue (Pro-62) in the small ribosomal protein S23 (RPS23). Unusually OGFOD1 retained a high affinity for, and forms a stable complex with, the hydroxylated RPS23 substrate. Knockdown or inactivation of OGFOD1 caused a cell type-dependent induction of stress granules, translational arrest, and growth impairment in a manner complemented by wild-type but not inactive OGFOD1. The work identifies a human prolyl hydroxylase with a role in translational regulation

Rare germline variants in DNA repair genes and the angiogenesis pathway predispose prostate cancer patients to develop metastatic disease

Background Prostate cancer (PrCa) demonstrates a heterogeneous clinical presentation ranging from largely indolent to lethal. We sought to identify a signature of rare inherited variants that distinguishes between these two extreme phenotypes. Methods We sequenced germline whole exomes from 139 aggressive (metastatic, age of diagnosis < 60) and 141 non-aggressive (low clinical grade, age of diagnosis ≥60) PrCa cases. We conducted rare variant association analyses at gene and gene set levels using SKAT and Bayesian risk index techniques. GO term enrichment analysis was performed for genes with the highest differential burden of rare disruptive variants. Results Protein truncating variants (PTVs) in specific DNA repair genes were significantly overrepresented among patients with the aggressive phenotype, with BRCA2, ATM and NBN the most frequently mutated genes. Differential burden of rare variants was identified between metastatic and non-aggressive cases for several genes implicated in angiogenesis, conferring both deleterious and protective effects. Conclusions Inherited PTVs in several DNA repair genes distinguish aggressive from non-aggressive PrCa cases. Furthermore, inherited variants in genes with roles in angiogenesis may be potential predictors for risk of metastases. If validated in a larger dataset, these findings have potential for future clinical application

A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area