Breaks, bubbles, booms, and busts: the evolution of primary commodity price fundamentals

This paper explores the behavior of real commodity prices over a 50–year period. Attention is given to how the fundamentals for various commodity prices have changed with a special emphasis on behavior since the mid 2000s. To identify changing commodity price fundamentals we estimate shifting–mean autoregressions by using: the Bai and Perron (1998) procedure for estimating structural breaks; a SlowShift procedure that specifies intercepts to be nonlinear, potentially smooth functions of time; and low frequency Fourier functions. We find that the pattern in the timing of the various shifts is suggestive of the causal fundamentals underlying the recent boom.Commodity Prices, Fundamentals, Nonlinear Trends, Shifting--Mean Autoregression

Learning Articulated Motions From Visual Demonstration

Many functional elements of human homes and workplaces consist of rigid components which are connected through one or more sliding or rotating linkages. Examples include doors and drawers of cabinets and appliances; laptops; and swivel office chairs. A robotic mobile manipulator would benefit from the ability to acquire kinematic models of such objects from observation. This paper describes a method by which a robot can acquire an object model by capturing depth imagery of the object as a human moves it through its range of motion. We envision that in future, a machine newly introduced to an environment could be shown by its human user the articulated objects particular to that environment, inferring from these "visual demonstrations" enough information to actuate each object independently of the user. Our method employs sparse (markerless) feature tracking, motion segmentation, component pose estimation, and articulation learning; it does not require prior object models. Using the method, a robot can observe an object being exercised, infer a kinematic model incorporating rigid, prismatic and revolute joints, then use the model to predict the object's motion from a novel vantage point. We evaluate the method's performance, and compare it to that of a previously published technique, for a variety of household objects.Comment: Published in Robotics: Science and Systems X, Berkeley, CA. ISBN: 978-0-9923747-0-

Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences

We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the observable world state. We introduce a multi-level aligner that empowers our model to focus on sentence "regions" salient to the current world state by using multiple abstractions of the input sentence. In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers) or task-specific annotations (e.g., seed lexicons). It is therefore generalizable, yet still achieves the best results reported to-date on a benchmark single-sentence dataset and competitive results for the limited-training multi-sentence setting. We analyze our model through a series of ablations that elucidate the contributions of the primary components of our model.Comment: To appear at AAAI 2016 (and an extended version of a NIPS 2015 Multimodal Machine Learning workshop paper

Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Mitochondrial protein import

The precursor of F0-ATPase subunit 9 was bound to mitochondria in the absence of a mitochondrial membrane potential (delta psi). Binding was mediated by a protease-sensitive component on the mitochondrial surface. When delta psi was reestablished, bound precursor was directly imported without prior release from the mitochondrial membranes. A chimaeric protein consisting of the complete subunit 9 precursor fused to cytosolic dihydrofolate reductase (DHFR) was also specifically bound to mitochondria in the absence of delta psi. Two other fusion proteins, consisting either of the entire presequence of subunit 9 and DHFR or of part of the presequence and DHFR, were imported in the presence of delta psi. In the absence of delta psi, however, specific binding to mitochondria did not take place. We suggest that the hydrophobic mature part of subunit 9 is involved in the delta psi-independent binding of the subunit 9 precursor to receptor sites on the mitochondrial surface