A UAV Mission Hierarchy

In the following sections, each of the primary missions are decomposed into mission planning, management, and replanning segments in order to identify what the primary functions a human operator will need to perform. The goal is to understand what tasks/functions are common across different UAV missions and platforms in order to map the generalizability of any particular research project.Prepared for Charles River Analytic

Toddler-Inspired Visual Object Learning

Real-world learning systems have practical limitations on the quality and quantity of the training datasets that they can collect and consider. How should a system go about choosing a subset of the possible training examples that still allows for learning accurate, generalizable models? To help address this question, we draw inspiration from a highly efficient practical learning system: the human child. Using head-mounted cameras, eye gaze trackers, and a model of foveated vision, we collected first-person (egocentric) images that represents a highly accurate approximation of the "training data" that toddlers' visual systems collect in everyday, naturalistic learning contexts. We used state-of-the-art computer vision learning models (convolutional neural networks) to help characterize the structure of these data, and found that child data produce significantly better object models than egocentric data experienced by adults in exactly the same environment. By using the CNNs as a modeling tool to investigate the properties of the child data that may enable this rapid learning, we found that child data exhibit a unique combination of quality and diversity, with not only many similar large, high-quality object views but also a greater number and diversity of rare views. This novel methodology of analyzing the visual "training data" used by children may not only reveal insights to improve machine learning, but also may suggest new experimental tools to better understand infant learning in developmental psychology

The Impact of Heterogeneity on Operator Performance in Future Unmanned Vehicle Systems

Recent studies have shown that with appropriate operator decision support and with sufficient automation, inverting the multiple operators to single-unmanned vehicle control paradigm is possible. These studies, however, have generally focused on homogeneous teams of vehicles, and have not completely addressed either the manifestation of heterogeneity in vehicle teams, or the effects of heterogeneity on operator capacity. An important implication of heterogeneity in unmanned vehicle teams is an increase in the diversity of possible team configurations available for each operator, as well as an increase in the diversity of possible attention allocation schemes that can be utilized by operators. To this end, this paper introduces a discrete event simulation (DES) model as a means to model a single operator supervising multiple heterogeneous unmanned vehicles. The DES model can be used to understand the impact of varying both vehicle team design variables (such as team composition) and operator design variables (including attention allocation strategies). The model also highlights the sub-components of operator attention allocation schemes that can impact overall performance when supervising heterogeneous unmanned vehicle teams. Results from an experimental case study are then used to validate the model, and make predictions about operator performance for various heterogeneous team configurations.The research was supported by Charles River Analytics, the Office of Naval Research (ONR), and MIT Lincoln Laboratory

Dynamics of a lattice Universe

We find a solution to Einstein field equations for a regular toroidal lattice of size L with equal masses M at the centre of each cell; this solution is exact at order M/L. Such a solution is convenient to study the dynamics of an assembly of galaxy-like objects. We find that the solution is expanding (or contracting) in exactly the same way as the solution of a Friedman-Lema\^itre-Robertson-Walker Universe with dust having the same average density as our model. This points towards the absence of backreaction in a Universe filled with an infinite number of objects, and this validates the fluid approximation, as far as dynamics is concerned, and at the level of approximation considered in this work.Comment: 14 pages. No figure. Accepted version for Classical and Quantum Gravit

Evaluation of a Single Nucleotide Polymorphism Baseline for Genetic Stock Identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem

Chinook Salmon (Oncorhynchus tshawytscha) is an economically and ecologically important species, and populations from the west coast of North America are a major component of fisheries in the North Pacific Ocean. The anadromous life history strategy of this species generates populations (or stocks) that typically are differentiated from neighboring populations. In many cases, it is desirable to discern the stock of origin of an individual fish or the stock composition of a mixed sample to monitor the stock-specific effects of anthropogenic impacts and alter management strategies accordingly. Genetic stock identification (GSI) provides such discrimination, and we describe here a novel GSI baseline composed of genotypes from more than 8000 individual fish from 69 distinct populations at 96 single nucleotide polymorphism (SNP) loci. The populations included in this baseline represent the likely sources for more than 99% of the salmon encountered in ocean fisheries of California and Oregon. This new genetic baseline permits GSI with the use of rapid and cost-effective SNP genotyping, and power analyses indicate that it provides very accurate identification of important stocks of Chinook Salmon. In an ocean fishery sample, GSI assignments of more than 1000 fish, with our baseline, were highly concordant (98.95%) at the reporting unit level with information from the physical tags recovered from the same fish. This SNP baseline represents an important advance in the technologies available to managers and researchers of this species

Topology and Homoclinic Trajectories of Discrete Dynamical Systems

We show that nontrivial homoclinic trajectories of a family of discrete, nonautonomous, asymptotically hyperbolic systems parametrized by a circle bifurcate from a stationary solution if the asymptotic stable bundles Es(+{\infty}) and Es(-{\infty}) of the linearization at the stationary branch are twisted in different ways.Comment: 19 pages, canceled the appendix (Properties of the index bundle) in order to avoid any text overlap with arXiv:1005.207

Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.Comment: 10 pages, 7 figures, published in WACV 201

Repeated games for eikonal equations, integral curvature flows and non-linear parabolic integro-differential equations

The main purpose of this paper is to approximate several non-local evolution equations by zero-sum repeated games in the spirit of the previous works of Kohn and the second author (2006 and 2009): general fully non-linear parabolic integro-differential equations on the one hand, and the integral curvature flow of an interface (Imbert, 2008) on the other hand. In order to do so, we start by constructing such a game for eikonal equations whose speed has a non-constant sign. This provides a (discrete) deterministic control interpretation of these evolution equations. In all our games, two players choose positions successively, and their final payoff is determined by their positions and additional parameters of choice. Because of the non-locality of the problems approximated, by contrast with local problems, their choices have to "collect" information far from their current position. For integral curvature flows, players choose hypersurfaces in the whole space and positions on these hypersurfaces. For parabolic integro-differential equations, players choose smooth functions on the whole space

Rocaglates as dual-targeting agents for experimental cerebral malaria

Cerebral malaria (CM) is a severe and rapidly progressing complication of infection by Plasmodium parasites that is associated with high rates of mortality and morbidity. Treatment options are currently few, and intervention with artemisinin (Art) has limited efficacy, a problem that is compounded by the emergence of resistance to Art in Plasmodium parasites. Rocaglates are a class of natural products derived from plants of the Aglaia genus that have been shown to interfere with eukaryotic initiation factor 4A (eIF4A), ultimately blocking initiation of protein synthesis. Here, we show that the rocaglate CR-1-31B perturbs association of Plasmodium falciparum eIF4A (PfeIF4A) with RNA. CR-1-31B shows potent prophylactic and therapeutic antiplasmodial activity in vivo in mouse models of infection with Plasmodium berghei (CM) and Plasmodium chabaudi (blood-stage malaria), and can also block replication of different clinical isolates of P. falciparum in human erythrocytes infected ex vivo, including drug-resistant P. falciparum isolates. In vivo, a single dosing of CR-1-31B in P. berghei-infected animals is sufficient to provide protection against lethality. CR-1-31B is shown to dampen expression of the early proinflammatory response in myeloid cells in vitro and dampens the inflammatory response in vivo in P. berghei-infected mice. The dual activity of CR-1-31B as an antiplasmodial and as an inhibitor of the inflammatory response in myeloid cells should prove extremely valuable for therapeutic intervention in human cases of CM.We thank Susan Gauthier, Genevieve Perreault, and Patrick Senechal for technical assistance. This work was supported by a research grant (to P.G.) from the Canadian Institutes of Health Research (CIHR) (Foundation Grant). J.P. and P.G. are supported by a James McGill Professorship salary award. D.L. is supported by fellowships from the Fonds de recherche sante Quebec, the CIHR Neuroinflammation training program. J.P. is supported by CIHR Research Grant FDN-148366. M.S. is supported by a CIHR Foundation grant. J.A.P. is supported by NIH Grant R35 GM118173. Work at the Boston University Center for Molecular Discovery is supported by Grant R24 GM111625. K.C.K. was supported by a CIHR Foundation Grant and the Canada Research Chair program. (Canadian Institutes of Health Research (CIHR); James McGill Professorship salary award; Fonds de recherche sante Quebec; CIHR Neuroinflammation training program; FDN-148366 - CIHR Research Grant; CIHR Foundation grant; R35 GM118173 - NIH; Canada Research Chair program; R24 GM111625