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This thesis explores the concept of care and counsel for the family within the early Christian movement of the first and second centuries. Following an introduction into the placement of the inquiry within the broader scholarly scene, methodology, and scope of research, a background into ‘family’ within the broader Greco-Roman culture of the period is detailed. Next, early Christian sources from the first and second century are examined with a view towards ‘family’ and familial instruction. Then, two subsequent chapters are given where the larger Greco-Roman rhetorical structure for counsel and care known as psychagogy (soul guidance) is examined followed by a description of the themes of psychagogic content related to family observed within the Christian literature of the period. “Paraenetic-psychagogy” becomes a particular paradigm coined within the thesis for placing the structure of Christian counsel and care for the family. First, the broader data regarding family within the Greco-Roman culture of the period is given alonside the data available regarding Christian expectations for family. Secondly, forms of rhetoric used in giving counsel are examined wherein concepts like paraenesis, protrepsis, and psychagogy are detailed. Thirdly, the pastoral care discussions are demonstrated, one example being that of Ignatius. Lastly, early Christian familial psychagogy is compared to that of contemporary Stoic philosopher Musonius Rufus wherein a divergence of telos is noted within the psychagogy. These all serve as a broader background upon which a detailed examination of the early Christian primary source documents can occur. In the end, the focus will be on the ideals or expectations of the various Christian writers in the sources versus a claim of fact about what all supposed Christian households definitively looked like. Ultimately, the thesis argues that Christian care for the family was a part of the larger psychagogic enterprise. This argument is augmente

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.

This study provided unique insight into the Australian consumer’s adoption of electric vehicles. The research applied a novel framework, jointly exploring psychological constructs, such as attitudes, in conjunction with vehicle attributes and sociodemographic characteristics. The findings highlight the central role of psychological constructs in decision making and electric vehicle adoption.

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014–2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as “The Cannon”; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V., Peer Reviewed

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014-2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as ¿The Cannon¿ and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V. © 2018. The American Astronomical Society..

Solving tasks with sparse rewards is a main challenge in reinforcement learning. While hierarchical controllers are an intuitive approach to this problem, current methods often require manual reward shaping, alternating training phases, or manually defined sub tasks. We introduce modulated policy hierarchies (MPH), that can learn end-to-end to solve tasks from sparse rewards. To achieve this, we study different modulation signals and exploration for hierarchical controllers. Specifically, we find that communicating via bit-vectors is more efficient than selecting one out of multiple skills, as it enables mixing between them. To facilitate exploration, MPH uses its different time scales for temporally extended intrinsic motivation at each level of the hierarchy. We evaluate MPH on the robotics tasks of pushing and sparse block stacking, where it outperforms recent baselines., Comment: 8 pages, 5 figures

Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especially in image-based domains. We propose the Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space. To achieve high performance, the dynamics model must accurately predict the rewards ahead for multiple time steps. We approach this using a latent dynamics model with both deterministic and stochastic transition components. Moreover, we propose a multi-step variational inference objective that we name latent overshooting. Using only pixel observations, our agent solves continuous control tasks with contact dynamics, partial observability, and sparse rewards, which exceed the difficulty of tasks that were previously solved by planning with learned models. PlaNet uses substantially fewer episodes and reaches final performance close to and sometimes higher than strong model-free algorithms., Comment: 20 pages, 12 figures, 1 table

Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of an independent normal prior in weight space imposes relatively weak constraints on the function posterior, allowing it to generalize in unforeseen ways on inputs outside of the training distribution. We propose noise contrastive priors (NCPs) to obtain reliable uncertainty estimates. The key idea is to train the model to output high uncertainty for data points outside of the training distribution. NCPs do so using an input prior, which adds noise to the inputs of the current mini batch, and an output prior, which is a wide distribution given these inputs. NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training. Empirically, we show that NCPs prevent overfitting outside of the training distribution and result in uncertainty estimates that are useful for active learning. We demonstrate the scalability of our method on the flight delays data set, where we significantly improve upon previously published results., Comment: 12 pages, 6 figures

Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed? Interestingly, in most cases humans can predict the effects of similar collisions with different conditions such as changes in mass, friction, etc. It is postulated this is primarily because we learn to model physics with meaningful latent variables. This does not imply we can estimate the precise values of these meaningful variables (estimate exact values of mass or friction). Inspired by this observation, we propose an interpretable intuitive physics model where specific dimensions in the bottleneck layers correspond to different physical properties. In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.) and test on collisions of unseen combinations of shapes. Furthermore, we demonstrate our model generalizes well even when similar scenes are simulated with different underlying properties.

What is a good visual representation for autonomous agents? We address this question in the context of semantic visual navigation, which is the problem of a robot finding its way through a complex environment to a target object, e.g. go to the refrigerator. Instead of acquiring a metric semantic map of an environment and using planning for navigation, our approach learns navigation policies on top of representations that capture spatial layout and semantic contextual cues. We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy. This choice allows using additional data, from orthogonal sources, to better train different parts of the model the representation extraction is trained on large standard vision datasets while the navigation component leverages large synthetic environments for training. This combination of real and synthetic is possible because equitable feature representations are available in both (e.g., segmentation and detection masks), which alleviates the need for domain adaptation. Both the representation and the navigation policy can be readily applied to real non-synthetic environments as demonstrated on the Active Vision Dataset [1]. Our approach gets successfully to the target in 54% of the cases in unexplored environments, compared to 46% for non-learning based approach, and 28% for the learning-based baseline., Comment: Accepted to ICRA 2019 and ECCV 2018 Workshop on Visual Learning and Embodied Agents in Simulation Environments

Goals for reinforcement learning problems are typically defined through hand-specified rewards. To design such problems, developers of learning algorithms must inherently be aware of what the task goals are, yet we often require agents to discover them on their own without any supervision beyond these sparse rewards. While much of the power of reinforcement learning derives from the concept that agents can learn with little guidance, this requirement greatly burdens the training process. If we relax this one restriction and endow the agent with knowledge of the reward function, and in particular of the goal, we can leverage backwards induction to accelerate training. To achieve this, we propose training a model to learn to take imagined reversal steps from known goal states. Rather than training an agent exclusively to determine how to reach a goal while moving forwards in time, our approach travels backwards to jointly predict how we got there. We evaluate our work in Gridworld and Towers of Hanoi and empirically demonstrate that it yields better performance than standard DDQN.

We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in parallel, and group them to perform the neural network computation on a batch rather than individual observations. This allows the TensorFlow execution engine to parallelize computation, without the need for manual synchronization. Environments are stepped in separate Python processes to progress them in parallel without interference of the global interpreter lock. As part of this project, we introduce BatchPPO, an efficient implementation of the proximal policy optimization algorithm. By open sourcing TensorFlow Agents, we hope to provide a flexible starting point for future projects that accelerates future research in the field., Comment: White paper, 7 pages

This paper focuses on the problem of learning 6-DOF grasping with a parallel jaw gripper in simulation. We propose the notion of a geometry-aware representation in grasping based on the assumption that knowledge of 3D geometry is at the heart of interaction. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. Specifically, we formulate the learning of deep geometry-aware grasping model in two steps: First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10 percent relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances., Comment: Published at ICRA 2018

Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning. In this study, we ask whether sample complexity can be reduced for systems where the structure of the domain is unknown beforehand, and the structure and parameters must both be learned from the data. We show that sample complexity reduction through learning structure is possible for at least two simple cases. In studying these cases, we also gain insight into how this might be done for more complex domains., Comment: Accepted for ICML 2017 Workshop on Picky Learners

We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routing center, endowing the modules with the flexibility to share features over multiple time steps. We show that our model learns to route information hierarchically, processing input data by a chain of modules. We observe common architectures, such as feed forward neural networks and skip connections, emerging as special cases of our architecture, while novel connectivity patterns are learned for the text8 compression task. Our model outperforms standard recurrent neural networks on several sequential benchmarks., Comment: NIPS 2017

It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that predict one dimension at a time. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.

Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based approaches fail to generalize since: (a) the gap between simulation and real world is so large that policy-learning approaches fail to transfer; (b) even if policy learning is done in real world, the data scarcity leads to failed generalization from training to test scenarios (e.g., due to different friction or object masses). Inspired from H-infinity control methods, we note that both modeling errors and differences in training and test scenarios can be viewed as extra forces/disturbances in the system. This paper proposes the idea of robust adversarial reinforcement learning (RARL), where we train an agent to operate in the presence of a destabilizing adversary that applies disturbance forces to the system. The jointly trained adversary is reinforced -- that is, it learns an optimal destabilization policy. We formulate the policy learning as a zero-sum, minimax objective function. Extensive experiments in multiple environments (InvertedPendulum, HalfCheetah, Swimmer, Hopper and Walker2d) conclusively demonstrate that our method (a) improves training stability; (b) is robust to differences in training/test conditions; and c) outperform the baseline even in the absence of the adversary., Comment: 10 pages

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. We train and test CMP on navigation problems in simulation environments derived from scans of real world buildings. Our experiments demonstrate that CMP outperforms alternate learning-based architectures, as well as, classical mapping and path planning approaches in many cases. Furthermore, it naturally extends to semantically specified goals, such as 'going to a chair'. We also deploy CMP on physical robots in indoor environments, where it achieves reasonable performance, even though it is trained entirely in simulation., Comment: Extended IJCV Version of the original paper at CVPR17. Project website with code, models, simulation environment and videos: https://sites.google.com/view/cognitive-mapping-and-planning

We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology. Next, the sampling-based planners provide roadmaps which connect robot configurations that can be successfully navigated by the RL agent. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are constructed using feature-based and deep neural net policies in continuous state and action spaces. We evaluate PRM-RL, both in simulation and on-robot, on two navigation tasks with non-trivial robot dynamics: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments with load displacement constraints. Our results show improvement in task completion over both RL agents on their own and traditional sampling-based planners. In the indoor navigation task, PRM-RL successfully completes up to 215 m long trajectories under noisy sensor conditions, and the aerial cargo delivery completes flights over 1000 m without violating the task constraints in an environment 63 million times larger than used in training., Comment: 9 pages, 7 figures

Vanessa Zann,1 John McDermott,1 Jeffrey W Jacobs,2 James P Davidson,2 Fangling Lin,2 Paul Korner,2 Robert C Blanks,2 David P Rosenbaum2 1Quotient Clinical, Nottingham, UK; 2Ardelyx Inc., Fremont, CA, USA Background: Hyperkalemia is a potentially life-threatening condition that patients with heart failure or chronic kidney disease, especially those taking renin–angiotensin–aldosterone system inhibitors, are at high risk of developing. Sodium polystyrene sulfonate (SPS), a current treatment, binds potassium within the gastrointestinal tract to reduce potassium absorption. However, poor palatability limits its long-term use. RDX7675, a novel potassium binder in development for the treatment of hyperkalemia, is a calcium salt of a reengineered polystyrene sulfonate-based resin designed to have enhanced palatability. Here, the physical properties and palatability of RDX7675 and SPS are compared.Methods: RDX7675 and SPS particle sizes were measured using wet dispersion laser diffraction. Palatability was assessed in a randomized, crossover, healthy volunteer study with two visits. At visit 1 (open label), volunteers evaluated high-viscosity, intermediate-viscosity, and water-reconstituted formulations of RDX7675 (all vanilla flavor), and an equivalent reconstituted SPS (Resonium A®). At visit 2 (single-blind), volunteers evaluated RDX7675 as a high-viscosity formulation in vanilla, citrus, and mint flavors, and as intermediate-viscosity, low-viscosity, and reconstituted formulations in citrus flavor. Volunteers used a “sip and spit” technique to rate overall acceptability and seven individual characteristics from 1 (“dislike everything”) to 9 (“like extremely”).Results: RDX7675 particles were smaller than SPS particles, with a narrower size range (RDX7675, 80%, 14–52 µm; SPS, 11.3–124.2 µm), and had a smooth, spherical shape, in contrast to the s

There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots., Comment: Submission to ICRA 2017

We present results from a study of the eclipsing, colliding-wind binary V444 Cyg that uses a combination of X-ray and optical spectropolarimetric methods to describe the 3-D nature of the shock and wind structure within the system. We have created the most complete X-ray light curve of V444 Cyg to date using 40 ksec of new data from Swift, and 200 ksec of new and archived XMM-Newton observations. In addition, we have characterized the intrinsic, polarimetric phase-dependent behavior of the strongest optical emission lines using data obtained with the University of Wisconsin's Half-Wave Spectropolarimeter. We have detected evidence of the Coriolis distortion of the wind-wind collision in the X-ray regime, which manifests itself through asymmetric behavior around the eclipses in the system's X-ray light curves. The large opening angle of the X-ray emitting region, as well as its location (i.e. the WN wind does not collide with the O star, but rather its wind) are evidence of radiative braking/inhibition occurring within the system. Additionally, the polarimetric results show evidence of the cavity the wind-wind collision region carves out of the Wolf-Rayet star's wind., Comment: 19 pages, 14 figures, accepted A&A

A better, more effective dialogue is needed between biodiversity science and policy to underpin the sustainable use and conservation of biodiversity. Many initiatives exist to improve communication, but these largely conform to a ‘linear’ or technocratic model of communication in which scientific “facts” are transmitted directly to policy advisers to “solve problems”. While this model can help start a dialogue, it is, on its own, insufficient, as decision taking is complex, iterative and often selective in the information used. Here, we draw on the literature, interviews and a workshop with individuals working at the interface between biodiversity science and government policy development to present practical recommendations aimed at individuals, teams, organisations and funders. Building on these recommendations, we stress the need to: (a) frame research and policy jointly; (b) promote inter- and trans-disciplinary research and “multi-domain” working groups that include both scientists and policy makers from various fields and sectors; (c) put in place structures and incentive schemes that support interactive dialogue in the long-term. These are changes that are needed in light of continuing loss of biodiversity and its consequences for societal dependence on and benefits from nature.

We present results from a study of the eclipsing, colliding-wind binary V444 Cyg that uses a combination of X-ray and optical spectropolarimetric methods to describe the 3-D nature of the shock and wind structure within the system. We have created the most complete X-ray light curve of V444 Cyg to date using 40 ksec of new data from Swift, and 200 ksec of new and archived XMM-Newton observations. In addition, we have characterized the intrinsic, polarimetric phase-dependent behavior of the strongest optical emission lines using data obtained with the University of Wisconsin's Half-Wave Spectropolarimeter. We have detected evidence of the Coriolis distortion of the wind-wind collision in the X-ray regime, which manifests itself through asymmetric behavior around the eclipses in the system's X-ray light curves. The large opening angle of the X-ray emitting region, as well as its location (i.e. the WN wind does not collide with the O star, but rather its wind) are evidence of radiative braking/inhibition occurring within the system. Additionally, the polarimetric results show evidence of the cavity the wind-wind collision region carves out of the Wolf-Rayet star's wind., Comment: 19 pages, 14 figures, accepted A&A

A better, more effective dialogue is needed between biodiversity science and policy to underpin the sustainable use and conservation of biodiversity. Many initiatives exist to improve communication, but these largely conform to a ‘linear’ or technocratic model of communication in which scientific “facts” are transmitted directly to policy advisers to “solve problems”. While this model can help start a dialogue, it is, on its own, insufficient, as decision taking is complex, iterative and often selective in the information used. Here, we draw on the literature, interviews and a workshop with individuals working at the interface between biodiversity science and government policy development to present practical recommendations aimed at individuals, teams, organisations and funders. Building on these recommendations, we stress the need to: (a) frame research and policy jointly; (b) promote inter- and trans-disciplinary research and “multi-domain” working groups that include both scientists and policy makers from various fields and sectors; (c) put in place structures and incentive schemes that support interactive dialogue in the long-term. These are changes that are needed in light of continuing loss of biodiversity and its consequences for societal dependence on and benefits from nature.

This report presents the results of an investigation involving finite element simulation of partially grouted concrete masonry walls subjected to blast loading and the development of an engineering design equation to address the potential for breaching between grouted cells. Tests performed by the Air Force Research Laboratory were used to verify finite element modeling approach. Input parameter studies were carried out to understand the mechanisms and causes of the breaching shear in concrete masonry walls. Based upon the mechanism findings, a design shear equation was formulated, and a maximum pressure for partially grouted construction was defined., Prepared in cooperation with Auburn University, Department of Civil Engineering, Auburn, AL.

We study the effect of spatial structure, genetic drift, mutation, and selective pressure on the evolutionary dynamics in a simplified model of asexual organisms colonizing a new territory. Under an appropriate coarse-graining, the evolutionary dynamics is related to the directed percolation processes that arise in voter models, the Domany-Kinzel (DK) model, contact process, and so on. We explore the differences between linear (flat front) expansions and the much less familiar radial (curved front) range expansions. For the radial expansion, we develop a generalized, off-lattice DK model that minimizes otherwise persistent lattice artifacts. With both simulations and analytical techniques, we study the survival probability of advantageous mutants, the spatial correlations between domains of neutral strains, and the dynamics of populations with deleterious mutations. “Inflation” at the frontier leads to striking differences between radial and linear expansions. For a colony with initial radius R[subscript 0] expanding at velocity v, significant genetic demixing, caused by local genetic drift, occurs only up to a finite time t[superscript *]=R[subscript 0]/v, after which portions of the colony become causally disconnected due to the inflating perimeter of the expanding front. As a result, the effect of a selective advantage is amplified relative to genetic drift, increasing the survival probability of advantageous mutants. Inflation also modifies the underlying directed percolation transition, introducing novel scaling functions and modifications similar to a finite-size effect. Finally, we consider radial range expansions with deflating perimeters, as might arise from colonization initiated along the shores of an island., National Science Foundation (U.S.) (Grant 1005289), National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Grant DMR 0820484)

This report discusses simulation methodologies used to analyze large deflection static and dynamic behavior of foam-insulated concrete sandwich wall panels. Both conventionally reinforced cast-on-site panels and precast/prestressed panels were considered. The experimental program used for model development and validation involved component-level testing as well as both static and dynamic testing of full-scale wall panels. The static experiments involved single spans and double spans subjected to near-uniform distributed loading. The dynamic tests involved spans up to 30 ft tall that were subjected to impulse loads generated by an external explosion. Primary modeling challenges included: (1) accurately simulating prestressing initial conditions in an explicit dynamic code framework, (2) simulating the concrete, reinforcement, and foam insulation in the high strain rate environment, and (3) simulating shear transfer between wythes, including frictional slippage and connector rupture. Correlation challenges, conclusions and recommendations regarding efficient and accurate modeling techniques are highlighted. The modeling methodologies developed were used to conduct additional behavioral studies and help to assess single-degree-of-freedom prediction methodology developed for foam-insulated precast/prestressed sandwich panels for blast loads., Prepared in collaboration with Jacobs ASG, Fort Walton Beach, FL and the Airbase Technologies Division, Air Force Research Laboratory, Tyndall Air Force Base, FL. Document contains color images.

This paper summarizes recent testing conducted as part of a collaborative research program among the Air Force Research Laboratory (AFRL), the Portland Cement Association (PCA), and the National Concrete Masonry Association (NCMA). The stated objective of the broader cooperative research program is "to develop blast protection data for concrete building products typically used in construction and to develop improvements to these designs as needed to improve blast resistance." The most recent component of the program focused on the blast impulse load response of partially grouted concrete masonry unit (CMU) walls that minimally comply with the standards of the Department of Defense Unified Facilities Criteria (UFC) and other relevant non-DoD specifications. Three design sections were evaluated: (1) a 6-inch standard block masonry wall reinforced with #3 rebar at 32-inch nominal spacing, (2) an 8-inch standard block masonry wall reinforced with #4 rebar at 48-inch nominal spacing, and (3) a cavity wall consisting of the 8-inch standard reinforced CMU wythe, plus a 4-inch clay facing brick veneer with 2-inch thick extruded polystyrene rigid board insulation and a 1-inch air gap between the structural wythe and the veneer. The test program included (1) large- displacement static resistance testing under uniform pressure in a vacuum chamber and (2) full-scale explosion arena testing. During the static testing, displacements were recorded as each test panel was loaded to collapse, and the resistance function results were assessed against the resistance definitions assumed by standard blast design methodologies. Interior and exterior videography was also used to record the progression of failure. Subsequent to the static testing, three full-scale explosion arena tests were conducted; each experiment involved one each of the three test panel designs (nine total panels tested)., Annual (81st) Shock and Vibration Symposium (SAVIAC 2010) held 24-28 October 2010 in Orlando, FL.

In the face of Australia’s disaster-prone environment, architects Ian Weir and James Davidson are reconceptualising how our residential buildings might become more resilient to fire, flood and cyclone. With their first-hand experience of natural disasters, James, director of Emergency Architects Australia (EAA), and Ian, one of Australia’s few ‘bushfire architects’, discuss the ways we can design with disaster in mind. Dr Ian Weir is one of Australia’s few ‘bushfire architects’. Exploring a holistic ‘ground up’ approach to bushfire where landscape, building design and habitation patterns are orchestrated to respond to site-specific fire characteristics. Ian’s research is developed through design studio teaching at QUT and through built works in Western Australia’s fire prone forests and heathlands.

The current automotive industry and todays car drivers are faced with every increasing challenges, not previously experienced. Climate Change, financial issues, rising fuel prices, increased traffic congestion and reduced parking space in cities are all leading to changes in consumer preferences and the requirements of modern passenger vehicles. However, despite the shift in the industry dynamics, the principal layout of a car hasn’t changed since its invention. The design of a ’conventional’ vehicle is still principally a matchbox with four wheels, one at each corner. The concept has served its purpose well for over 100 years, but such a layout is not suited to solving today’s problems. To address the range of problems faced by the industry, a number of alternative commuting vehicles have been developed. Yet the commercialization of these ‘alternative’ vehicles has yet to be successful. This is largely due failure of these vehicles to meet the changing demands of the industry and the limited understanding of consumer behaviour, motivation and attitudes. Deakin University’s Tomorrow’s Car concept tackles all of these problems. The vehicle is a novel three-wheeler cross over concept between a car and a motorbike that combines the best of both worlds. The vehicle combines the low cost, small size and ‘fun’ factor of a motorbike together with the safety, comfort and easy to drive features of a car produce a vehicle with a fuel efficiency better than either car or scooter. Intensive market research has been conducted for various major potential markets of alternative vehicles including India, China and Australia. The research analysed consumer attitudes in relation to narrow tilting vehicles, and in particular towards Deakin’s Tomorrow’s Car (TC). The study revealed that a relatively large percentage of consumers find such a concept very appealing. For the other consumers, the overall appearance and

This report presents the results of blast load resistance testing of masonry walls that minimally meet applicable Unified Facilities Criteria (UFC) reinforcement requirements. The testing and analyses presented in this report involved partially-grouted (1) 6-inch, (2) 8-inch, and (3) insulated clay brick veneer (cavity wall) concrete masonry test panels. Three blast load experiments were conducted; each experiment tested one each of the three test panel designs (nine test panels total). The impulse loading to the panels varied significantly between each of the three experiments. Failure mechanism observations were made using interior and exterior high-speed videos that captured the response of each panel. A post-test forensic investigation was also conducted after each experiment to further explore and document the ultimate response of each test article. Dynamic deflections were captured and the responses were compared to blast analysis codes used for masonry design., Prepared in collaboration with Black & Veatch, Federal Services Division, Webb City, MO; University of Missouri, Department of Civil and Environmental Engineering, Columbia, MO; Air Force Civil Engineering Support Agency, Tyndall AFB, FL; and U.S. Army Engineer Research & Development Center, Vicksburg, MS.

This paper describes a methodology for analyzing the impulse pressure response of unreinforced concrete masonry walls that have been retrofitted with membranes that are not bonded to the masonry (catcher systems). Membrane catcher systems can be used to protect building occupants from secondary debris resulting from blast pressure, and the effectiveness of systems comprised of polymers, composites, geotextiles, and thin steel and aluminum sheets has been researched extensively over the past fifteen years. The methodology presented herein is based upon the large displacement response of the unreinforced masonry wall, with and without compression membrane arching, and the subsequent tension membrane resistance of the catcher system. The necessary equations are developed in the form of nonlinear resistance functions, which are then used in single-degree-of-freedom analyses to develop dynamic response predictions. The applicability of the approach is substantiated through comparison to full-scale blast test results, and demonstrations involving disparate materials and loading are made., Published in the International Journal of Protective Structures, v2 n1 p83-102, March 2011. Prepared in cooperation with Air Force Civil Engineering Support Agency, Tyndall AFB, FL; Air Force Research Laboratory, Tyndall AFB, FL; and Auburn University, Auburn, AL.

This paper discusses simulation methodology used to analyze static and dynamic behavior of foam insulated concrete sandwich wall panels through ultimate capacity. The experimental program used for model development and validation involved component-level testing, as well as both static and dynamic testing of full-scale wall panels. The static experiments involved single spans and double spans subjected to near-uniform distributed loading. The dynamic tests involved spans up to 30 ft tall that were subjected to impulse loads generated by an external explosion. Primary modeling challenges included: (1) accurately simulating prestressed initial conditions in an explicit dynamic code framework, (2) simulating the foam insulation in the high strain rate environment, and (3) simulating shear transfer between wythes, including frictional slippage and connector rupture. After validation, the models will be used to conduct additional behavioral studies and parametric analyses, and assess and improve methodology currently used in the design of foam insulated precast/prestressed sandwich panels for blast loads., For presentation at ASCE Structures Congress to be held 12-14 May 2010 in Orlando FL.

Over the past decade, extensive experimental and analytical research has been conducted on the behavior and resistance of unreinforced masonry (URM) walls retrofitted with methods for increasing ductility. This includes numerous experiments conducted by the Airbase Technologies Division of the Air Force Research Laboratory (AFRL). These retrofit materials varied from soft elastomeric coatings to very stiff composites and metal sheets. Some retrofit materials were strongly bonded to the masonry wall, which resulted in an integrated system response, while others were not bonded to the masonry and the membrane simply acted as a barrier that prevented secondary fragmentation from entering the occupied space. Previous research programs by AFRL and others have focused on the development of the retrofit materials, with the predominant exploratory measure focusing on the maximum inward transverse displacement. However, little emphasis was placed on the real behavior of the boundaries of these systems and the proper and efficient design of connections. This paper discusses an appropriate analytical methodology for the design of retrofit connections to resist impulse loads due to blast. In addition, typical support conditions for URM walls, and the shear, flexure and friction interaction of blast-impulse-loaded retrofitted URM walls at their support boundaries are discussed. The ideas and conclusions presented herein are based on component-level static testing, full scale explosion arena testing, and high fidelity finite element modeling., For presentation at ASCE Structures Congress to be held 12-14 May 2010 in Orlando FL.

This paper considers tests for cointegration with allowance for structural breaks, using the extrema of residual-based tests over subsamples of the data. One motivation for the approach is to formalize the practice of data snooping by practitioners, who may examine subsamples after failing to find a predicted cointegrating relationship. Valid critical values for such multiple testing situations may be useful. The methods also have the advantage of not imposing a form for the alternative hypothesis, in particular slope vs. intercept shifts and single versus multiple breaks, and being comparatively easy to compute. A range of alternative subsampling procedures, including sample splits, incremental and rolling samples are tabulated and compared experimentally. Shiller's annual stock prices and dividends series provide an illustration.

The goal of information superiority presents an analytic challenge: how to assess information technologies' support to command and control. The assessment difficulty lies in the cognitive domain that the technology supports, and identifying those features of the cognitive domain that lend themselves to quantification and objective assessments. This paper describes solutions to this problem. First, we survey the Army's traditional human-in-the-loop IT assessment methods, and identify their limitations. Next, we discuss M&S' relative advantages in assessing IT support of C2. Our major argument emphasizes three essential requirements: definition, representation and objective measurement of key C2 products, specifically of situation awareness and situation understanding of tactical decision makers., Presented at the International Command and Control Research and Technology Symposium (ICCRTS) (14th), Topic 8: C2 Assessment Tools and Metrics, Paper ID 126, held in Washington, DC, on 15-17 Jun 2009. Prepared in collaboration with U.S. Army Research Laboratory, WSMR, NM. The original document contains color images.

Mitigation techniques are currently sought to ensure public safety in the event of intentional or accidental explosions. The use of concrete masonry walls in civilian and military buildings is one of the most common methods of construction. These walls, however, are vulnerable to impulse loads, and can result in collapse, fragmentation and severe injury to occupants. Over the past several years, the Airbase Technologies Division of the Air Force Research Laboratory has investigated methods of retrofitting concrete masonry walls to better resist blast loads from external explosions. One method that was demonstrated to be very effective is the application of thin membranes of high elongation materials to the inside surface of the walls. Due to the nonlinear behavior of concrete masonry walls, the use of advanced simulation techniques provides certain advantages over experiments for full understanding of their structural responses under explosive loads. In the present study, several finite element models were developed according to blast test conditions, and analyzed using LS-DYNA explicit code. Input sensitivity studies were conducted to investigate the variations of a wide range of parameters on wall deformation, damping coefficients, boundary conditions and arching action. The effort has led to cost effective analysis techniques for use by structural engineers in designing membrane retrofit concrete masonry walls subjected to blast loads. This report summarizes the simulation methodologies, challenges, techniques and comparison to full-scale dynamic tests for membrane retrofit concrete masonry walls., The original document contains color images. All DTIC reproductions will be in black and white. Universal Technology Corp., Dayton, OH.

The overall objective of this project was to define the dynamic flexural resistance of multi-wythe insulated masonry walls with specific emphasis placed on determining the potential application of foam insulation as a blast-resistant material. The project was closely coordinated with full-scale explosive testing conducted by personnel at the Airbase Technologies Division of the Air Force Research Laboratory (AFRL) at Tyndall Air Force Base Florida. The project involved the following tasks: (1) use of finite element (FE) and single-degree-of-freedom (SDOF) analytical models for test analysis and prediction of results, (2) identification of the constitutive relationships of insulating foam(s), (3) synthesis of full-scale test methodology and results, (4) utilization of the data gathered from the full-scale tests to validate the FE models, (5) implementation of input parameter studies using the advanced FE models to characterize the mechanical behavior of the systems tested, and (6) development of engineering-level resistance definitions and multi-degree-of-freedom models of multi-wythe insulated masonry walls. Four standard wall section designs were recommended by the National Concrete Masonry Association (NCMA). Of these designs, two were selected for full-scale testing: a conventional block wall with a brick veneer and an A-block wall with a brick veneer - both with extruded polystyrene board insulation in the cavity and cells fully grouted. A single-wythe control wall with equivalent mass and flexural capacity was also included in the tests. FE models were used to assess the ability of the insulation to reduce the peak deflection of masonry walls subjected to impulse loads. Observations regarding the crushing of the insulation during the full-scale testing are also presented., The original document contains color images. Prepared in collaboration with Auburn University, Dept. of Civil Engineering, 238 Harbert Engineering Ctr, Auburn, AL. Work Unit No. Q210FA72.

Mitigation techniques are currently being sought to ensure public safety in the event of intentional or accidental explosions. Building material fragmentation is a major cause of human injury during such events. Use of concrete masonry walls is a common method of building construction. Concrete masonry provides a fast inexpensive way to construct buildings of various heights; however, these walls are extremely vulnerable to blast pressure resulting in collapse, fragmentation and severe injury to occupants. Much research has been conducted using actual blast tests as well as computational methods to study the behavior of masonry walls. Blast tests examined masonry walls of various shapes and make up, as well as the use of retrofit materials to mitigate the blast damage to masonry. In the computational arena, research made use of Livermore Software - DYNamics (LS-DYNA) finite element software to simulate full-scale models of concrete masonry walls. The results were compared to the actual blast tests, but the cost of high fidelity computational models made them impractical for day-to-day design. Design tools developed by other investigators in the field have been available for the past few years; however, their accuracy remains questionable when compared to actual blast test data. The research presented in this dissertation developed resistance functions for three different scenarios of membrane retrofit unreinforced concrete masonry walls to lateral pressure. These functions were further coupled with single degree of freedom systems to predict wall response to blast loads. The analysis results were compared to field blast tests for verification. This research gives the structural engineer a practical software tool for the design of membrane retrofit masonry walls to resist lateral pressures such as wind, and various blast charges and distances., The original document contains color images.

This paper first describes the current state of analysis for the response of unreinforced concrete masonry walls subjected to lateral uniform pressure. The formulation is based on the initial elastic response, the subsequent initiation of cracks and the nonlinear rocking response, and the eventual large displacement and wall's potential collapse. The necessary equations are developed for these phases in the form of a resistance function. The paper then incorporates membrane retrofit materials to strengthen the wall's resistance to lateral pressure, and develops the necessary resistance function equations. In blast tests, membrane retrofit unreinforced masonry walls have experienced severe cracking and large displacements without collapse. This is of high interest to the areas of DOD Force Protection, the Department of Homeland Security, and the construction industry impacted by hurricanes and other high wind events. The paper concludes with several illustrations and examples that the application of membrane retrofits indeed increases the resistance of the wall to lateral pressure., Presented at International Symposium on Interaction of the Effects of Munitions with Structures 12.1, ISIEMS 12.1, Orlando, FL, 17-21 Sep 2007. The original document contains color images.