Getting Beyond Visual Impact: Designing Renewable Energy as a Positive Landscape Addition

The critical necessity of scaling up renewable energy to meet the challenge of climate change implicates vast swaths of American landscape. Renewable energy infrastructure has long concerned itself with minimizing its visual impact, in order to decrease opposition from local landowners and users of the landscape. As energy facilities proliferate across the landscape, their visual impact can be expected to grow as well—both in terms of the scale of installations, as well as the amount of territory affected. On public lands, renewable energy infrastructure has had to compete with alternate public uses of the land, including scenic and recreational values. Managers of public landscapes have developed specific procedures for describing the visual impact to landscapes stemming from energy development, and specific methodologies to evaluate whether a particular project should proceed. In most contemporary energy planning processes that include landscape design professionals, these designers’ scope is limited to comparing the visual impact of discrete energy installations: the spacing, height, and alignment of wind turbines or solar panels, for example. We argue for a more inclusive approach to incorporating spatial design considerations, earlier in the planning process, as a way of incorporating public aspirations and opinions about the energy landscape, expanding the field of potential planning outcomes, and identifying synergies for co-locating multiple positive elements. How can energy infrastructure actively participate in the shaping of a positive landscape experience, and not just try to minimize its impact on the landscape? This paper will present several examples of infrastructure-driven landscape transformations that actively incorporated public input and visual assessment considerations, at the municipal and regional scales, in order to develop energy planning frameworks with high social acceptance. One case study looks at the spatial planning around wind turbine installations in the Wieringermeer polder in the Netherlands, which used design to develop a consistent image for wind installations, and create a recognizable new layer in the cultural landscape that reflects the qualities, scale, and character of the underlying landscape (H+N+S Landschapsarchitecten, 2014). One other European example demonstrates the impact of an iterative design process in producing the successful Middelgrunden wind farm in Copenhagen, Denmark. We analyze the potential of these kinds of planning processes on American renewable energy infrastructure planning. We note examples of energy planning that are successfully minimizing conflict between social and ecological stakeholders, focusing on California programs such as the Desert Renewable Energy Conservation Plan (DRECP), but that would benefit from incorporating design methodologies more extensively to manage visual landscape impact

Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates

Recently, data augmentation (DA) has emerged as a method for leveraging domain knowledge to inexpensively generate additional data in reinforcement learning (RL) tasks, often yielding substantial improvements in data efficiency. While prior work has demonstrated the utility of incorporating augmented data directly into model-free RL updates, it is not well-understood when a particular DA strategy will improve data efficiency. In this paper, we seek to identify general aspects of DA responsible for observed learning improvements. Our study focuses on sparse-reward tasks with dynamics-invariant data augmentation functions, serving as an initial step towards a more general understanding of DA and its integration into RL training. Experimentally, we isolate three relevant aspects of DA: state-action coverage, reward density, and the number of augmented transitions generated per update (the augmented replay ratio). From our experiments, we draw two conclusions: (1) increasing state-action coverage often has a much greater impact on data efficiency than increasing reward density, and (2) decreasing the augmented replay ratio substantially improves data efficiency. In fact, certain tasks in our empirical study are solvable only when the replay ratio is sufficiently low

On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling

On-policy reinforcement learning (RL) algorithms perform policy updates using i.i.d. trajectories collected by the current policy. However, after observing only a finite number of trajectories, on-policy sampling may produce data that fails to match the expected on-policy data distribution. This sampling error leads to noisy updates and data inefficient on-policy learning. Recent work in the policy evaluation setting has shown that non-i.i.d., off-policy sampling can produce data with lower sampling error than on-policy sampling can produce. Motivated by this observation, we introduce an adaptive, off-policy sampling method to improve the data efficiency of on-policy policy gradient algorithms. Our method, Proximal Robust On-Policy Sampling (PROPS), reduces sampling error by collecting data with a behavior policy that increases the probability of sampling actions that are under-sampled with respect to the current policy. Rather than discarding data from old policies -- as is commonly done in on-policy algorithms -- PROPS uses data collection to adjust the distribution of previously collected data to be approximately on-policy. We empirically evaluate PROPS on both continuous-action MuJoCo benchmark tasks as well as discrete-action tasks and demonstrate that (1) PROPS decreases sampling error throughout training and (2) improves the data efficiency of on-policy policy gradient algorithms. Our work improves the RL community's understanding of a nuance in the on-policy vs off-policy dichotomy: on-policy learning requires on-policy data, not on-policy sampling

Global Motion Planning under Uncertain Motion, Sensing, and Environment Map

Motion planning that takes into account uncertainty in motion, sensing, and environment map, is critical for autonomous robots to operate reliably in our living spaces. Partially Observable Markov Decision Processes (POMDPs) is a principled and general framework for planning under uncertainty. Although recent development of point-based POMDPs have drastically increased the speed of POMDP planning, even the best POMDP planner today, fails to generate reasonable motion strategies when the environment map is not known exactly. This paper presents Guided Cluster Sampling (GCS), a new point-based POMDP planner for motion planning under uncertain motion, sensing, and environment map, when the robot has active sensing capability. It uses our observations that in this problem, the belief space B can be partitioned into a collection of much smaller subspaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. GCS samples B using two-stage cluster sampling, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Preliminary results suggest that GCS generates reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDP planner today, within reasonable time. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, GCS converges to the optimal policy, given enough time. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space

Student Preferences for Faculty-Led Honors Study Abroad Experiences

A critical component of any education, particularly an honors education, is an interdisciplinary curriculum that enriches the college experience. At South Dakota State University (SDSU), the Fishback Honors College strives to provide a robust and holistic educational experience through innovative honors courses paired with enriching co-curricular programs. One way to meet these goals is an honors study abroad experience included as part of the Fishback Honors College curriculum. The study abroad course is an integral part the honors curriculum, fulfilling the requirement of an interdisciplinary colloquium course and thus making it accessible to students from every field of study

Ghajn Klieb, (Rabat, Malta)

Between October and December 1999 a team of local and foreign undergraduates from the University of Malta carried out a survey of the site at Ghajn Klieb outside Rabat. The exercise constituted the practical part of a unit on the Principles of Archaeological Surveying directed by Dr Nicholas Vella of the Department of Classics & Archaeology. For the survey the team was joined by Hanna Stager, a graduate of the same department, who also researched some of the references used in this article. Initial reconnaissance of the site was carried out on 15 October 1999 with Nathaniel Cutajar and Michelle B uhagiar, Curator and Assistant Curator respectively at the National Museum of Archaeology. The scatter of surface ceramics and the existence of previously known and unknown features revealed the extent and potential of the site. It was decided that the locality of Ghajn Klieb warranted systematic study that could be carried out in various stages, with the longterm aim being an assessment of human activity and cultural behaviour at the site. The Museums Department gave the go-ahead for this project, and permission to collect the surface ceramics was granted. This short report is intended to give an outline of the work undertaken to date. Emphasis is placed on the field methods adopted and on the presentation of what we believe to be worth talking about at this stage. An effort is here made by the senior author to unravel the collaborative nature of the exercise by lending weight to individual thoughts and interpretations that arose while work progressed in the field.peer-reviewe

Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning

Learning from demonstration (LfD) is a popular technique that uses expert demonstrations to learn robot control policies. However, the difficulty in acquiring expert-quality demonstrations limits the applicability of LfD methods: real-world data collection is often costly, and the quality of the demonstrations depends greatly on the demonstrator's abilities and safety concerns. A number of works have leveraged data augmentation (DA) to inexpensively generate additional demonstration data, but most DA works generate augmented data in a random fashion and ultimately produce highly suboptimal data. In this work, we propose Guided Data Augmentation (GuDA), a human-guided DA framework that generates expert-quality augmented data. The key insight of GuDA is that while it may be difficult to demonstrate the sequence of actions required to produce expert data, a user can often easily identify when an augmented trajectory segment represents task progress. Thus, the user can impose a series of simple rules on the DA process to automatically generate augmented samples that approximate expert behavior. To extract a policy from GuDA, we use off-the-shelf offline reinforcement learning and behavior cloning algorithms. We evaluate GuDA on a physical robot soccer task as well as simulated D4RL navigation tasks, a simulated autonomous driving task, and a simulated soccer task. Empirically, we find that GuDA enables learning from a small set of potentially suboptimal demonstrations and substantially outperforms a DA strategy that samples augmented data randomly

An unexpectedly high degree of specialization and a widespread involvement in sterol metabolism among the C. elegans putative aminophospholipid translocases

<p>Abstract</p> <p>Background</p> <p>P-type ATPases in subfamily IV are exclusively eukaryotic transmembrane proteins that have been proposed to directly translocate the aminophospholipids phosphatidylserine and phosphatidylethanolamine from the exofacial to the cytofacial monolayer of the plasma membrane. Eukaryotic genomes contain many genes encoding members of this subfamily. At present it is unclear why there are so many genes of this kind per organism or what individual roles these genes perform in organism development.</p> <p>Results</p> <p>We have systematically investigated expression and developmental function of the six, <it>tat-1 </it>through <it>6</it>, subfamily IV P-type ATPase genes encoded in the <it>Caenorhabditis elegans </it>genome. <it>tat-5 </it>is the only ubiquitously-expressed essential gene in the group. <it>tat-6 </it>is a poorly-transcribed recent duplicate of <it>tat-5</it>. <it>tat-2 </it>through <it>4 </it>exhibit tissue-specific developmentally-regulated expression patterns. Strong expression of both <it>tat-2 </it>and <it>tat-4 </it>occurs in the intestine and certain other cells of the alimentary system. The two are also expressed in the uterus, during spermatogenesis and in the fully-formed spermatheca. <it>tat-2 </it>alone is expressed in the pharyngeal gland cells, the excretory system and a few cells of the developing vulva. The expression pattern of <it>tat-3 </it>is almost completely different from those of <it>tat-2 </it>and <it>tat-4</it>. <it>tat-3 </it>expression is detectable in the steroidogenic tissues: the hypodermis and the XXX cells, as well as in most cells of the pharynx (except gland), various tissues of the reproductive system (except uterus and spermatheca) and seam cells. Deletion of <it>tat-1 </it>through <it>4 </it>individually interferes little or not at all with the regular progression of organism growth and development under normal conditions. However, <it>tat-2 </it>through <it>4 </it>become essential for reproductive growth during sterol starvation.</p> <p>Conclusion</p> <p><it>tat-5 </it>likely encodes a housekeeping protein that performs the proposed aminophospholipid translocase function routinely. Although individually dispensable, <it>tat-1 </it>through <it>4 </it>seem to be at most only partly redundant. Expression patterns and the sterol deprivation hypersensitivity deletion phenotype of <it>tat-2 </it>through <it>4 </it>suggest that these genes carry out subtle metabolic functions, such as fine-tuning sterol metabolism in digestive or steroidogenic tissues. These findings uncover an unexpectedly high degree of specialization and a widespread involvement in sterol metabolism among the genes encoding the putative aminophospholipid translocases.</p