Potential for wind erosion as affected by management in bean-potato rotations

Non-Peer ReviewedThe potential for soil erosion in the bean phase of bean-potato rotations is significant due to low levels of crop residue following potatoes and the effect of management on soil structure particularly in light textured soils typical of the potato growing area of Manitoba. This potential can be mitigated by fall cover crops, application of straw or composted manure. In a study at Carberry, MB crop residue cover from 2000 to 2004, the proportion of small erodible aggregates and stability of aggregates were measured in treatments with fall applied cereal, fall applied compost, and spring applied polymer. Crop residue cover, proportion of erodible aggregates and aggregate stability were not consistently affected by management over the short term. In some years application of cereal residue in the fall increased residue cover, reduced the proportion of small erodible aggregates (<0.5 mm) and increased stability of aggregates. Application of polyacrylamide did not affect stability of wet-sieved aggregates but decreased the proportion of small aggregates (<0.5 mm) in 2002. Further research is required to assess the long-term impact of management on potential for wind erosion and properties of soil aggregates in bean-potato rotations

Force-induced recruitment of cten along keratin network in epithelial cells.

The cytoskeleton provides structural integrity to cells and serves as a key component in mechanotransduction. Tensins are thought to provide a force-bearing linkage between integrins and the actin cytoskeleton; yet, direct evidence of tensin's role in mechanotransduction is lacking. We here report that local force application to epithelial cells using a micrometer-sized needle leads to rapid accumulation of cten (tensin 4), but not tensin 1, along a fibrous intracellular network. Surprisingly, cten-positive fibers are not actin fibers; instead, these fibers are keratin intermediate filaments. The dissociation of cten from tension-free keratin fibers depends on the duration of cell stretch, demonstrating that the external force favors maturation of cten-keratin network interactions over time and that keratin fibers retain remarkable structural memory of a cell's force-bearing state. These results establish the keratin network as an integral part of force-sensing elements recruiting distinct proteins like cten and suggest the existence of a mechanotransduction pathway via keratin network

Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, like continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions

A robust adaptive wavelet-based method for classification of meningioma histology images

Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

Ironic Encounters in the Anthropocene: Jürgen Nefzger’s Nuclear Landscape Photography

In this article, I explore the question of how art may help us to map and, indeed, inhabit the problematic subject position that the Anthropocene confronts us with. I focus on the landscape photography collected in Jürgen Nefzger’s Fluffy Clouds (2010) and its use of irony to obstruct the power dynamics at work in traditional landscape aesthetics. I suggest that Fluffy Clouds helps us to think subjectivity in the Anthropocene from a non-unitary position, i.e. a position that is not based on notions of individuality and identity, but is by default relational. My reading will be helped by Ernst van Alphen’s interpretation of perspective as a subject-constituting device and Paul de Man’s notion of the twofold, ironic self.Modern and Contemporary Studie

Identification. The missing link between joint attention and imitation

In this paper we outline our hypothesis that human intersubjective engagement entails identifying with other people. We tested a prediction derived from this hypothesis that concerned the relation between a component of joint attention and a specific form of imitation. The empirical investigation involved “blind” ratings of videotapes from a recent study in which we tested matched children with and without autism for their propensity to imitate the self-/other-orientated aspects of another person's actions. The results were in keeping with three a priori predictions, as follows: (a) children with autism contrasted with control participants in spending more time looking at the objects acted upon and less time looking at the tester; (b) participants with autism showed fewer “sharing” looks toward the tester, and although they also showed fewer “checking” and “orientating” looks, they were specifically less likely to show any sharing looks; and, critically, (c) within each group, individual differences in sharing looks (only) were associated with imitation of self–other orientation. We suggest that the propensity to adopt the bodily anchored psychological stance of another person is essential to certain forms of joint attention and imitation, and that a weak tendency to identify with others is pivotal for the developmental psychopathology of autism