Maintaining the Regular Ultra Passum Law in data envelopment analysis

The variable returns to scale data envelopment analysis (DEA) model is developed with a maintained hypothesis of convexity in input-output space. This hypothesis is not consistent with standard microeconomic production theory that posits an S-shape for the production frontier, i.e. for production technologies that obey the Regular Ultra Passum Law. Consequently, measures of technical efficiency assuming convexity are biased downward. In this paper, we provide a more general DEA model that allows the S-shape.Data envelopment analysis; homothetic production; S-shaped production function; non-convex production set

Screening in (2+1)D pure gauge theory at high temperatures

We compute heavy quark potentials in pure gauge $SU(3)$ at high temperatures in $2+1$ dimensions and confront them with expectations emerging from perturbative calculations.Comment: 3 pages, latex, 4 figures, uu, Contribution to LATTICE 9

Testing over-representation of observations in subsets of a DEA technology

This paper proposes a test for whether data are over-represented in a given production zone, i.e. a subset of a production possibility set which has been estimated using the non-parametric Data Envelopment Analysis (DEA) approach. A binomial test is used that relates the number of observations inside such a zone to a discrete probability weighted relative volume of that zone. A Monte Carlo simulation illustrates the performance of the proposed test statistic and suggests good estimation of both facet probabilities and the assumed common inefficiency distribution in a three dimensional input space.Data Envelopment Analysis (DEA); Over-representation; Data density; Binomial test; Convex hull

Barriers for developing more robust organic arable farming systems in practice

There is a gap between the scientific idea of robust and economically viable organic arable farming systems with optimized crop rotations for nutrient and pest management and how these systems look like in practice. In order to explore this gap, we visited and interviewed ten organic arable farms in Denmark. Our main findings are: 1) Organic arable farming operates in a very dynamic and changing environment in terms of prizing and market opportunities, and the main focus of the farm managements was the coping strategy within this changing environment; 2) The farming systems were continuously changing and developing, buying and renting more land, changing manure agreements and other forms of cooperation and arrangements; 3) Short term profit was paid much more attention than more theoretical expectation on long term profit or opportunities in relation to optimizing the production system. This again seems logical in relation to the very dynamic world that the farmers have to operate within; 4) Most of the farmers do not see their farm as a coherent system but as a coordination of a series of separate operations, which means that most decisions are taken with specific reference to the individual field in at the present situation without considering the long-term effects. Management focus is thus much more on solving problems as they are occurring, by adjusting their practice, than it is on developing a robust system preventing problems to occur. This partial focus is also strongly supported by the way in which extension services mostly operate

Deep transfer learning for improving single-EEG arousal detection

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.Comment: Accepted for presentation at EMBC202

Improved weed management in organic crop production

Weed problems can constrain organic crop production resulting in significant losses in yield and quality. Especially perennial weed species such as Elytrigia repens, Cirsium arvense and Tussilago farfara are posing problems as arable cropping systems do not hamper their vegetative proliferation sufficiently. Annual weeds may also reach unacceptable infestation levels leaving the growers with poor yielding crops and severe future weed problems owing to the shedding and spread of weed seeds. Effective weed management is a key component for successful organic crop production and a prerequisite to increase the yielding potential of many organic crop production systems. Weed problems are most severe on stockless arable farms because the supply of nutrients can be limited and may not suffice to produce competitive crop stands. Crop rotations in arable cropping systems often have fewer grass-clovers leys or other perennial crops known to disrupt weed proliferation more effectively than cash crops such as cereals and pulses. The higher nutritional status of soils on dairy farms and the more frequent use of perennial crops for mowing are major causes for less severe weed problems on those farms

Exact Periodic Solutions of Shells Models of Turbulence

We derive exact analytical solutions of the GOY shell model of turbulence. In the absence of forcing and viscosity we obtain closed form solutions in terms of Jacobi elliptic functions. With three shells the model is integrable. In the case of many shells, we derive exact recursion relations for the amplitudes of the Jacobi functions relating the different shells and we obtain a Kolmogorov solution in the limit of infinitely many shells. For the special case of six and nine shells, these recursions relations are solved giving specific analytic solutions. Some of these solutions are stable whereas others are unstable. All our predictions are substantiated by numerical simulations of the GOY shell model. From these simulations we also identify cases where the models exhibits transitions to chaotic states lying on strange attractors or ergodic energy surfaces.Comment: 25 pages, 7 figure

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.Comment: Accepted for publication in 41st International Engineering in Medicine and Biology Conference (EMBC), July 23-27, 201