Improving Irrigation Scheduling and Water use Efficiency in Cotton

Cotton (Gossypium hirsutum L) is an important crop in the southern United States. The crop is grown in both irrigated and rainfed situations and is seldom free from periods of water shortages at some stage during the season. In recent years the need for consistency in yields and a stable cash flow has resulted in a rapid expansion in the number of irrigated acres of cotton in the Mississippi Delta. Irrigation research has, however, not kept pace with this expansion. This project represents a start at meeting this urgent need. The influence of weather patterns necessitates that these studies be conducted over several years, and the results given here are, therefore, only preliminary observations. The early termination of irrigation has not resulted in any significant decrease in yield or lint quality on the Sharkey clay, although there was a slight detrimental trend when irrigation was terminated too early in August. These studies have helped to clarify the relationship between soil-moisture deficit and plant stress, especially as relates to yield, for cotton cropped on a Sharkey clay soil. Evaluation of crop indicators of water deficit showed that leaf water potential and the air-canopy temperature differential are reliable indicators of the onset of water stress. Leaf extension growth is also a sensitive indicator, but of no practical value in irrigation management. With further research, leaf water potential and canopy-air temperature differentials could provide useful indicators for use in conjunction with traditional methods of scheduling irrigation for cotton in the humid mid-south. A better understanding of the irrigation requirements of the crop will improve management and will have a very significant dollar reduction in the cost of production of the crop

Differentiable Unbiased Online Learning to Rank

Online Learning to Rank (OLTR) methods optimize rankers based on user interactions. State-of-the-art OLTR methods are built specifically for linear models. Their approaches do not extend well to non-linear models such as neural networks. We introduce an entirely novel approach to OLTR that constructs a weighted differentiable pairwise loss after each interaction: Pairwise Differentiable Gradient Descent (PDGD). PDGD breaks away from the traditional approach that relies on interleaving or multileaving and extensive sampling of models to estimate gradients. Instead, its gradient is based on inferring preferences between document pairs from user clicks and can optimize any differentiable model. We prove that the gradient of PDGD is unbiased w.r.t. user document pair preferences. Our experiments on the largest publicly available Learning to Rank (LTR) datasets show considerable and significant improvements under all levels of interaction noise. PDGD outperforms existing OLTR methods both in terms of learning speed as well as final convergence. Furthermore, unlike previous OLTR methods, PDGD also allows for non-linear models to be optimized effectively. Our results show that using a neural network leads to even better performance at convergence than a linear model. In summary, PDGD is an efficient and unbiased OLTR approach that provides a better user experience than previously possible.Comment: Conference on Information and Knowledge Management 201

Balancing Speed and Quality in Online Learning to Rank for Information Retrieval

In Online Learning to Rank (OLTR) the aim is to find an optimal ranking model by interacting with users. When learning from user behavior, systems must interact with users while simultaneously learning from those interactions. Unlike other Learning to Rank (LTR) settings, existing research in this field has been limited to linear models. This is due to the speed-quality tradeoff that arises when selecting models: complex models are more expressive and can find the best rankings but need more user interactions to do so, a requirement that risks frustrating users during training. Conversely, simpler models can be optimized on fewer interactions and thus provide a better user experience, but they will converge towards suboptimal rankings. This tradeoff creates a deadlock, since novel models will not be able to improve either the user experience or the final convergence point, without sacrificing the other. Our contribution is twofold. First, we introduce a fast OLTR model called Sim-MGD that addresses the speed aspect of the speed-quality tradeoff. Sim-MGD ranks documents based on similarities with reference documents. It converges rapidly and, hence, gives a better user experience but it does not converge towards the optimal rankings. Second, we contribute Cascading Multileave Gradient Descent (C-MGD) for OLTR that directly addresses the speed-quality tradeoff by using a cascade that enables combinations of the best of two worlds: fast learning and high quality final convergence. C-MGD can provide the better user experience of Sim-MGD while maintaining the same convergence as the state-of-the-art MGD model. This opens the door for future work to design new models for OLTR without having to deal with the speed-quality tradeoff.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information and Knowledge Managemen

Optimizing Ranking Models in an Online Setting

Online Learning to Rank (OLTR) methods optimize ranking models by directly interacting with users, which allows them to be very efficient and responsive. All OLTR methods introduced during the past decade have extended on the original OLTR method: Dueling Bandit Gradient Descent (DBGD). Recently, a fundamentally different approach was introduced with the Pairwise Differentiable Gradient Descent (PDGD) algorithm. To date the only comparisons of the two approaches are limited to simulations with cascading click models and low levels of noise. The main outcome so far is that PDGD converges at higher levels of performance and learns considerably faster than DBGD-based methods. However, the PDGD algorithm assumes cascading user behavior, potentially giving it an unfair advantage. Furthermore, the robustness of both methods to high levels of noise has not been investigated. Therefore, it is unclear whether the reported advantages of PDGD over DBGD generalize to different experimental conditions. In this paper, we investigate whether the previous conclusions about the PDGD and DBGD comparison generalize from ideal to worst-case circumstances. We do so in two ways. First, we compare the theoretical properties of PDGD and DBGD, by taking a critical look at previously proven properties in the context of ranking. Second, we estimate an upper and lower bound on the performance of methods by simulating both ideal user behavior and extremely difficult behavior, i.e., almost-random non-cascading user models. Our findings show that the theoretical bounds of DBGD do not apply to any common ranking model and, furthermore, that the performance of DBGD is substantially worse than PDGD in both ideal and worst-case circumstances. These results reproduce previously published findings about the relative performance of PDGD vs. DBGD and generalize them to extremely noisy and non-cascading circumstances.Comment: European Conference on Information Retrieval (ECIR) 201

Policy-Aware Unbiased Learning to Rank for Top-k Rankings

Counterfactual Learning to Rank (LTR) methods optimize ranking systems using logged user interactions that contain interaction biases. Existing methods are only unbiased if users are presented with all relevant items in every ranking. There is currently no existing counterfactual unbiased LTR method for top-k rankings. We introduce a novel policy-aware counterfactual estimator for LTR metrics that can account for the effect of a stochastic logging policy. We prove that the policy-aware estimator is unbiased if every relevant item has a non-zero probability to appear in the top-k ranking. Our experimental results show that the performance of our estimator is not affected by the size of k: for any k, the policy-aware estimator reaches the same retrieval performance while learning from top-k feedback as when learning from feedback on the full ranking. Lastly, we introduce novel extensions of traditional LTR methods to perform counterfactual LTR and to optimize top-k metrics. Together, our contributions introduce the first policy-aware unbiased LTR approach that learns from top-k feedback and optimizes top-k metrics. As a result, counterfactual LTR is now applicable to the very prevalent top-k ranking setting in search and recommendation.Comment: SIGIR 2020 full conference pape

Comparison of growing media for container grown plants

Greenhouse and growth chamber experiments are conducted worldwide in efforts to produce solutions that would increase yields of agronomic crops. However, the results of those experiments vary due to the many growth media being used. An experiment was conducted in the fall of 2010 to identify a broadly acceptable growth media that would produce uniform stands and optimum results in greenhouse and growth chamber settings. A total of six growth media were tested on cotton (Gossypium hirsutum) at the Arkansas Agricutural Research and Extension Center’s Altheimer Lab in Fayetteville. The plants grown in each medium were harvested six weeks after planting and the measurements performed included plant height, plant dry matter, leaf area, and nutrient analysis. The results indicated that a positive, significant difference (P \u3c 0.05) existed between “Sunshine” mix (MIX1) and the other media. Plants grown in MIX1 experienced greater plant height, dry matter, leaf area, and also experienced higher leaf tissue levels of N, P, and S. “Sunshine” (Mix1) is a readily available growth medium that produces optimum plant growth and uniform results in growth chamber and greenhouse experiments

An Evolutionary Economic Analysis of Energy Transitions

Evolutionary economics offers clear insights into the mechanisms that underlie innovations, structural change and transitions. It is therefore of great value for the framing of policies aimed at fostering a transition to a sustainable development. This paper offers an overview of the main insights of evolutionary economics and derives core concepts, namely ‘diversity’, ‘innovation’, ‘selection environment’, ‘bounded rationality’, ‘path dependence and lock-in’, and ‘coevolution’. These concepts are subsequently used to formulate guidelines for the role of the government and the design of public policies, such as the learning from historical technological pathways and the creation of an extended level playing field. In addition, the developments of certain energy technologies are examined in detail within the adopted evolutionary economics framework. Three particular technologies received attention, namely fuel cells, nuclear fusion, and photovoltaic cells.

Jet pumps for thermoacoustic applications: design guidelines based on a numerical parameter study

The oscillatory flow through tapered cylindrical tube sections (jet pumps) is characterized by a numerical parameter study. The shape of a jet pump results in asymmetric hydrodynamic end effects which cause a time-averaged pressure drop to occur under oscillatory flow conditions. Hence, jet pumps are used as streaming suppressors in closed-loop thermoacoustic devices. A two-dimensional axisymmetric computational fluid dynamics model is used to calculate the performance of a large number of conical jet pump geometries in terms of time-averaged pressure drop and acoustic power dissipation. The investigated geometrical parameters include the jet pump length, taper angle, waist diameter and waist curvature. In correspondence with previous work, four flow regimes are observed which characterize the jet pump performance and dimensionless parameters are introduced to scale the performance of the various jet pump geometries. The simulation results are compared to an existing quasi-steady theory and it is shown that this theory is only applicable in a small operation region. Based on the scaling parameters, an optimum operation region is defined and design guidelines are proposed which can be directly used for future jet pump design.Comment: The following article has been accepted by the Journal of the Acoustical Society of America. After it is published, it will be found at http://scitation.aip.org/JAS

A numerical investigation on the vortex formation and flow separation of the oscillatory flow in jet pumps

A two-dimensional computational fluid dynamics model is used to predict the oscillatory flow through a tapered cylindrical tube section (jet pump) placed in a larger outer tube. Due to the shape of the jet pump, there will exist an asymmetry in the hydrodynamic end effects which will cause a time-averaged pressure drop to occur that can be used to cancel Gedeon streaming in a closed-loop thermoacoustic device. The performance of two jet pump geometries with different taper angles is investigated. A specific time-domain impedance boundary condition is implemented in order to simulate traveling acoustic wave conditions. It is shown that by scaling the acoustic displacement amplitude to the jet pump dimensions, similar minor losses are observed independent of the jet pump geometry. Four different flow regimes are distinguished and the observed flow phenomena are related to the jet pump performance. The simulated jet pump performance is compared to an existing quasi-steady approximation which is shown to only be valid for small displacement amplitudes compared to the jet pump length.Comment: The following article has been accepted by the Journal of the Acoustical Society of America. After it is published, it will be found at: http://scitation.aip.org/JAS