Agricultural Education Through News

If a visitor came into your office and offered to influence a thousand people in your behalf, with no cost or obligation to you, would you take him up on it? Your job is education. Your problem is reaching the great number of people in your county with the wealth of information you have at hand. Of course you would jump at the chance. That man is your local newspaperman. Extension education is primarily a proposition of personal contacts through schools, meetings, field tours and farm visits. Yet not everyone is enrolled in your crop improvement association or home demonstration dub. They don\u27t all turn out for field tours or meetings. The office mailing list doesn\u27t reach everyone in the county and there is a reasonable limitation on the number of mailings that may be made. Some people in the county have never set foot inside your office there is always that group that is hard to contact. So take your newspaperman up on his offer of help. The offer may not be made explicitly. You may not even be aware that it exists. But every progressive newspaperman is looking for good, live, local stories for his paper

Modeling the interface between morphology and syntax in data-driven dependency parsing

When people formulate sentences in a language, they follow a set of rules specific to that language that defines how words must be put together in order to express the intended meaning. These rules are called the grammar of the language. Languages have essentially two ways of encoding grammatical information: word order or word form. English uses primarily word order to encode different meanings, but many other languages change the form of the words themselves to express their grammatical function in the sentence. These languages are commonly subsumed under the term morphologically rich languages. Parsing is the automatic process for predicting the grammatical structure of a sentence. Since grammatical structure guides the way we understand sentences, parsing is a key component in computer programs that try to automatically understand what people say and write. This dissertation is about parsing and specifically about parsing languages with a rich morphology, which encode grammatical information in the form of words. Today’s parsing models for automatic parsing were developed for English and achieve good results on this language. However, when applied to other languages, a significant drop in performance is usually observed. The standard model for parsing is a pipeline model that separates the parsing process into different steps, in particular it separates the morphological analysis, i.e. the analysis of word forms, from the actual parsing step. This dissertation argues that this separation is one of the reasons for the performance drop of standard parsers when applied to other languages than English. An analysis is presented that exposes the connection between the morphological system of a language and the errors of a standard parsing model. In a second series of experiments, we show that knowledge about the syntactic structure of sentence can support the prediction of morphological information. We then argue for an alternative approach that models morphological analysis and syntactic analysis jointly instead of separating them. We support this argumentation with empirical evidence by implementing two parsers that model the relationship between morphology and syntax in two different but complementary ways

Characterisation of telomere length dynamics in dairy cattle and association with productive lifespan

Telomeres form protective caps at the ends of linear chromosomes. They consist of repetitive DNA nucleotides and associated proteins of the shelterin complex. In vitro telomeres become shorter during cell division and when a critical shortness is reached they trigger a DNA damage response that leads to replicative senescence or apoptosis. Telomere shortening is a recognised hallmark of cellular ageing and seems to be also associated with organismal ageing. Telomere length (TL) and the rate of shortening vary across individuals and several studies have found that short telomeres and fast telomere depletion are associated with poor survival and early onset of age related diseases. However, longitudinal studies are needed to better understand the relationship of TL and TL dynamics with longevity measures. Relevant studies on livestock species are largely missing from the literature. In the dairy industry, farmers are forced to cull a considerable percentage of their heifers and cows at a young age due to fertility problems or diseases. As a consequence many replacement heifers have to be reared to maintain a specific herd size. This results in increased costs, consumption of resources, and damage to the environment. Breeding for an improved productive lifespan is difficult because longevity measures are recorded at the end of life and are known to have a low heritability. Therefore, the expected genetic improvement is generally slow, but could be considerably accelerated if an early life heritable biomarker was identified that is predictive of productive lifespan and could be used for animal selection. The question is if TL could be used as such a biomarker. The objectives of this thesis were to 1) develop robust methods to measure average relative leukocyte TL (RLTL) in cattle, 2) examine RLTL dynamics with age at a population as well as at an individual level, 3) estimate genetic parameters and 4) assess the association of RLTL and RLTL dynamics with productive lifespan. A quantitative polymerase chain reaction (qPCR) based assay developed for human studies was adapted to cattle and delivered robust results (repeatability > 80%, coefficient of variation=0.05). Different DNA extraction methods were tested for their effect on RLTL measurements and it was demonstrated that fast silica based DNA extraction methods are suitable for telomere projects which can improve the sample throughput and enable large-scale projects. Subsequently, RLTL in 1328 whole blood samples of 308 Holstein Friesian dairy cows and additionally in 284 whole blood samples of 38 female calves was measured. Repeatability and random regression models were used for the statistical analysis of telomere data. RLTL decreased considerably within the first year of life, but remained relatively stable afterwards at population level. Animals varied significantly in their amount and direction of telomere change. The genetic correlation between consecutive measurements in the same individual weakened with increasing sample interval from r=1 to r=0.69 which indicates that TL in the beginning of life might be under a different genetic control than TL later in life. For the first time in a livestock species we calculated heritability estimates for RLTL which were high (0.32-0.38) and remained constant over life. Long telomeres at birth were not predictive of better productive lifespan. However, animals with long RLTL at the ages of one and five years had a survival advantage. Also, animals that showed less average RLTL attrition over their lives remained in production for longer. TL dynamics differed among individuals and a considerable subset of individuals demonstrated telomere lengthening between consecutive measurements. On average, telomeres tend to shorten early in life and then remain relatively constant. While TL is a heritable trait throughout lifetime, telomere change is not heritable. Short TL at specific ages and telomere attrition over life were associated with poorer productive lifespan

Hard constraints for grammatical function labelling

For languages with (semi-) free word order (such as German), labelling grammatical functions on top of phrase-structural constituent analyses is crucial for making them interpretable. Unfortunately, most statistical classifiers consider only local information for function labelling and fail to capture important restrictions on the distribution of core argument functions such as subject, object etc., namely that there is at most one subject (etc.) per clause. We augment a statistical classifier with an integer linear program imposing hard linguistic constraints on the solution space output by the classifier, capturing global distributional restrictions. We show that this improves labelling quality, in particular for argument grammatical functions, in an intrinsic evaluation, and, importantly, grammar coverage for treebankbased (Lexical-Functional) grammar acquisition and parsing, in an extrinsic evaluation

User experience driven CPU frequency scaling on mobile devices towards better energy efficiency

With the development of modern smartphones, mobile devices have become ubiquitous in our daily lives. With high processing capabilities and a vast number of applications, users now need them for both business and personal tasks. Unfortunately, battery technology did not scale with the same speed as computational power. Hence, modern smartphone batteries often last for less than a day before they need to be recharged. One of the most power hungry components is the central processing unit (CPU). Multiple techniques are applied to reduce CPU energy consumption. Among them is dynamic voltage and frequency scaling (DVFS). This technique reduces energy consumption by dynamically changing CPU supply voltage depending on the currently running workload. Reducing voltage, however, also makes it necessary to reduce the clock frequency, which can have a significant impact on task performance. Current DVFS algorithms deliver a good user experience, however, as experiments conducted later in this thesis will show, they do not deliver an optimal energy efficiency for an interactive mobile workload. This thesis presents methods and tools to determine where energy can be saved during mobile workload execution when using DVFS. Furthermore, an improved DVFS technique is developed that achieves a higher energy efficiency than the current standard. One important question when developing a DVFS technique is: How much can you slow down a task to save energy before the negative effect on performance becomes intolerable? The ultimate goal when optimising a mobile system is to provide a high quality of experience (QOE) to the end user. In that context, task slowdowns become intolerable when they have a perceptible effect on QOE. Experiments conducted in this thesis answer this question by identifying workload periods in which performance changes are directly perceptible by the end user and periods where they are imperceptible, namely interaction lags and interaction idle periods. Interaction lags are the time it takes the system to process a user interaction and display a corresponding response. Idle periods are the periods between interactions where the user perceives the system as idle and ready for the next input. By knowing where those periods are and how they are affected by frequency changes, a more energy efficient DVFS governor can be developed. This thesis begins by introducing a methodology that measures the duration of interaction lags as perceived by the user. It uses them as an indicator to benchmark the quality of experience for a workload execution. A representative benchmark workload is generated comprising 190 minutes of interactions collected from real users. In conjunction with this QOE benchmark, a DVFS Oracle study is conducted. It is able to find a frequency profile for an interactive mobile workload which has the maximum energy savings achievable without a perceptible performance impact on the user. The developed Oracle performance profile achieves a QOE which is indistinguishable from always running on the fastest frequency while needing 45% less energy. Furthermore, this Oracle is used as a baseline to evaluate how well current mobile frequency governors are performing. It shows that none of these governors perform particularly well and up to 32% energy savings are possible. Equipped with a benchmark and an optimisation baseline, a user perception aware DVFS technique is developed in the second part of this thesis. Initially, a runtime heuristic is introduced which is able to detect interaction lags as the user would perceive them. Using this heuristic, a reinforcement learning driven governor is developed which is able to learn good frequency settings for interaction lag and idle periods based on sample observations. It consumes up to 22% less energy than current standard governors on mobile devices, and maintains a low impact on QOE

Revealing Compiler Heuristics through Automated Discovery and Optimization

Tuning compiler heuristics and parameters is well known to improve optimization outcomes dramatically. Prior works have tuned command line flags and a few expert identified heuristics. However, there are an unknown number of heuristics buried, unmarked and unexposed inside the compiler as a consequence of decades of development without auto-tuning being foremost in the minds of developers. Many may not even have been considered heuristics by the developers who wrote them. The result is that auto-tuning search and machine learning can optimize only a tiny fraction of what could be possible if all heuristics were available to tune. Manually discovering all of these heuristics hidden among millions of lines of code and exposing them to auto-tuning tools is a Herculean task that is simply not practical. What is needed is a method of automatically finding these heuristics to extract every last drop of potential optimization. In this work, we propose Heureka, a framework that automat ically identifies potential heuristics in the compiler that are highly profitable optimization targets and then automatically finds available tuning parameters for those heuristics with minimal human involvement. Our work is based on the following key insight: When modifying the output of a heuristic within an acceptable value range, the calling code using that output will still function correctly and produce semantically correct results. Building on that, we automatically manipulate the output of potential heuristic code in the compiler and decide using a Differential Testing approach if we found a heuristic or not. During output manipulation, we also explore acceptable value ranges of the targeted code. Heuristics identified in this way can then be tuned to optimize an objective function. We used Heureka to search for heuristics among eight thou sand functions from the LLVM optimization passes, which is about 2% of all available functions. We then use identified heuristics to tune the compilation of 38 applications from the NAS and Polybench benchmark suites. Compared to an -Oz baseline we reduce binary sizes by up to 11.6% considering single heuristics only and up to 19.5% when stacking the effects of multiple identified tuning targets and applying a random search with minimal search effort. Generalizing from existing analysis results, Heureka needs, on average, a little under an hour on a single machine to identify relevant heuristic targets for a previously unseen application.<br/

Carbon Mineralization by Aqueous Precipitation for Beneficial Use of CO2 from Flue Gas

Calera's innovative Mineralization via Aqueous Precipitation (MAP) technology for the capture and conversion of CO{sub 2} to useful materials for use in the built environment was further developed and proven in the Phase 1 Department of Energy Grant. The process was scaled to 300 gallon batch reactors and subsequently to Pilot Plant scale for the continuous production of product with the production of reactive calcium carbonate material that was evaluated as a supplementary cementitious material (SCM). The Calera SCM{trademark} was evaluated as a 20% replacement for ordinary portland cement and demonstrated to meet the industry specification ASTM 1157 which is a standard performance specification for hydraulic cement. The performance of the 20% replacement material was comparable to the 100% ordinary portland cement control in terms of compressive strength and workability as measured by a variety of ASTM standard tests. In addition to the performance metrics, detailed characterization of the Calera SCM was performed using advanced analytical techniques to better understand the material interaction with the phases of ordinary portland cement. X-ray synchrotron diffraction studies at the Advanced Photon Source in Argonne National Lab confirmed the presence of an amorphous phase(s) in addition to the crystalline calcium carbonate phases in the reactive carbonate material. The presence of carboaluminate phases as a result of the interaction of the reactive carbonate materials with ordinary portland cement was also confirmed. A Life Cycle Assessment was completed for several cases based on different Calera process configurations and compared against the life cycle of ordinary portland cement. In addition to the materials development efforts, the Calera technology for the production of product using an innovative building materials demonstration plant was developed beyond conceptual engineering to a detailed design with a construction schedule and cost estimate