Random Number Generation: Types and Techniques

What does it mean to have random numbers? Without understanding where a group of numbers came from, it is impossible to know if they were randomly generated. However, common sense claims that if the process to generate these numbers is truly understood, then the numbers could not be random. Methods that are able to let their internal workings be known without sacrificing random results are what this paper sets out to describe. Beginning with a study of what it really means for something to be random, this paper dives into the topic of random number generators and summarizes the key areas. It covers the two main groups of generators, true-random and pseudo-random, and gives practical examples of both. To make the information more applicable, real life examples of currently used and currently available generators are provided as well. Knowing the how and why of a number sequence without knowing the values that will come is possible, and this thesis explains how it is accomplished

Armillaria root rot in fruit orchards

"Armillaria root is usually considered to be a disease affecting forest trees, but it can cause significant losses in orchards. The pathogen, a fungus known as Armillaria mellea, will kill trees, and its persistence in the soil for many years can prevent the re-establishment of productive orchards on infested sites."--First page.Al Wrather and Henry F. DiCarlo (Departments of Plant Pathology and Horticulture, College of Agriculture)New 5/85/4

Controlling nematodes in gardens

"Nematodes cause serious damage to gardens in Southeast Missouri. These pests can occur in other Missouri areas but are less common there. Nematodes are a greater problem where there are long, warm growing seasons and lighter, sandier soils."--First page.H.F. DiCarlo (Department of Horticulture), James A. Wrath (Plant Pathology, College of Agriculture)Revised 5/90/8

The Neural Representation Benchmark and its Evaluation on Brain and Machine

A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible to directly test representational learning algorithms directly against the representations contained in neural systems. Here, we propose a new benchmark for visual representations on which we have directly tested the neural representation in multiple visual cortical areas in macaque (utilizing data from [Majaj et al., 2012]), and on which any computer vision algorithm that produces a feature space can be tested. The benchmark measures the effectiveness of the neural or machine representation by computing the classification loss on the ordered eigendecomposition of a kernel matrix [Montavon et al., 2011]. In our analysis we find that the neural representation in visual area IT is superior to visual area V4. In our analysis of representational learning algorithms, we find that three-layer models approach the representational performance of V4 and the algorithm in [Le et al., 2012] surpasses the performance of V4. Impressively, we find that a recent supervised algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of IT for an intermediate level of image variation difficulty, and surpasses IT at a higher difficulty level. We believe this result represents a major milestone: it is the first learning algorithm we have found that exceeds our current estimate of IT representation performance. We hope that this benchmark will assist the community in matching the representational performance of visual cortex and will serve as an initial rallying point for further correspondence between representations derived in brains and machines.Comment: The v1 version contained incorrectly computed kernel analysis curves and KA-AUC values for V4, IT, and the HT-L3 models. They have been corrected in this versio

Implementing optimal control pulse shaping for improved single-qubit gates

We employ pulse shaping to abate single-qubit gate errors arising from the weak anharmonicity of transmon superconducting qubits. By applying shaped pulses to both quadratures of rotation, a phase error induced by the presence of higher levels is corrected. Using a derivative of the control on the quadrature channel, we are able to remove the effect of the anharmonic levels for multiple qubits coupled to a microwave resonator. Randomized benchmarking is used to quantify the average error per gate, achieving a minimum of 0.007+/-0.005 using 4 ns-wide pulse.Comment: 4 pages, 4 figure

Calculating energy derivatives for quantum chemistry on a quantum computer

Modeling chemical reactions and complicated molecular systems has been proposed as the `killer application' of a future quantum computer. Accurate calculations of derivatives of molecular eigenenergies are essential towards this end, allowing for geometry optimization, transition state searches, predictions of the response to an applied electric or magnetic field, and molecular dynamics simulations. In this work, we survey methods to calculate energy derivatives, and present two new methods: one based on quantum phase estimation, the other on a low-order response approximation. We calculate asymptotic error bounds and approximate computational scalings for the methods presented. Implementing these methods, we perform the world's first geometry optimization on an experimental quantum processor, estimating the equilibrium bond length of the dihydrogen molecule to within 0.014 Angstrom of the full configuration interaction value. Within the same experiment, we estimate the polarizability of the H2 molecule, finding agreement at the equilibrium bond length to within 0.06 a.u. (2% relative error).Comment: 19 pages, 1 page supplemental, 7 figures. v2 - tidied up and added example to appendice

Fresh market tomatoes

"In Missouri, there are opportun' ties for producing and marketing fresh market tomatoes, if growers can achieve high yields of uniformly high quality tomatoes. Resources essential for profitable production are productive land, sufficient water for irrigation, and adequate family labor. Timely and thorough implementation of cultural practices is required through all stages of production."--First page.Arthur E. Gaus, Henry F. DiCarlo, John B. Lower (Department of Horticulture College of Agriculture)New 10/83/10