University of Waterloo

About: University of Waterloo is a education organization based out in Waterloo, Ontario, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 36093 authors who have published 93906 publications receiving 2948139 citations. The organization is also known as: UW & uwaterloo.

Quantum Amplitude Amplification and Estimation

Abstract: Consider a Boolean function $\chi: X \to \{0,1\}$ that partitions set $X$ between its good and bad elements, where $x$ is good if $\chi(x)=1$ and bad otherwise. Consider also a quantum algorithm $\mathcal A$ such that $A |0\rangle= \sum_{x\in X} \alpha_x |x\rangle$ is a quantum superposition of the elements of $X$, and let $a$ denote the probability that a good element is produced if $A |0\rangle$ is measured. If we repeat the process of running $A$, measuring the output, and using $\chi$ to check the validity of the result, we shall expect to repeat $1/a$ times on the average before a solution is found. *Amplitude amplification* is a process that allows to find a good $x$ after an expected number of applications of $A$ and its inverse which is proportional to $1/\sqrt{a}$, assuming algorithm $A$ makes no measurements. This is a generalization of Grover's searching algorithm in which $A$ was restricted to producing an equal superposition of all members of $X$ and we had a promise that a single $x$ existed such that $\chi(x)=1$. Our algorithm works whether or not the value of $a$ is known ahead of time. In case the value of $a$ is known, we can find a good $x$ after a number of applications of $A$ and its inverse which is proportional to $1/\sqrt{a}$ even in the worst case. We show that this quadratic speedup can also be obtained for a large family of search problems for which good classical heuristics exist. Finally, as our main result, we combine ideas from Grover's and Shor's quantum algorithms to perform amplitude estimation, a process that allows to estimate the value of $a$. We apply amplitude estimation to the problem of *approximate counting*, in which we wish to estimate the number of $x\in X$ such that $\chi(x)=1$. We obtain optimal quantum algorithms in a variety of settings.

Methods for isolating and characterizing ACC deaminase-containing plant growth-promoting rhizobacteria.

Abstract: One of the major mechanisms utilized by plant growth-promoting rhizobacteria (PGPR) to facilitate plant growth and development is the lowering of ethylene levels by deamination of 1-aminocyclopropane-1-carboxylic acid (ACC) the immediate precursor of ethylene in plants. The enzyme catalysing this reaction, ACC deaminase, hydrolyses ACC to α-ketobutyrate and ammonia. Several bacterial strains that can utilize ACC as a sole source of nitrogen have been isolated from rhizosphere soil samples. All of these strains are considered to be PGPR based on the ability to promote canola seedling root elongation under gnotobiotic conditions. The treatment of plant seeds or roots with these bacteria reduces the amount of ACC in plants, thereby lowering the concentration of ethylene. Here, a rapid procedure for the isolation of ACC deaminase-containing bacteria, a root elongation assay for evaluating the effects of selected bacteria on root growth, and a method of assessing bacterial ACC deaminase activity are described in detail. This should allow researchers to readily isolate new PGPR strains adapted to specific environments.

Classification of imbalanced data: a review

Abstract: Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. This paper provides a review of the classification of imbalanced data regarding: the application domains; the nature of the problem; the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.

Sodium and sodium-ion energy storage batteries

Abstract: Owing to almost unmatched volumetric energy density, Li-ion batteries have dominated the portable electronics industry and solid state electrochemical literature for the past 20 years. Not only will that continue, but they are also now powering plug-in hybrid electric vehicles and electric vehicles. In light of possible concerns over rising lithium costs in the future, Na and Na-ion batteries have re-emerged as candidates for medium and large-scale stationary energy storage, especially as a result of heightened interest in renewable energy sources that provide intermittent power which needs to be load-levelled. The sodium-ion battery field presents many solid state materials design challenges, and rising to that call in the past couple of years, several reports of new sodium-ion technologies and electrode materials have surfaced. These range from high-temperature air electrodes to new layered oxides, polyanion-based materials, carbons and other insertion materials for sodium-ion batteries, many of which hold promise for future sodium-based energy storage applications. In this article, the challenges of current high-temperature sodium technologies including Na-S and Na-NiCl2 and new molten sodium technology, Na-O2 are summarized. Recent advancements in positive and negative electrode materials suitable for Na-ion and hybrid Na/Li-ion cells are reviewed, along with the prospects for future developments.