Recognition system design by statistical analysis

Kanal, Laveen N.; Randall, Neil

doi:10.1145/800257.808903

Cited by 13 publications

(3 citation statements)

References 2 publications

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“…Laveen N. Kanal, Neil C. Randall, and Thomas Harley at the Philco Corporation, for example, attempted to screen aerial photographs for military tanks. 6,11 A small section of the film was processed to enhance any edges, and the result presented to the target detection system as a 32Â32 array of 1's and 0's. This array was segmented into 24 overlapping 8Â8 ''feature blocks'', and each of which was then evaluated using a statistical test to establish if the block contained a part of the tank.…”

Section: What Is Artificial Intelligence?mentioning

confidence: 99%

Machine Learning in Chemistry

2020

Theoretical and Computational Chemistry Series

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Computers excel at many tasks that humans are very bad at (see Figure 1.1). For example, if you have a smartphone with a voice assistant, try asking it what the square root of 32 761 is. For the phone, this problem is very straightforward, and it almost immediately returns the correct answer of 181. If instead, we were to ask a person the same question, they would probably find it very challenging. However, if they get the correct answer as quickly as the phone, we will think they are a genius (or perhaps more likely that they are cheating!). Now, for comparison, try to engage your voice assistant with a knock-knock joke, and it doesn't work. In contrast to the mathematical challenge, the machine finds this social interaction impossibly complicated, but we would think a person a simpleton if he/she is not able to partake in it. Children learn how to make these jokes before they learn to add. This illustrates why computers make such good scientific companions as they help scientists to be better at the things that they are naturally much worse at.Computational science is a scientific field, which uses a computational methodology to answer scientific questions. Computational scientists are most often trying to answer questions in fields such as chemistry, physics or biology using the algorithms developed by computer scientists. This is, in some respects, analogous to an experimental chemist who uses a

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Section: What Is Artificial Intelligence?mentioning

confidence: 99%

Machine Learning in Chemistry

2020

Theoretical and Computational Chemistry Series

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“…While not necessarily a problem in the training dataset, if new data is drawn from different strains this can lead to much poorer prediction than expected. This effect has been known since early efforts to statistically classify images, where from as early as 1964 it was shown that pictures of tanks were recognised by the background they appeared in, rather than the shape of the tank itself (19) . In genetics, a similar problem has arisen due to an overrepresentation of samples of European ancestry in genotype databases.…”

Section: Introductionmentioning

confidence: 98%

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

Lees

Mai

Wheeler

et al. 2019

Preprint

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Discovery of influential genetic variants and prediction of phenotypes such as antibiotic resistance are becoming routine tasks in bacterial genomics. Genome-wide association study (GWAS) methods can be applied to study bacterial populations, with a particular emphasis on alignment-free approaches, which are necessitated by the more plastic nature of bacterial genomes. Here we advance bacterial GWAS by introducing a computationally scalable joint modeling framework, where genetic variants covering the entire pangenome are compactly represented by unitigs, and the model fitting is achieved using elastic net penalization. In contrast to current leading GWAS approaches, which test each genotype-phenotype association separately for each variant, our joint modelling approach is shown to lead to increased statistical power while maintaining control of the false positive rate. Our inference procedure also delivers an estimate of the narrow-sense heritability, which is gaining considerable interest in studies of bacteria. Using an extensive set of state-of-the-art bacterial population genomic datasets we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. We expect that these advances will pave the way for the next generation of high-powered association and prediction studies for an increasing number of bacterial species.

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“…Mott-Smith and Langmuir [1] discussed collection characteristics of charged particles with a drifting Maxwellian distribution function. Kanal [2] derived mathematical formulas of ion current collected by a cylindrical probe set in a drifting plasma by taking account of ion orbital motion around the collecting surface. Stangeby and Allen [3] investigated density profi le around a spherical obstacle set in a drifting plasma and discussed ion currents as a function of collecting angle with respect to the plasma fl ow direction.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Para-Perp Type Mach Probe by Using a Fast Flowing Plasma

Andõ

Watanabe

et al. 2005

J. Plasma Fusion Res.

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Mach probe with two collecting tips facing to the direction parallel and perpendicular to the plasma fl ow, named para-perp type one, is evaluated by using a directional Langmuir probe (DLP) in a fast fl owing plasma produced by an MPD (Magneto-Plasma-Dynamic) arcjet. We present simple formulas to determine an ion acoustic Mach number M i from the ratio of collected ion saturation currents. The formulas and experimentally-obtained data are compared with Hutchinson's simulation results which are calculated using a particle-in-cell(PIC) code in an unmagnetized plasma condition. [I.H. Hutchinson, Plasma Phys. Control. Fusion, 44 1953.] Good agreements are obtained among these data in both subsonic and supersonic plasma fl ows. Correction factor κ to determine M i is presented under various conditions of T i /T e and the specifi c heat ratio for ions γ i .

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Recognition system design by statistical analysis

Cited by 13 publications

References 2 publications

Machine Learning in Chemistry

Machine Learning in Chemistry

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

Evaluation of Para-Perp Type Mach Probe by Using a Fast Flowing Plasma

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