•Download as PPTX, PDF•

0 likes•7 views

The document discusses methods to remove tiling artefacts from Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) data. It compares 6 different methods on 4 datasets, including a mouse brain sample, and uses statistical analysis and a user survey to evaluate the methods. The results found that the Seamless Stitching and Linear Multiplicative methods generally performed best, though the best method depended on the dataset. The document advocates testing multiple methods to select the one most suitable for the specific dataset, rather than relying on a single approach.

Report

Share

Report

Share

Although we often told not to do it, statistical scientists frequently predict the value of outcome measures of physical systems at input points far the observed data. Since predictions are made in new regions of the input space, a statistical theory cannot dictate optimal rules for measures of uncertainty associated with extrapolation. This talk presents several solutions based on simple principles. The solutions are illustrated via the analysis of data generated by dropping spheres of varying radii and masses from different heights. Some of the techniques apply to more complex physical systems. The efficacy of these techniques is demonstrated using data (experimental and simulated) of the level of complexity physical scientist frequently face. Scientists should tailor these techniques to fit the needs of a particular application.MUMS: Transition & SPUQ Workshop - Some Strategies to Quantify Uncertainty fo...

MUMS: Transition & SPUQ Workshop - Some Strategies to Quantify Uncertainty fo...The Statistical and Applied Mathematical Sciences Institute

Although we often told not to do it, statistical scientists frequently predict the value of outcome measures of physical systems at input points far the observed data. Since predictions are made in new regions of the input space, a statistical theory cannot dictate optimal rules for measures of uncertainty associated with extrapolation. This talk presents several solutions based on simple principles. The solutions are illustrated via the analysis of data generated by dropping spheres of varying radii and masses from different heights. Some of the techniques apply to more complex physical systems. The efficacy of these techniques is demonstrated using data (experimental and simulated) of the level of complexity physical scientist frequently face. Scientists should tailor these techniques to fit the needs of a particular application.MUMS: Transition & SPUQ Workshop - Some Strategies to Quantify Uncertainty fo...

MUMS: Transition & SPUQ Workshop - Some Strategies to Quantify Uncertainty fo...The Statistical and Applied Mathematical Sciences Institute

Biostatistics CH Lecture Pack

Biostatistics CH Lecture Pack

Teaching Population Genetics with R

Teaching Population Genetics with R

fNIRS data analysis

fNIRS data analysis

Some Developments in Space-Time Modelling with GIS Tao Cheng – University Col...

Some Developments in Space-Time Modelling with GIS Tao Cheng – University Col...

Fast Single-pass K-means Clusterting at Oxford

Fast Single-pass K-means Clusterting at Oxford

Xrd nanomaterials course_s2015_bates

Xrd nanomaterials course_s2015_bates

Computational Tools for Extracting, Representing and Analyzing Facial Features

Computational Tools for Extracting, Representing and Analyzing Facial Features

Predictive Modelling

Predictive Modelling

Isolation Forest

Isolation Forest

ACM 2013-02-25

ACM 2013-02-25

⭐⭐⭐⭐⭐ Finding a Dynamical Model of a Social Norm Physical Activity Intervention

⭐⭐⭐⭐⭐ Finding a Dynamical Model of a Social Norm Physical Activity Intervention

Statistics for analytics

Statistics for analytics

MUMS: Transition & SPUQ Workshop - Some Strategies to Quantify Uncertainty fo...

MUMS: Transition & SPUQ Workshop - Some Strategies to Quantify Uncertainty fo...

Robots, Small Molecules & R

Robots, Small Molecules & R

Simple math for anomaly detection toufic boubez - metafor software - monito...

Simple math for anomaly detection toufic boubez - metafor software - monito...

Statistics - Basics

Statistics - Basics

Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in R

Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in R

Learning to Search Henry Kautz

Learning to Search Henry Kautz

Learning to Search Henry Kautz

Learning to Search Henry Kautz

Physics, Astrophysics & Simulation of Gravitational Wave Source (Lecture 2)

Physics, Astrophysics & Simulation of Gravitational Wave Source (Lecture 2)

Explainable insights on algorithm performance

Explainable insights on algorithm performance

Sophisticated tools for spatio-temporal data exploration

Sophisticated tools for spatio-temporal data exploration

Explainable algorithm evaluation from lessons in education

Explainable algorithm evaluation from lessons in education

A time series of networks. Is everything OK? Are there anomalies?

A time series of networks. Is everything OK? Are there anomalies?

Explainable algorithm evaluation.pptx

Explainable algorithm evaluation.pptx

Anomalous Networks

Anomalous Networks

Four, fast geostatistical methods - a comparison

Four, fast geostatistical methods - a comparison

Comparison of geostatistical methods for spatial data

Comparison of geostatistical methods for spatial data

From ensembles to computer networks

From ensembles to computer networks

Algorithm evaluation using Item Response Theory

Algorithm evaluation using Item Response Theory

Getting better at detecting anomalies by using ensembles

Getting better at detecting anomalies by using ensembles

Evaluating algorithms using Item Response Theory

Evaluating algorithms using Item Response Theory

Anomalies! You can't escape them.

Anomalies! You can't escape them.

Anomalies and events keep us on our toes

Anomalies and events keep us on our toes

Mathematics of anomalies

Mathematics of anomalies

Here is the anomalow-down!

Here is the anomalow-down!

Looking out for anomalies

Looking out for anomalies

Algorithm evaluation using item response theory

Algorithm evaluation using item response theory

WASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 Rp

WASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 Rp

Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...

Gliese 12 b: A Temperate Earth-sized Planet at 12 pc Ideal for Atmospheric Tr...

Microbial bio Synthesis of nanoparticles.pptx

Microbial bio Synthesis of nanoparticles.pptx

MODERN PHYSICS_REPORTING_QUANTA_.....pdf

MODERN PHYSICS_REPORTING_QUANTA_.....pdf

Film Coated Tablet and Film Coating raw materials.pdf

Film Coated Tablet and Film Coating raw materials.pdf

The solar dynamo begins near the surface

The solar dynamo begins near the surface

GBSN - Biochemistry (Unit 4) Chemistry of Carbohydrates

GBSN - Biochemistry (Unit 4) Chemistry of Carbohydrates

Plasmapheresis - Dr. E. Muralinath - Kalyan . C.pptx

Plasmapheresis - Dr. E. Muralinath - Kalyan . C.pptx

Quantifying Artificial Intelligence and What Comes Next!

Quantifying Artificial Intelligence and What Comes Next!

ERTHROPOIESIS: Dr. E. Muralinath & R. Gnana Lahari

ERTHROPOIESIS: Dr. E. Muralinath & R. Gnana Lahari

Constraints on Neutrino Natal Kicks from Black-Hole Binary VFTS 243

Constraints on Neutrino Natal Kicks from Black-Hole Binary VFTS 243

mixotrophy in cyanobacteria: a dual nutritional strategy

mixotrophy in cyanobacteria: a dual nutritional strategy

A Giant Impact Origin for the First Subduction on Earth

A Giant Impact Origin for the First Subduction on Earth

The Scientific names of some important families of Industrial plants .pdf

The Scientific names of some important families of Industrial plants .pdf

INSIGHT Partner Profile: Tampere University

INSIGHT Partner Profile: Tampere University

Climate extremes likely to drive land mammal extinction during next supercont...

Climate extremes likely to drive land mammal extinction during next supercont...

Detectability of Solar Panels as a Technosignature

Detectability of Solar Panels as a Technosignature

GBSN - Microbiology Lab 1 (Microbiology Lab Safety Procedures)

GBSN - Microbiology Lab 1 (Microbiology Lab Safety Procedures)

National Biodiversity protection initiatives and Convention on Biological Di...

National Biodiversity protection initiatives and Convention on Biological Di...

Jet reorientation in central galaxies of clusters and groups: insights from V...

Jet reorientation in central galaxies of clusters and groups: insights from V...

- 1. The painful removal of tiling artefacts in ToF-SIMS data Sevvandi Kandanaarachchi, Wil Gardner, David Alexander, Ben Muir, Philippe Chouinard, Sheila Crewther, David Scurr, Mark Halliday, Paul Pigram 21-11-2023
- 3. What are tiling artefacts? A noise only portion A mouse brain sample
- 4. What’s the story? • ToF-SIMS data have tiling artefacts • We want to remove them – to make the data cleaner • Multiple methods to remove tiling artefacts • Didn’t pick one, instead we did a comparison study • We did a statistical analysis and a survey
- 5. Comparison study • 4 Datasets • 1 biological dataset (mouse brain), 2 dye datasets, 1 resistor • 6 Methods • 1 tailored method for tiling removal (Seamless Stitching) – (Legesse et al, 2015) • 3 statistical methods (LDA, tensor decomposition, linear multiplicative) • 2 simple benchmarks (averaging and interpolating)
- 7. Mouse Dataset • Biological dataset • Curves • Intensity variation
- 8. Dye and Polymer datasets • The same shape - circles • Repetition • More regular repetition in Dye compared to polymer • No intricate curves
- 9. Resistor dataset • Same structure repeated twice • No intricate curves • Bigger tiles compared to the other datasets
- 10. Tiling artefact removal methods • Seamless stitching • Linear Discriminant Analysis • Tensor Decomposition • Linear Multiplicative method • Simple Averaging • Simple Interpolation
- 11. What is a tensor? • Not the technical definition • 3D data cube, or a 4D or 5D data cube X axis Y axis Time
- 12. Candecomp/PARAFAC tensor decomposition 𝑎𝑖𝑗𝑘 ≈ 𝑥𝑖 𝑦𝑗𝑧𝑘 𝑥 𝑦 𝑧
- 13. Candecomp/PARAFAC tensor decomposition 𝑎𝑖𝑗𝑘 ≈ 𝑥𝑖 1 𝑦𝑗 1 𝑧𝑘 1 + 𝑥𝑖 2 𝑦𝑗 2 𝑧𝑘 2 𝑥(1) 𝑦(1) 𝑧(1) 𝑥(2) 𝑦(2) 𝑧(2) +
- 14. Methods Approximate Tiling Artefacts and Remove Smoothen around Edges Hybrid 1. Tensor Decomposition 2. LDA 3. Linear Multiplicative method 1. Simple averaging 2. Simple Interpolation Seamless Stitching Approximation Original
- 16. Dye data
- 17. Polymer data
- 18. Resistor data
- 19. How do we evaluate these methods? • Mutual information between position and intensity • Correlation • Measure periodic nature and its reduction Statistical methods Survey A big thank you all survey participants! • Ask two types of questions. • How well the information was retained after tiling artefact removal. • How well the artefacts were removed No standard evaluation methods in literature for ToF-SIMS tiling artefact removal.
- 21. Results from the survey Rank Brain - Remove artefacts Brain - Retain information • Depending on the question asked participants selected different methods.
- 22. Summary of results • Overall Seamless Stitching and Linear Multiplicative methods were preferred more. • Seamless stitching was popular for the mouse brain dataset • No method was always supreme – No free lunch! • Depending on the dataset characteristics, the preferred methods were different. • Message: caution researchers not to latch on to one method. Try out many methods and select the best one for the dataset.
- 23. Resources • Code: https://github.com/sevvandi/supplementary_material/tree/master/ToF- SIMS • Paper: https://pubs.acs.org/doi/10.1021/acs.analchem.3c03887
- 24. Thank you!
- 26. kk = 1998
- 28. Strong periodic signal • In a time series context seasonality • Time series features can capture this • 𝑦𝑡 = 𝑓𝑡 + 𝑠𝑡 + 𝑒𝑡 (STL decomposition) • 𝑓𝑡 - trend • 𝑠𝑡 - seasonality • 𝑒𝑡 - error term • Strength of seasonality = 1 − 𝑣𝑎𝑟 𝑒𝑡 𝑣𝑎𝑟 𝑠𝑡+𝑒𝑡 • Strength of seasonality = 1 − 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑜𝑓 𝑒𝑟𝑟𝑜𝑟 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑜𝑓( 𝑒𝑟𝑟𝑜𝑟+𝑠𝑒𝑎𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦)
- 29. For this dataset • We need to set the period. In this case it is 10. • X-seasonality = 0.93 • Y-seasonality = 0.89 • There is a strong seasonal component. • Of the remaining data if we recompute parafac and find seasonality we get • X-remaining-seasonality = 0.02 • Y-remaining-seasonality = 0.009
- 30. k = 56 - more subtle
- 31. k = 964
- 32. New linear, multiplicative method A noise only portion
- 33. Model the noise – look at one tile • Noise increases as x and y increases • Can we model this? • 𝑧𝑥,𝑦 = 𝑧𝑥,𝑦 × 𝑛𝑥,𝑦 • where 𝑧𝑥,𝑦 = observed value at 𝑥, 𝑦 • 𝑧𝑥,𝑦 is the true value at 𝑥, 𝑦 • 𝑛𝑥,𝑦 is the noise at 𝑥, 𝑦 is the relative position on the tile • 𝑥 = 𝑥 𝑚𝑜𝑑 𝑡𝑖𝑙𝑒𝑠𝑖𝑧𝑒 • 𝑦 = 𝑦 𝑚𝑜𝑑 𝑡𝑖𝑙𝑒𝑠𝑖𝑧𝑒
- 34. Model • 𝑧𝑥,𝑦 = 𝑧𝑥,𝑦 × 𝑛𝑥,𝑦 • Model 𝑛𝑥,𝑦 • 𝑛𝑥,𝑦 = 𝑎0 + 𝑎𝑥 𝑥 + 𝑎𝑦 𝑦 • Then 𝑧𝑥,𝑦/𝑛𝑥,𝑦 = 𝑧𝑥,𝑦 • Instead of modelling using one tile, we can use several noise tiles to model it