#import "@preview/polylux:0.3.1": *
#import themes.metropolis: *

#show: metropolis-theme.with(
  footer: [CC BY-SA 4.0 Arthur Grisel-Davy]
)

#set text(font: "Fira Sans", weight: "light", size: 20pt)
#show math.equation: set text(font: "Fira Math")
#set strong(delta: 100)
#set par(justify: true)

#title-slide(
  author: [Arthur Grisel-Davy, Sebastian Fischmeister],
  title: "MAD: One-Shot Machine Activity Detector for Physics-Based Cyber Security",
  subtitle: "",
  date: "University of Waterloo",
  extra: "agriseld@uwaterloo.ca"
)

//#slide(title: "Table of contents")[
//  #metropolis-outline
//]

#slide(title: "Introduction")[
    #only(1)[#figure(image("images/wein_p1.svg", height: 100%))]
    #only(2)[#figure(image("images/wein_p2.svg", height: 100%))]
    #only(3)[#figure(image("images/wein_p3.svg", height: 100%))]
    #only(4)[#figure(image("images/wein_p4.svg", height: 100%))]
    #only(5)[#figure(image("images/wein_p5.svg", height: 100%))]
]


#slide(title: "Problem Statement")[
#align(center)[Given a #text(fill: blue, weight:400 )[discretized time series $t$] and a #text(fill: red, weight:400)[set of patterns $P=\{P_1, dots.h, P_n\}$], identify a mapping $m: NN arrow.r P union lambda$ such that every sample $t[i]$ maps to a pattern in $P union lambda$ with the condition that the sample #text(fill: purple, weight: 400)[matches] an occurrence of the pattern in $t$.]
]

#slide(title: "Proposed Approach")[
]

#slide(title: "Proposed Approach - 2D Interpretation")[
]

#slide(title: "Parameter")[]

#slide(title: "Case Study 1")[]
#slide(title: "Case Study 1 - Results")[]


#slide(title: "Case Study 2")[]
#slide(title: "Case Study 2 - Results")[]

#slide(title: "Futur Work")[]

#slide(title: "Conclusion")[
]


#focus-slide()[
#align(center)[Thank you for your attention.]
]


//#new-section-slide([Second section])
//
//#focus-slide[
//  Wake up!
//]