The document discusses graphing trigonometric functions like sine and cosine curves. It explains that a sine curve is defined by the equation y = a sin(bx + c), where the period is 2π/b units, the amplitude is a units, and c shifts the curve left or right. A worked example with the equation y = 5 sin(9x - π/2) is shown. The cosine curve is similarly defined by y = a cos(bx + c), with the same period, amplitude, and shift properties. An example equation of y = -4 cos((x + π)/8) + 2 is provided and its graphing details are described.
Nonlinear Stochastic Programming by the Monte-Carlo methodSSA KPI
AACIMP 2010 Summer School lecture by Leonidas Sakalauskas. "Applied Mathematics" stream. "Stochastic Programming and Applications" course. Part 4.
More info at http://summerschool.ssa.org.ua
Aristidis Likas, Associate Professor and Christoforos Nikou, Assistant Professor, University of Ioannina, Department of Computer Science , Mixture Models for Image Analysis
Nonlinear Stochastic Programming by the Monte-Carlo methodSSA KPI
AACIMP 2010 Summer School lecture by Leonidas Sakalauskas. "Applied Mathematics" stream. "Stochastic Programming and Applications" course. Part 4.
More info at http://summerschool.ssa.org.ua
Aristidis Likas, Associate Professor and Christoforos Nikou, Assistant Professor, University of Ioannina, Department of Computer Science , Mixture Models for Image Analysis
WAVELET-PACKET-BASED ADAPTIVE ALGORITHM FOR SPARSE IMPULSE RESPONSE IDENTIFI...bermudez_jcm
Presented at IEEE ICASSP-2007:
This paper proposes a wavelet-packet-based (WPB) algorithm for efficient identification of sparse impulse responses with arbitrary frequency spectra. The discrete wavelet packet transform (DWPT) is adaptively tailored to the energy distribution of the unknown system\'s response spectrum. The new algorithm leads to a reduced number of active coefficients and to a reduced computational complexity, when compared to competing wavelet-based algorithms. Simulation results illustrate the applicability of the proposed algorithm.
The brain never rests. In the absence of external stimuli, fluctuations in cerebral activity reveal an intrinsic structure that mirrors brain function during cognitive tasks.
Learning and comparing multi-subject models of brain functional connecitivityGael Varoquaux
High-level brain function arises through functional interactions. These can be mapped via co-fluctuations in activity observed in functional imaging.
First, I first how spatial maps characteristic of on-going activity in a population of subjects can be learned using multi-subject decomposition models extending the popular Independent Component Analysis. These methods single out spatial atoms of brain activity: functional networks or brain regions. With a probabilistic model of inter-subject variability, they open the door to building data-driven atlases of on-going activity.
Subsequently, I discuss graphical modeling of the interactions between brain regions. To learn highly-resolved large scale individual
graphical models models, we use sparsity-inducing penalizations introducing a population prior that mitigates the data scarcity at the subject-level. The corresponding graphs capture better the community structure of brain activity than single-subject models or group averages.
Finally, I address the detection of connectivity differences between subjects. Explicit group variability models of the covariance structure can be used to build optimal edge-level test statistics. On stroke patients resting-state data, these models detect patient-specific functional connectivity perturbations.
WAVELET-PACKET-BASED ADAPTIVE ALGORITHM FOR SPARSE IMPULSE RESPONSE IDENTIFI...bermudez_jcm
Presented at IEEE ICASSP-2007:
This paper proposes a wavelet-packet-based (WPB) algorithm for efficient identification of sparse impulse responses with arbitrary frequency spectra. The discrete wavelet packet transform (DWPT) is adaptively tailored to the energy distribution of the unknown system\'s response spectrum. The new algorithm leads to a reduced number of active coefficients and to a reduced computational complexity, when compared to competing wavelet-based algorithms. Simulation results illustrate the applicability of the proposed algorithm.
The brain never rests. In the absence of external stimuli, fluctuations in cerebral activity reveal an intrinsic structure that mirrors brain function during cognitive tasks.
Learning and comparing multi-subject models of brain functional connecitivityGael Varoquaux
High-level brain function arises through functional interactions. These can be mapped via co-fluctuations in activity observed in functional imaging.
First, I first how spatial maps characteristic of on-going activity in a population of subjects can be learned using multi-subject decomposition models extending the popular Independent Component Analysis. These methods single out spatial atoms of brain activity: functional networks or brain regions. With a probabilistic model of inter-subject variability, they open the door to building data-driven atlases of on-going activity.
Subsequently, I discuss graphical modeling of the interactions between brain regions. To learn highly-resolved large scale individual
graphical models models, we use sparsity-inducing penalizations introducing a population prior that mitigates the data scarcity at the subject-level. The corresponding graphs capture better the community structure of brain activity than single-subject models or group averages.
Finally, I address the detection of connectivity differences between subjects. Explicit group variability models of the covariance structure can be used to build optimal edge-level test statistics. On stroke patients resting-state data, these models detect patient-specific functional connectivity perturbations.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
4. Graphing Trig
1) Sine Curve
Functions
y
1
2 2 3 4 x
-1
5. Graphing Trig
1) Sine Curve
Functions
y
1
2 2 3 4 x
-1
6. Graphing Trig
1) Sine Curve
Functions
y
1
2 2 3 4 x
-1
7. Graphing Trig
1) Sine Curve
Functions
y
1
2 2 3 4 x
-1
8. Graphing Trig
1) Sine Curve
Functions
y
1 y sin x
2 2 3 4 x
-1
9. Graphing Trig
1) Sine Curve
Functions
y
1 y sin x
2 2 3 4 x
-1
domain : all real x
10. Graphing Trig
1) Sine Curve
Functionsy
1 y sin x
2 2 3 4 x
-1
domain : all real x
range : - 1 y 1
11. Graphing Trig
1) Sine Curve
Functionsy
period
1 y sin x
2 2 3 4 x
-1
domain : all real x
range : - 1 y 1
12. Graphing Trig
1) Sine Curve
Functionsy
period
1 y sin x
2 2 3 4 x
-1
In general;
domain : all real x y a sin bx c
range : - 1 y 1 2
period units
b
13. Graphing Trig
1) Sine Curve
Functions
y
period
1 y sin x
amplitude
2 2 3 4 x
-1
In general;
domain : all real x y a sin bx c
range : - 1 y 1 2
period units
b
14. Graphing Trig
1) Sine Curve
Functions
y
period
1 y sin x
amplitude
2 2 3 4 x
-1
In general;
domain : all real x y a sin bx c
range : - 1 y 1 2
period units
b
amplitude a units
15. Graphing Trig
1) Sine Curve
Functions
y
period
1 y sin x
amplitude
2 2 3 4 x
-1
In general;
domain : all real x y a sin bx c
range : - 1 y 1 2 period
period units divisions
b 4
amplitude a units
16. Graphing Trig
1) Sine Curve
Functions
y
period
1 y sin x
amplitude
2 2 3 4 x
-1
In general;
domain : all real x y a sin bx c
range : - 1 y 1 2 period
period units divisions
b 4
c
amplitude a units shift to left
b
18.
e.g. y 5 sin 9 x
2
2 period units
9
19.
e.g. y 5 sin 9 x
2
2 period units
9
amplitude 5 units
20.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units
21.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units shift to right
18
22.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units shift to right
18
y
5
2
x
9 -5 9 9
23.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units shift to right
18
y
5
2
x
9 -5 9 9
24.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units shift to right
18
y
5
2
x
9 -5 9 9
25.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units shift to right
18
y
5
2
x
9 -5 9 9
26.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units shift to right
18
y
5
2
x
9 -5 9 9
27.
e.g. y 5 sin 9 x
2
2 period units divisions
9 18
amplitude 5 units shift to right
18
y 9x
y 5 sin
5 2
2
x
9 -5 9 9
31. 2) Cosine Curve y a cosbx c
2
period units
b
amplitude a units
32. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
amplitude a units
33. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
34. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2
e.g. y 4 cos
8
35. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period
8 1
8
16
36. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period
8 1
8
16
amplitude 4
37. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8
16
amplitude 4
38. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8 shift 8 to left, 2 up,
16 upside down
amplitude 4
39. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8 shift 8 to left, 2 up,
16 upside down
y
amplitude 4
6
2
8 8 16 x
-2
40. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8 shift 8 to left, 2 up,
16 upside down
y
amplitude 4
6
2
8 8 16 x
-2
41. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8 shift 8 to left, 2 up,
16 upside down
y
amplitude 4
6
2
8 8 16 x
-2
42. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8 shift 8 to left, 2 up,
16 upside down
y
amplitude 4
6
2
8 8 16 x
-2
43. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8 shift 8 to left, 2 up,
16 upside down
y
amplitude 4
6
2
8 8 16 x
-2
44. 2) Cosine Curve y a cosbx c
2 period
period units divisions
b 4
c
amplitude a units shift to left
b
x 2 2
e.g. y 4 cos period divisions 4
8 1
8 shift 8 to left, 2 up,
16 upside down
y
amplitude 4
y 4 cos 2
x
6
8
2
8 8 16 x
-2
48. 3) Tangent Curve y a tan bx c divisions
period
2
period units
b
49. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
50. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2
51. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2
period
1
52. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2 1
period divisions
2
1
53. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2 1
period divisions
2
1 shift 2 to right
54. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2 1
period divisions
2
1 shift 2 to right
y
1 2 x
55. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2 1
period divisions
2
1 shift 2 to right
y
1 2 x
56. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2 1
period divisions
2
1 shift 2 to right
y
1 2 x
57. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2 1
period divisions
2
1 shift 2 to right
y
y e tan x 2
1 2 x
58. 3) Tangent Curve y a tan bx c divisions
period
c
2
period units shift to left
b b
e.g. y e tan x 2 1
period divisions
2
1 shift 2 to right
y
y e tan x 2
Exercise 14C; 2b, 3b,
1 2 x 4b, 5bce, 8, 9, 10b, 13,
15, 16, 17, 20