英文PPT模板presentation
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英文PPT报告模板
英文PPT报告模板❖1、开场白正式场合做presentation,可能需要一些隆重点的开场白。
Ladies and gentleman. It's an honor to have the opportunity to address such a distinguished audience.女士们先生们,我感到很荣幸有机会在这么多杰出的观众面前发言。
Good morning, everyone. I appreciate the opportunity to be with you today.I am here to talk to you about..各位早上好。
很开心有这个机会跟你们一起。
今天在这里我主要想跟大家谈…….I am delighted/pleased/glad to have the opportunity to present/of making this presentation..我非常开心有机会在这做演示….如果是轻松场合,则可以随意一些。
Hello again everybody. Thank you for being on time/making the effort to come today.大家好。
谢谢大家今天抽出时间来这里。
I'm glad you could all get here.我很开心你们都能来….❖概述简单介绍一下你的展示,包括主要内容、展示时长和你准备如何回答问题。
主题The subject of my presentation is...今天演示主题是….I shall be speaking today about.…我今天主要谈论…My presentation concerns我的演示主要是关于Today's topic is...今天的题目是…Today we are here to give a presentation on... 今天我们要做一个关于……的演示。
商业英文PPT模板
are communication
tools.
Infographic slide report
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
Infographic slide report
Presentations are communication tools that can be used as demonstrations.
EX AM PL E
4.3 %
21.3 %
31.9 %
21.3 %
21.3 %
EXAMPLE
9.5
9.5
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
B USINESS AND C ORPORATE
Business Infographic Template
Hello & welcome to presentation
WEL C OM E M ESSAGE
Presentations are communication tools that can be used as demonstrations, lectures, speeches, reports, and more. It is mostly presented before an audience.
tools.
Infographic slide report
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
Infographic slide report
Presentations are communication tools that can be used as demonstrations.
EX AM PL E
4.3 %
21.3 %
31.9 %
21.3 %
21.3 %
EXAMPLE
9.5
9.5
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
PRODUCTIN
Presentations are communication tools that can be used as demonstrations.
B USINESS AND C ORPORATE
Business Infographic Template
Hello & welcome to presentation
WEL C OM E M ESSAGE
Presentations are communication tools that can be used as demonstrations, lectures, speeches, reports, and more. It is mostly presented before an audience.
PPT英文presentation
c1 c2 c3
a1=cos(k1)*cos(k3)-sin(k1)*sin(k2)*sin(k3); a2=-cos(k1)*sin(k3)-sin(k1)*sin(k2)*cos(k3); a3=-sin(k1)*cos(k2); b1=cos(k2)*sin(k3); b2=cos(k2)*cos(k3); b3=-sin(k2); c1=sin(k1)*cos(k3)+cos(k1)*sin(k2)*sin(k3); c2=-sin(k1)*sin(k3)+cos(k1)*sin(k2)*cos(k3); c3=cos(k1)*cos(k2); ,where k1,k2,k3 is the camera calibration, this three datas have been known.
The test data were included residential, urban streets, and highway scenes. The data is in binary format containing around 2.24 GB LiDAR data (about 126 million points) and 97MB panoramic images (200 spherical panoramas).
2
——,WwahnegreRH, FaenrrdieWF aPr,eMthacefhareliagnhetJa. nAduwtoimdathticofretghiestrpaatinoonraomf micoibmilaegLeisDAR and
sphereicsaplecptiavneolrya.mas[C]//Computer Vision and Pattern Recognition Workshops
英语演讲PresentationPPT模板
Here are three ideas from the dreamer
First idea: Create a happy pill that makes people feel happy and positive.provide it free to employees. As a realist How can this be made into a benefit?How are attitudes adjusted?
a critic reviews all the ideas and tries to punch holes in them.
To be a dreamer, we should ask:
if i could ......——what would i create? how would it look?
nothing is silly.all things are possible for the dreamers.
Realist
the realist imagineers the dreamer's ideas into something realistic and feasible.
Critic
As a realist What are some aspects of cars that can be engineered into ideas?
TEAMWORK
Disney uesd the same three strategies to keep his stuff coordinated in their thinking.
I think that by using the cycle ,our creative thinking can be transformed into imagineerings just like what Walt Disney did.
英文主题小演讲：Presentation-拖延症PPT课件
Acknowledge that you are postponing （推迟）a task.
Fear of failure
Lack of focus
Unclear goals
Tips for avoiding procrastination.
1. Think and plan on paper.
拖延症
First，let's consider the following question
1.Have you always been reluctant (不情愿的)to go to bed in the evening, while reluctant to wake up in the morning?
Don’t want to make a mistake until you’re sure it’s right. Everyone will know you made a mistake.
Four Complex Reasons
Perfectionism
Anger/Hostility
Low Frustration Tolerance Self-Downing
Don't procratinate any more,
What makes you most happy is to be able to do what you wanted to do.
Thank you~
个人观点供参考，欢迎讨论
If I can’t do it right, don’t do it at all
Withhold best efforts out of spite
1. Feeling overwhelmed（受打击) 2. Unfairness
Fear of failure
Lack of focus
Unclear goals
Tips for avoiding procrastination.
1. Think and plan on paper.
拖延症
First，let's consider the following question
1.Have you always been reluctant (不情愿的)to go to bed in the evening, while reluctant to wake up in the morning?
Don’t want to make a mistake until you’re sure it’s right. Everyone will know you made a mistake.
Four Complex Reasons
Perfectionism
Anger/Hostility
Low Frustration Tolerance Self-Downing
Don't procratinate any more,
What makes you most happy is to be able to do what you wanted to do.
Thank you~
个人观点供参考，欢迎讨论
If I can’t do it right, don’t do it at all
Withhold best efforts out of spite
1. Feeling overwhelmed（受打击) 2. Unfairness
Presentation-公司、产品英文展示的范例ppt课件
26.91
-11.6
23.41
-3.5
23.41
0
1CIA World Factbook, 2021 2Fields, Sanchez-Puerta 2005
Planning Verse Market
• Index of Economic Freedom • The Index of Economic Freedom is “a
confidence in the government’s ability to pay off the debt. • the government may use the money for ‘special’ purposes.
• Solutions: • return the pension funds to the private organization. • improve the management of the banks and financial system.
0
0
0
0
0
0
10
0 10.0 10.0 0
Washington, D.C.: The Heritage Foundation and Dow Jones & Company, Inc., 2021
Examples for state-owned enterprises and price fixing
• According to the UNDP, Argentina was listed as ‘High’ on the Human Development Index in 2007.
• 97% adult literacy, Compulsary Education through 17, Free University Education.
大学英语课前演讲presentation—流行语PPT
· Catchword`s
expressive power is strong, it easily display the current young people`s game mentality of remain free, hunt for new thing， but it also reflect some young`s Impetuous mentality to some dedree. 流行语具有比较强的表现力，更能表现当下年轻人们无拘 无束、趋新猎奇的游戏心态，但是也一定程度上也反映了 社会上一些年轻人浮躁的心态。
No.1
To give strength or Ungelivable
给力不给力
Source：from a funny comic novel of the Westward Journey
六集《搞笑漫画日和》的《西游 记——旅程的终点》一集中，画面 一开始，师徒三人（动画设定为八 戒仙去了）历经磨难到达天竺后， 却发现所谓的天竺只有一面小旗子， 上书“天竺”二字。弄得师徒三人 都愣了神，悟空不无抱怨地说： “这就是天竺吗，不给力啊老湿。” 悟空的这番话也成为后来“给力” 一词的渊源。网友根据“给力”造 出一个新的英文单词——
NO.5
What I am smoking isn’t a cigarette, but loneliness! 哥抽的不是烟，是寂寞！
Source：
Someone uploads a picture of a maow: ’哥吃的不是面，是寂寞！’. Soon the similar sentence rise up everywhere.
Source：
Life is full of pain.Happy is a hard thing to us. So we must get rid of unhappy,speak it out, and do something to make others happy
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Epileptic Seizure Recognition
01 INTRODUCTION TO EPILEPSY
02 DATA UNDERSTANDING AND PREPARATION
A GENDA GROUP 6 PRESENTATION
03 MODELLING PROCESS 04 STRENGTHS, LIMITATIONS &
Data Transformation
Frequency Domain Dataset
Advantages • Remove the initial time offsets.
• Extract more recognizable features.
Limitation
• Might filter useful information of the original dataset.
Potential
Pure medical/research use
Pervasive domestic use (App/Website)
Continuous EEG record
Data Set for research purpose Individual patient’s customization
ACCURACY
96.73% 95.12%
97.12%
SENSITIVITY
79.23%
95.72% 97.00%
SPECIFICITY
96.99% 99.18%
97.16%
75.00% 80.00% 85.00% 90.00% 95.00% 100.00%
Model A - Neural Network Model C - Logistic Regression
Model B - Discriminant
True Prediction of Either Type True Prediction of Seizure Activity
True Prediction of Non-Seizure Activity
RESULT ANALYSIS & MODEL VALIDATION
Accuracy - True Prediction
98.00% 96.00%
NO.1
95.29%
94.00%
92.00%
90.00%
88.00%
86.00%
84.00%
82.00%
80.00% Actual Seizure
95.39% Potential Seizure
93.45% Healthy
accurate modelling
Auto Classifier
Computer’s job!! Models are constructed and
tested for each group
Validate Model
Analyze testing results of selected models
• Deliver more recognizable dataset.
MODELLING PROCESS
Reclassify Data Types
split or combine into customized groups based on
research purpose
Balance Sample Size of Each Group
02
Symptom
• Recurrent seizures • Loss of consciousness
• Damage to the brain, or even death. ( >30min )
03
Premature Death
Rate of People died prematurely
Epilesy Patients 8.8% Others 0.7%
Ensure even prediction ability for each group
Select Top 3 Accurate Models
Select based on model accuracy in model recalibration
Transformed Data
Reclassify
(University of Oxford and the Karolinska Institute, Stockholm)
Epilepsy EEG
• One of the most common neurological diseases
• Excessive electrical activity within networks of neurons in the brain
Data Partition
Balance
Model Construction
Model Selection
Model Validation
Partition Data Randomly for Different Use in Modelling
Assign proportion of data used in model building, recalibrate and test for
BEST!
Gain Chart How good each model predicting results approach the ideal condition?
Strengths of the Model
• Quick and Accurate. • Models are dynamic, and constantly learn from new data. • Cost effective. Targeted follow-up treatment • Saves times which benefits patients.
1.37
1.34
Deaths in millions Stroke lung cancer Lower respiratory infections
Chronic obstructive pulmonary disease Diabetes Diarrhoeal diseases
Seizure disorders
94.60% Overall
Model A - Neural Network
Model B - Logistic Regression Model C - C5
RESULT ANALYSIS & MODEL VALIDATION
3 Aspects of Validation in T/F Diagnose
• Detection & Prediction • Allow for interventional treatment • Improve current epilepsy diagnosis • Medical signal-pattern recognition
framework
Brain Activity
Properties • 178 Hz sampling rate • 1 second long
Types of EEG Waveforms
Healthy (Type A)
Healthy (Type B)
Potential Seizure (Type C)
Potential Seizure (Type D)
Potential
10
8.76
6.24
5
0
Ischaemic heart disease Trachea and bronchus cancers Dementias Tuberculosis
Top 10 causes of deaths globally
3.19
3.17
1.69
1.59
1.54
1.39
Actual Seizure (Type E)
Time Series
Frequency Series
Data Preparation
Discrete Fourier Transformation
Time Domain Dataset
������=π
4
������=π ������=π
2
f(t)= ������������������(������������ + ������)
Limitations of the Model
• Potential costs of misclassifying a patient. • Model has to be used as a supportive tool. • Model is only as good as data used to build it. • Abnormal EEG may be caused by other neurological disorders.
RESULT ANALYSIS & MODEL VALIDATION
Actual Seizure
Potential Seizure
Healthy
Actual
433
4
20
Seizure
Potential Seizure
10
873
29
Healthy
20
33
863
Coincidence Matrix of Logistic Regression
POTENTIALS
Epilepsy
01
Demography
• 65 million people worldwide suffer from epilepsy (the Epilepsy Foundation)
01 INTRODUCTION TO EPILEPSY
02 DATA UNDERSTANDING AND PREPARATION
A GENDA GROUP 6 PRESENTATION
03 MODELLING PROCESS 04 STRENGTHS, LIMITATIONS &
Data Transformation
Frequency Domain Dataset
Advantages • Remove the initial time offsets.
• Extract more recognizable features.
Limitation
• Might filter useful information of the original dataset.
Potential
Pure medical/research use
Pervasive domestic use (App/Website)
Continuous EEG record
Data Set for research purpose Individual patient’s customization
ACCURACY
96.73% 95.12%
97.12%
SENSITIVITY
79.23%
95.72% 97.00%
SPECIFICITY
96.99% 99.18%
97.16%
75.00% 80.00% 85.00% 90.00% 95.00% 100.00%
Model A - Neural Network Model C - Logistic Regression
Model B - Discriminant
True Prediction of Either Type True Prediction of Seizure Activity
True Prediction of Non-Seizure Activity
RESULT ANALYSIS & MODEL VALIDATION
Accuracy - True Prediction
98.00% 96.00%
NO.1
95.29%
94.00%
92.00%
90.00%
88.00%
86.00%
84.00%
82.00%
80.00% Actual Seizure
95.39% Potential Seizure
93.45% Healthy
accurate modelling
Auto Classifier
Computer’s job!! Models are constructed and
tested for each group
Validate Model
Analyze testing results of selected models
• Deliver more recognizable dataset.
MODELLING PROCESS
Reclassify Data Types
split or combine into customized groups based on
research purpose
Balance Sample Size of Each Group
02
Symptom
• Recurrent seizures • Loss of consciousness
• Damage to the brain, or even death. ( >30min )
03
Premature Death
Rate of People died prematurely
Epilesy Patients 8.8% Others 0.7%
Ensure even prediction ability for each group
Select Top 3 Accurate Models
Select based on model accuracy in model recalibration
Transformed Data
Reclassify
(University of Oxford and the Karolinska Institute, Stockholm)
Epilepsy EEG
• One of the most common neurological diseases
• Excessive electrical activity within networks of neurons in the brain
Data Partition
Balance
Model Construction
Model Selection
Model Validation
Partition Data Randomly for Different Use in Modelling
Assign proportion of data used in model building, recalibrate and test for
BEST!
Gain Chart How good each model predicting results approach the ideal condition?
Strengths of the Model
• Quick and Accurate. • Models are dynamic, and constantly learn from new data. • Cost effective. Targeted follow-up treatment • Saves times which benefits patients.
1.37
1.34
Deaths in millions Stroke lung cancer Lower respiratory infections
Chronic obstructive pulmonary disease Diabetes Diarrhoeal diseases
Seizure disorders
94.60% Overall
Model A - Neural Network
Model B - Logistic Regression Model C - C5
RESULT ANALYSIS & MODEL VALIDATION
3 Aspects of Validation in T/F Diagnose
• Detection & Prediction • Allow for interventional treatment • Improve current epilepsy diagnosis • Medical signal-pattern recognition
framework
Brain Activity
Properties • 178 Hz sampling rate • 1 second long
Types of EEG Waveforms
Healthy (Type A)
Healthy (Type B)
Potential Seizure (Type C)
Potential Seizure (Type D)
Potential
10
8.76
6.24
5
0
Ischaemic heart disease Trachea and bronchus cancers Dementias Tuberculosis
Top 10 causes of deaths globally
3.19
3.17
1.69
1.59
1.54
1.39
Actual Seizure (Type E)
Time Series
Frequency Series
Data Preparation
Discrete Fourier Transformation
Time Domain Dataset
������=π
4
������=π ������=π
2
f(t)= ������������������(������������ + ������)
Limitations of the Model
• Potential costs of misclassifying a patient. • Model has to be used as a supportive tool. • Model is only as good as data used to build it. • Abnormal EEG may be caused by other neurological disorders.
RESULT ANALYSIS & MODEL VALIDATION
Actual Seizure
Potential Seizure
Healthy
Actual
433
4
20
Seizure
Potential Seizure
10
873
29
Healthy
20
33
863
Coincidence Matrix of Logistic Regression
POTENTIALS
Epilepsy
01
Demography
• 65 million people worldwide suffer from epilepsy (the Epilepsy Foundation)