-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.qmd
397 lines (195 loc) · 7.56 KB
/
index.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
---
title: "Automatic Classification of Social Behaviors among Mice Using Deep Learning"
author: "Elías Aburto Camacllanqui"
date: last-modified
format:
revealjs:
theme: white
chalkboard: true
logo: Images/eliaslogo.png
transition: slide
lang: Es-es
auto-stretch: true
---
# Introduction {background-image="Images/Resident_Intruder_Paradigm.png"}
---
## Resident-Intruder Paradigm
:::: {layout="[ 60, 40 ]"}
::: {#first-column}
<div style="font-size: 30px;"> <!-- Adjust the font size as needed -->
- Social behaviors which provoke territorial agression (Thurmond, 1975).
- Experiment:
- A resident mouse is put in a cage for 24 hours.
- Then, an intruder mouse enters the cage.
- A camera records the secuence of behaviors that provoce agression.
- At the end, researchers manually label the observed behaviors in the video to identify patterns of behavior.
</div>
:::
::: {#second-column}
![](Images/Resident_Intruder_Paradigm.png)
:::
::::
---
## Problems
:::: {layout="[ 60, 40 ]"}
::: {#first-column}
<div style="font-size: 35px;"> <!-- Adjust the font size as needed -->
- **Replicability Crisis**: The observations can vary between researchers, which affect the replicability of studies.
- **Fatigue and Error**: The process demands time and effort, which can provoke errors.
</div>
:::
::: {#second-column}
![](Images/Replicability.png)
:::
::::
---
# Justification {background-image="Images/Resident_Intruder_Paradigm.png"}
---
## Theoretical Justification
:::: {layout="[ 50, 50 ]"}
::: {#first-column}
::: {#text-block style="background-color: #f8d7da; padding: 20px; border-radius: 10px; border: 1px solid #f5c6cb; margin-top: 100px;"}
<div style="font-size: 35px; color: #a71c24; font-weight: bold; background: linear-gradient(to right, black, red); -webkit-background-clip: text; -webkit-text-fill-color: transparent;"> <!-- Adjust the font size and color as needed -->
**Problem**: The observations can vary between researchers, which affect the replicability of studies.
</div>
:::
:::
::: {#second-column }
::: {#text-block style="background-color: #8B0000; padding: 20px; border-radius: 10px; border: 1px solid #f5c6cb; margin-top: 70px;"}
<div style="font-size: 35px; color: white;"> <!-- Adjust the font size as needed -->
The classification model drives more **objective**, **precise**, and **scalable** measurements compare to manual labeling. Therefore, it increase the quality of social research.
</div>
:::
:::
::::
---
## Practical Justification
:::: {layout="[ 50, 50 ]"}
::: {#first-column}
::: {#text-block style="background-color: #f8d7da; padding: 20px; border-radius: 10px; border: 1px solid #f5c6cb; margin-top: 100px;"}
<div style="font-size: 35px; color: #a71c24; font-weight: bold; background: linear-gradient(to right, black, red); -webkit-background-clip: text; -webkit-text-fill-color: transparent;"> <!-- Adjust the font size and color as needed -->
**Problem**: The process demands time and effort, which can provoke errors.
</div>
:::
:::
::: {#second-column }
::: {#text-block style="background-color: #8B0000; padding: 20px; border-radius: 10px; border: 1px solid #f5c6cb; margin-top: 70px;"}
<div style="font-size: 35px; color: white;"> <!-- Adjust the font size as needed -->
The automatic classification model streamlines the research process in research centers making researchers more productive.
</div>
:::
:::
::::
---
# Objective {background-image="Images/Resident_Intruder_Paradigm.png"}
---
## Main Objective
<div style="font-size: 60px; margin-top: 80px;"> <!-- Adjust the font size as needed -->
Develop an automatic classification model for the monitoring and analysis of animal behavior in research environments.
</div>
---
# Methodology {background-image="Images/Resident_Intruder_Paradigm.png"}
---
## Data
::: {.panel-tabset}
### Tool
- **MARS**: A tool for capturing the positions of mice using deep learning (Segalin et al, 2021).
::: {layout="[40,60]"}
![](Images/MARS.png)
![](Images/mars_demo.gif)
:::
### Challenge
::: {layout="[50,50]"}
- **Caltech Data** published the <a href="https://data.caltech.edu/records/s0vdx-0k302" target="_blank">dataset</a> which contains positions and labels.
![](Images/Caltech.png){width=800px height=300px}
:::
### Task
![](Images/Task1.png)
:::
---
## Exploratory Data Analysis
::: {.panel-tabset}
### Sequence
- Each sequence corresponds to a particular experiment involving a resident mouse (0) and an intruder mouse (1).
![](Images/sequence.png)
### Visualitation
::: {layout="[40,60]"}
- Each sequence has a different number of frames.
![](Images/pose_sequence.gif)
:::
### F / sequence
![](Images/Frame1.png)
### F / Dataset
Percentaje of frames per class in the whole dataset.
![](Images/Frame2.png)
:::
---
## Preprocessing
::: {.panel-tabset}
### Loading data
![](Images/LoadData.png)
### Padding data
![](Images/PaddingData.png)
:::
---
## Training
::: {.panel-tabset}
### Loss function
<div style="font-size: 25px;"> <!-- Adjust the font size as needed -->
Sparse Categorical Cross Entropy is appropriate for multi-class classification problems.
</div>
![](Images/LossFunction.png)
### Model Structure
![](Images/ModelStructure.png)
### Training
![](Images/Training.png)
:::
---
## Evaluation
::: {.panel-tabset}
### Model Accuracy
![](Images/Model_Accuracy.png)
### Model Loss
![](Images/Model_Loss.png)
### Test Accuracy
![](Images/Test_Accuracy.png)
:::
---
# Expected Impact {background-image="Images/Resident_Intruder_Paradigm.png"}
---
## Expected Impact
:::: {layout="[ 50, 50 ]"}
::: {#first-column}
::: {#text-block style="background-color: #8B0000; padding: 20px; border-radius: 10px; border: 1px solid #f5c6cb; margin-top: 70px;"}
<div style="font-size: 25px; color: white;"> <!-- Adjust the font size as needed -->
**Advances in Scientific Research**:
- Significantly reduces time and errors in the process.
- Improves the precision and consistency of the results.
- Optimizes the replicability of studies by minimizing variance between observers.
</div>
:::
:::
::: {#second-column }
::: {#text-block style="background-color: #8B0000; padding: 20px; border-radius: 10px; border: 1px solid #f5c6cb; margin-top: 70px;"}
<div style="font-size: 25px; color: white;"> <!-- Adjust the font size as needed -->
**Practical Application**:
- Can be expanded to identify social human behaviors, such as harassment and bullying.
- Could help address security issues in schools and businesses by identifying dangerous behaviors automatically.
- Could aid in the care of animals during their development.
</div>
:::
:::
::::
---
# References{background-image="Images/Resident_Intruder_Paradigm.png"}
---
<div style="font-size: 30px; text-indent: -1.27cm; padding-left: 1.27cm;">
Segalin, C., Williams, J., Karigo, T., Hui, M., Zelikowsky, M., Sun, J. J., Perona, P., Anderson, D. J., & Kennedy, A. (2021). The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice. *eLife, 10*, e63720. https://doi.org/10.7554/eLife.63720
<br>
Sun, J. J., Karigo, T., Chakraborty, D., Mohanty, S. P., Wild, B., Sun, Q., Chen, C., Anderson, D. J., Perona, P., Yue, Y., & Kennedy, A. (2021). The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions. *Adv Neural Inf Process Syst*(Db1), 1-15.
<br>
Thurmond, J. B. (1975). Technique for producing and measuring territorial aggression using laboratory mice. *Physiology & Behavior, 14*(6), 879-881. https://doi.org/https://doi.org/10.1016/0031-9384(75)90086-4
</div>
---
# Thanks
---