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This document introduces a training DVD that teaches a low-arousal approach for de-escalating challenging behavior in patients with acquired brain injuries. It aims to address high staff turnover, which disrupts continuity of care. Currently, minimal staff training exists for managing challenging behavior. The DVD aims to effectively train large numbers of staff and improve risk management. It evaluates the unit's incident reports over the past year to understand the types of challenging behaviors occurring and justify the need for alternative training approaches that focus on positive relationships rather than power struggles. The DVD's goals are to provide a stand-alone training package for new and current staff on an interdisciplinary approach to working with patients who exhibit challenging behaviors following brain injuries.
The Integrated Landscape Assessment Project (ILAP) is a collaborative effort between the U.S. Forest Service, universities, and other organizations to conduct integrated landscape assessments to inform natural resource decisions. The goal of ILAP outreach is to expand partnerships and promote awareness and use of ILAP information by land managers. Outreach activities include webinars, fact sheets, advisory groups, and a project website. Next steps are to facilitate use of ILAP data and tools in forest planning, assessments, and projects over the next 4 years and expand collaboration beyond the initial 4 states.
Test for HIV-associated cognitive impairment in IndiaKimberly Schafer
This document describes the development of a brief screening battery to detect HIV-associated neurocognitive impairment (HAND) in India. Researchers administered a comprehensive neuropsychological (NP) battery to 206 HIV-positive Indian patients. Statistical analysis identified that combinations of two tests - the Brief Visuospatial Memory Test-Revised for learning and either the Color Trails 1 test for processing speed, Grooved Pegboard test for motor skills, or Digit Symbol test for processing speed - achieved high sensitivity and specificity for detecting HAND. The study aims to develop a quick iPad-based screening tool to assess cognitive functioning in resource-limited settings like India.
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This document describes a study on the relationship between term dispersion in source code identifiers and fault proneness. It introduces measures of physical dispersion (entropy) and conceptual dispersion (context coverage) to quantify how terms are scattered across identifiers. An aggregated metric (numHEHCC) counts the number of terms with high entropy and coverage. The study aims to determine if numHEHCC captures different characteristics than size alone, and whether higher dispersion is related to higher fault risk. It presents dispersion measures, outlines analyzing their relevance compared to size, and relating them to faults using two Java projects as case studies.
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Health practitioners are expected to master certain skills to manage settings, which necessitate critical decision taking in several life-threatening situations. In emergency medicine, physicians must be well trained on these tasks and many more before being able to practice them on real live patients.
This document outlines strategies for promoting understanding in blended courses. It suggests using online and in-class methods in combination, with asynchronous online activities to build foundational knowledge and synchronous in-class work to strengthen understanding through interaction and application of concepts. Key approaches include using course websites and social media for online learning, videoconferencing to connect distant students, and interactive class sessions focused on problem-solving in small groups. The goal is to move students from beginner to intermediate levels of expertise through authentic tasks that develop both individual comprehension and social learning skills.
Ready, Set, Click! Using response systems in the classroom. Presented at Conference on Information Technology (CIT), October 12, 2009, Detroit, MI. Sandy McCarthy, Faculty Librarian, Washtenaw Community College.
Ready, Set, Click! Using response systems in the classroom. Presented at Conference on Information Technology (CIT), October 12, 2009, Detroit, MI. Sandy McCarthy, Faculty Librarian, Washtenaw Community College.
The document provides an overview of different learning perspectives and instructional methods from the 1970s to 1990s. It summarizes key instructional methods such as cooperative learning, discovery, problem solving, games, drill and practice, tutorials, demonstrations, and presentations. Each method is defined in 1-2 sentences with examples given for some. The document uses diagrams and timelines to contextualize how perspectives on learning and common teaching approaches have changed over the decades.
This document discusses training on outcome-based education for the year 2011. It introduces outcome-based education and emphasizes that students should take responsibility for their own learning. It discusses teaching and learning strategies like modifying strategies based on student characteristics and being flexible. It contrasts teacher-centered and student-centered learning and describes different modes of delivery like lectures, workshops, and technology-based delivery. It concludes by discussing analyzing strengths and weaknesses of teaching and learning delivery and linking the choice of delivery modes to assessment.
This document discusses training on outcome-based education for the year 2011. It introduces outcome-based education and emphasizes that students should take responsibility for their own learning. It discusses teaching and learning strategies like modifying strategies based on student characteristics and being flexible. It contrasts teacher-centered and student-centered learning and describes different modes of delivery like lectures, workshops, and technology-based delivery. It concludes by discussing analyzing strengths and weaknesses of teaching and learning delivery and linking the choice of delivery modes to assessment.
The instructor's role is to assign leadership roles, clarify main points of discussions, and act as a mentor while also challenging students' thinking through questions. The instructor should be supportive, evaluative, and a learner. Content can be delivered through self-paced or collaborative activities using technologies like email, LMSs, and video conferencing. Instructors can help create a sense of presence by announcing activities, tutoring students, and determining presence through LMS access logs, communication logs, and student feedback surveys.
The document discusses various aspects of a learning program including:
1. It outlines different instructional domains and modes that include cognitive, psychomotor, and affective domains as well as expository, inquisitory, and other instructional modes.
2. It describes different components that make up the learning program including modules, lessons, topics, content levels, and a blended media cupboard utilizing various technologies, texts/graphics, and incorporating workplace culture elements.
3. Charts are presented covering different aspects of conceptual diagrams, instructional design, and learning modules paired with various blended media.
Here are the key points about informed consent:
- It is a process, not just a form. Researchers must ensure participants understand what participation involves through clear verbal and written explanations.
- Consent forms should be written in plain, easy-to-understand language appropriate for the population.
- Participants must be able to refuse or withdraw from the study without penalty.
- Risks and limitations of confidentiality should be clearly explained.
- Participants should have the opportunity to ask questions to fully comprehend what they are consenting to.
- Informed consent is an ongoing process, not a single event, with the option for participants to withdraw later.
The goal is to respect participants' autonomy by
Personalization in design should focus on the individual user through human-centered approaches like accommodation, adaptation, and user control. Systems should provide identity, freedom, and flexible exploration of content while also drawing from educational principles and social aspects like communities, role models, and collaboration with peers. The goal is to support diverse learners through adaptive learning paths and multiple perspectives.
1. This document discusses using learning analytics to gain insights from educational data.
2. Two case studies are described that analyzed institutional data to better understand the impacts of a new virtual learning environment and predictors of student satisfaction in science and engineering courses.
3. Both cases followed a process of appreciating the issue, identifying relevant data sources, summarizing individual data, joining data sources, preparing data for analysis, analyzing and visualizing results, and refining understanding.
The document outlines various process skills related to information processing, problem solving, teamwork, personal skills, and critical thinking. Some key skills listed include observing, listening, estimating, problem solving, managing challenges, collaboration, communication, critical thinking, and personal development skills like self-esteem and self-evaluation. The document provides a comprehensive overview of important soft skills for processing information, working with others, and developing critical thinking abilities.
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Classroom research is research conducted by teachers to improve their own or colleagues' teaching, test educational theories in practice, or evaluate and implement school priorities. It allows teachers to become more effective and critically evaluate research through gaining new insights and understanding classroom issues firsthand. Effective classroom research uses accepted research methods and is collaborative in nature, with teachers participating in all stages of identifying problems, taking action, observing results, and planning next steps.
Standards Based Assessment for the CTE Classroomccpc
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Los Angeles County ROP
Downey, CA
Sarah Vielma
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Herb Smith
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SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODELgrssieee
1) The document describes a segmentation algorithm for polarimetric SAR (PolSAR) data that can model both scalar-texture and multi-texture scattering.
2) The algorithm uses log-cumulants and hypothesis testing to determine whether a scalar-texture or dual-texture model best fits the data within each segment.
3) The algorithm is tested on simulated multi-texture PolSAR data and is shown to accurately segment the classes and estimate their texture parameters. However, when applied to real data sets, the algorithm only finds the simpler scalar-texture case.
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1. Introduction
Development
Experiments
Conclusions
Large Scale Semisupervised Image Segmentation
With Active Queries
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı
Image Processing Laboratory
University of Valencia, Spain
IGARSS 2011, Vancouver, Canada
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
2. Introduction
Development Introduction
Experiments Motivation
Conclusions
Introduction
Outline:
Image segmentation using a hierarchical description of the image
Hierarchical description based on clustering
Use active learning procedures to
Converge faster to an optimal solution
... and improve segmentation results
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
3. Introduction
Development Introduction
Experiments Motivation
Conclusions
Cluster based segmentation
Problems
1 Find right number of clusters
2 Find correct cluster labels
Undersegmentation Good level of segmentation Oversegmentation
Wrong labeling of big clusters Correct labeling Wrong labeling of small clusters
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
4. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Active Learning Segmentation
Proposed methodology components:
1 A hierarchical description of the data
Bottom up: linkage (slow, unfeasible for large images)
Top down: k-means (fast, proposed implementation)
2 Adaptation rule
Prune the description above to adapt it to a description according to
the objects and classes defined by the user
3 Active selection
The algorithm selects the samples to label that will improve results
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
5. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adapting the hierarchical description
Nodes level
Hierarchical Description Segmentation
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
6. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation: overall procedure
1 Obtain a hierarchical
description
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
7. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation: overall procedure
1 Obtain a hierarchical
description
2 Descend through the tree
and ask the user for sample
labels
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
8. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation: overall procedure
1 Obtain a hierarchical
description
2 Descend through the tree
and ask the user for sample
labels
3 Ascend and update node
labels
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
9. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: labeling
Get labels and estimate
pv ,l ∼ # labels l on node v
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
10. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: labeling
Get labels and estimate
pv ,l ∼ # labels l on node v
LB UB
Conf. interval [pv ,ω , pv ,ω ]
LB
pv ,ω = max(pv ,ω − ∆v ,ω , 0)
UB
pv ,ω = min(pv ,ω + ∆v ,ω , 1)
∆v ,ω ∝ node size and
number of labeled samples
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
11. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: labeling
Get labels and estimate
pv ,l ∼ # labels l on node v
LB UB
Conf. interval [pv ,ω , pv ,ω ]
LB
pv ,ω = max(pv ,ω − ∆v ,ω , 0)
UB
pv ,ω = min(pv ,ω + ∆v ,ω , 1)
∆v ,ω ∝ node size and
number of labeled samples
LB UB
pv ,l > 2pv ,l − 1 ∀l = l
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
12. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: labeling
Get labels and estimate
pv ,l ∼ # labels l on node v
LB UB
Conf. interval [pv ,ω , pv ,ω ]
LB
pv ,ω = max(pv ,ω − ∆v ,ω , 0)
UB
pv ,ω = min(pv ,ω + ∆v ,ω , 1)
∆v ,ω ∝ node size and
number of labeled samples
LB UB
pv ,l > 2pv ,l − 1 ∀l = l
Compute all admissible
labels and take the winner
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
13. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: error estimation
Update tree:
Error estimation of labeling a
node as ω:
1 − pv ,ω if (v , ω) admissible
˜v ,ω =
1 otherwise
Divide if
˜v ,ω > (˜vl ,ωl + ˜vr ,ωr )
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
14. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: error estimation
Update tree:
Error estimation of labeling a
node as ω:
1 − pv ,ω if (v , ω) admissible
˜v ,ω =
1 otherwise
Divide if
˜v ,ω > (˜vl ,ωl + ˜vr ,ωr )
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
15. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: error estimation
Update tree:
Error estimation of labeling a
node as ω:
1 − pv ,ω if (v , ω) admissible
˜v ,ω =
1 otherwise
Divide if
˜v ,ω > (˜vl ,ωl + ˜vr ,ωr )
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
16. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Adaptation rule: error estimation
Update tree:
Error estimation of labeling a
node as ω:
1 − pv ,ω if (v , ω) admissible
˜v ,ω =
1 otherwise
Good Pruning !
Divide if
˜v ,ω > (˜vl ,ωl + ˜vr ,ωr )
At the end each node has
An estimated error (˜v ,ω )
LB
A confidence (pv ,l )
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
17. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Active learning to select nodes and subnodes
Active Learning is about obtaining better results labeling less, but better.
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
18. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Active learning to select nodes and subnodes
Active Learning is about obtaining better results labeling less, but better.
Node selection strategies
s0 Proportional to node size (∼ random sampling): nv
LB
s1 Proportional to node size and uncertainty: nv · pv
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
19. Introduction
Active Learning Segmentation
Development
Adapting the hierarchical description
Experiments
Active learning node selection
Conclusions
Active learning to select nodes and subnodes
Active Learning is about obtaining better results labeling less, but better.
Node selection strategies
s0 Proportional to node size (∼ random sampling): nv
LB
s1 Proportional to node size and uncertainty: nv · pv
Subnode selection strategies (left of right node’s child)
d0 Proportional to subnode size: nv
LB
d1 Proportional to subnode uncertainty: pv
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
20. Introduction
Data description
Development
Results
Experiments
Visual inspection
Conclusions
Experiments
145 × 145 AVIRIS image
Indian Pines area, Indiana
Spatial resolution: 30 m
16 crop classes
200 spectral bands (0.4 -
2.5 µm)
All the available 10366 pixels
are considered
Spectral + spatial + PCA
Clustering: hierarchical
k-means (top-down)
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
21. Introduction
Data description
Development
Results
Experiments
Visual inspection
Conclusions
AVIRIS results
Mean results over 10 realizations
100
Random
90 Active
80
70
60
Error (%)
50
40
30
20
10
0
0 200 400 600 800 1000 1200 1400
Num. sample
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
22. Introduction
Data description
Development
Results
Experiments
Visual inspection
Conclusions
Visual inspection (50 labeled samples)
Ground truth Classification Confidence 10 Clusters
Random
Active
10 clusters
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
23. Introduction
Data description
Development
Results
Experiments
Visual inspection
Conclusions
Visual inspection (100 labeled samples)
Ground truth Classification Confidence 15 Clusters
Random
Active
21 clusters
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
24. Introduction
Data description
Development
Results
Experiments
Visual inspection
Conclusions
Visual inspection (200 labeled samples)
Ground truth Classification Confidence 34 Clusters
Random
Active
46 clusters
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
25. Introduction
Data description
Development
Results
Experiments
Visual inspection
Conclusions
Visual inspection (400 labeled samples)
Ground truth Classification Confidence 55 Clusters
Random
Active
82 clusters
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
26. Introduction
Development
Conclusions
Experiments
Conclusions
Conclusions
Structure-based AL exploits cluster structure of data
It discovers the structure representing the user’s desired classes
It does not need a starting training set or fixing the number of
classes
It is fast (no model is required)
Classification and confidence maps are obtained
With a bad clustering, slower convergence
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries
27. Introduction
Development
Conclusions
Experiments
Conclusions
Large Scale Semisupervised Image Segmentation
With Active Queries
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı
Image Processing Laboratory
University of Valencia, Spain
IGARSS 2011, Vancouver, Canada
Devis Tuia, Jordi Mu˜oz-Mar´ and Gustavo Camps-Valls
n ı Large Scale Semisupervised Image Segmentation With Active Queries