Task Index

  • David’s Notes \
  • non-classes \
  • Bioinformatics \
    • Genome Wide Association Studies (GWAS)
    ☐ Flesh out GWAS
  • David’s Notes \
  • old-classes \
  • Principles-Of-ClinicalTrials \
  • studyVisits \
    • Visit: 0
    ☐ Informed Consent / Assent
    ☐ Inclusion / Exclusion Criteria
    ☐ Dispense (return) diaries
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Spirometry: Pre-Study Drug
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Spirometry: Post-Study Drug
    ☐ Chest X-Ray
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Limited Chest HRCT
    ☐ Exercise oximetry (clinic)
    ☐ Serum Biomarkers
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ Serum Biomarkers
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Chest X-Ray
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ Serum Biomarkers
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Limited Chest HRCT
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Chest X-Ray
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ Serum Biomarkers
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Email / Telephone Monitoring
    ☐ Drug Administration Log
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Chest X-Ray
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ Serum Biomarkers
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
    ☐ Diary Completion (Assessment)
    ☐ Medical History
    ☐ Concomitant Therapies
    ☐ Adverse Events
    ☐ Vital Signs
    ☐ Physical Examination
    ☐ Clinical Laboratory Tests
    ☐ Spirometry: Pre-Study Drug
    ☐ Chest X-Ray
    ☐ Lung Volumes: Pre-Study Drug
    ☐ Exercise oximetry (clinic)
    ☐ Serum Biomarkers
    ☐ WHOQOL
    ☐ Dyspnea & Fatigue Score
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz13
    ☐ time complexity to train + [ ] space complexity to train
    ☐ space complexity to predict
    ☐ estimating \(p(c_i)\) is hard
    ☐ The assumption of independence is hard
    ☐ dealing with numerical data is hard
    ☐ \(p(x_1,\dots,x_d) = p(x_1) . \ldots . p(x_d)\)
    ☐ \(p(c_i | x_1,\dots,x_d) = p(c_i| x_1) . \ldots . p(c_i | x_d)\)
    ☐ \(p(x_1,\dots,x_d) = p(x_1|c_i) . \ldots . p(x_d | c_i)\)
    ☐ Ensuring that \(p(x_j = t | c_i) = 0\) when there are no points with \(x_j = t\) for all data points of \(c_i\)
    ☐ Ensuring that \(p(x_j= t) = 0\) even when there are no points with \(x_j = t\) for all data points of \(c_i\)
    ☐ Ensuring that \(p(c_i) \neq 0\) no data point belongs to class \(c_i\)
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz15
    ☐ n dimensions
    ☐ n + 1 dimensions
    ☐ d+1 dimensions
    ☐ the primal formulation
    ☐ both primal and dual formulations
    ☐ neither primal and dual formulation
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz16
    ☐ Knowing \(\phi(x_i) \forall i \in \{1\dots n\}\) is necessary to predict
    ☐ \(\mathbf{K}(x_i, x_j)\) must be a positive-definite matrix
    ☐ Knowing \(\mathbf{K}(x_i,x_j), \forall i,j \in \{1, \dots, n\}\) is sufficient to predict,
    ☐ Kernel matrices are of size \(n \times n\)
    ☐ Kernels should be symmetric
    ☐ Kernels should be positive semidefinite matrices
    ☐ When rules overlap, we need to determine the ranking
    ☐ Association rules mining is suited for discrete attributes
    ☐ Rules are ranked on the basis of confidence
    ☐ Class labels are always on the RHS of the rules (a => labels)
    ☐ Itemsets without labels are irrelevant
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz2
    ☐ 1
    ☐ 2
    ☐ 0
    ☐ linked-list
    ☐ matrix
    ☐ row vector
    ☐ Parameter of the population estimated by the entire population
    ☐ geoemetric view in 1D
    ☐ geometrix view in 2D
    ☐ In geometric 2D, each data-point is a vector
    ☐ In probablilitic view, parameters are estimated
    ☐ For continuous attributes, mean of an attribute is expressed as an integration \(\int_{-\infty}^{\infty}xp\left(x\right)dx\)
    ☐ Correlation measures linear relationships
    ☐ Cos(θ) is a measure of similarity
    ☐ Euclidean distance is a good measure for geometric distances
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz3
    ☐ \( x^T\Sigma x\in\mathbb{Z}\)
    ☐ \(x^T\Sigma x=0\)
    ☐ xTΣ x ≤ 0
    ☐ \(\mu + \sigma\)
    ☐ \(\mu - \sigma\)
    ☐ \(\mu + 2\sigma\)
    ☐ Correlation
    ☐ Cosine
    ☐ Covariance
    ☐ The samples are uniformly distributed
    ☐ The means of the samples are uniformly distributed
    ☐ The samples are normally distributed
    ☐ The confusion matrix is of size 2 x 3
    ☐ Since A & B are categorical, correlation is NOT the correct metric to measure similarity
    ☐ The null hypothesis of χ2 test is that variables are independent
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz4
    ☐ The origin in d-dimensions
    ☐ The surface of a d-dimensional hypershere
    ☐ The shell of a d-dimensional hypersphere
    ☐ \(\infty\)
    ☐ 1
    ☐ Correct!
    ☐ 0
    ☐ The volume of a unit hypercube is 1
    ☐ The volume of a hypercube with sides of length 2 goes to ∞
    ☐ The "corners" of a hypercube occupies more space than the inscribed hypercube
    ☐ \(\mathcal{O}(d)\)
    ☐ \(\mathcal{O}(d^2)\)
    ☐ \(\mathcal{O}(d^3)\)
    ☐ a line with length = 1
    ☐ a circle with radius = 1
    ☐ a circle with diameter = 1
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz5
    ☐ \(0.4x_1 + 0.3x_2 + 0.6x_3\)
    ☐ \(4^2 x_1 + 3^2 x_2 + 6^2 x_3\)
    ☐ \(4x_1 + 3x_2 + 6x_3\)
    ☐ PCA projects the attributes into a space where covariance matrix is diagonal
    ☐ The first Principal Component points in the direction of maximum variance
    ☐ PCA is useful for exploratory data analysis
    ☐ The first PC points to maximum variance
    ☐ PCA computes eigen-value eigen-vector decomposition of the covariance matrix
    ☐ PCA works well for ellipsoidal data
    ☐ \(x \times u\)
    ☐ \((x - \mu_x) \cdot (u - \mu_u)\)
    ☐ \(||x||||u||\)
    ☐ selecting principal components with maximum variance
    ☐ combining many features into one
    ☐ selecting principal components that are not orthogonal to each other
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz6
    ☐ \(\Sigma = \lambda_1 u_1^T u_1 + \lambda_2 u_2^T u_2 + \dots \lambda_d u_d^T u_d\)
    ☐ \(\Sigma = \lambda_1 u_1^T + \lambda_2 u_2^T + \dots \lambda_d u_d^T\)
    ☐ \(\Sigma = \lambda_1 u_1 + \lambda_2 u_2 + \dots \lambda_d u_d\)
    ☐ Eigen value/eigen vector corresponding to second-largest variance
    ☐ Eigen value/eigen vector corresponding to largest variance
    ☐ Eigen value/eigen vector corresponding to the smallest variance
    ☐ \(X = U\Delta V^T\)
    ☐ \(\Sigma = U\Delta V^T\)
    ☐ \(X = V\Delta V^T\)
    ☐ \(\Sigma = U\Delta V^T\)
    ☐ \(X = V\Delta V^T\)
    ☐ \(\Sigma = V\Delta V^T\)
    ☐ Eigenvectors of covariance of data-points
    ☐ Matrix of eigenvalues on diagonal
    ☐ Deflated matrix after removing first Principal Component
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • Quiz7
    ☐ \(\max \frac{|m_1 - m_2|}{s_1^2 + s_2^2}\)
    ☐ \(\min \frac{s_1^2 + s_2^2}{(m_1 - m_2) . (m_1 - m_2)}\)
    ☐ \(\max \frac{|m_2 - m_1|}{s_1^2 + s_2^2}\)
    ☐ PCA and LDA project data to 1 < d' <= d dimensions
    ☐ PCA projects data to 1 dimension, LDA projects data to 1 < d' <=d dimensions
    ☐ PCA and LDA project data to 1 dimension
    ☐ LDA inputs data only. PCA inputs data and labels
    ☐ Both PCA & LDA input dataset only
    ☐ Both PCA & LDA input dataset and labels
    ☐ Both methods project data to higher dimension
    ☐ Both maximize variance in ℝd
    ☐ Both minimize variance in ℝd
    ☐ \(Ax = \lambda x\)
    ☐ \(Ax = A^{-1}x\)
    ☐ \(Ax = x\)
  • David’s Notes \
  • old-classes \
  • Intelligent_Data_Design \
  • quiz-review \
    • all_quizzes
    ☐ 1
    ☐ 2
    ☐ 0
    ☐ linked-list
    ☐ matrix
    ☐ row vector
    ☐ Parameter of the population estimated by the entire population
    ☐ geoemetric view in 1D
    ☐ geometrix view in 2D
    ☐ In geometric 2D, each data-point is a vector
    ☐ In probablilitic view, parameters are estimated
    ☐ For continuous attributes, mean of an attribute is expressed as an integration \(\int_{-\infty}^{\infty}xp\left(x\right)dx\)
    ☐ Correlation measures linear relationships
    ☐ Cos(θ) is a measure of similarity
    ☐ Euclidean distance is a good measure for geometric distances
    ☐ \( x^T\Sigma x\in\mathbb{Z}\)
    ☐ \(x^T\Sigma x=0\)
    ☐ \(x^T\Sigma x\leq 0\)
    ☐ \(\mu + \sigma\)
    ☐ \(\mu - \sigma\)
    ☐ \(\mu + 2\sigma\)
    ☐ Correlation
    ☐ Cosine
    ☐ Covariance
    ☐ The samples are uniformly distributed
    ☐ The means of the samples are uniformly distributed
    ☐ The samples are normally distributed
    ☐ The confusion matrix is of size 2 x 3
    ☐ Since A & B are categorical, correlation is NOT the correct metric to measure similarity
    ☐ The null hypothesis of \(\chi^2\) test is that variables are independent
    ☐ The origin in d-dimensions
    ☐ The surface of a d-dimensional hypershere
    ☐ The shell of a d-dimensional hypersphere
    ☐ \(\infty\)
    ☐ 1
    ☐ e
    ☐ The volume of a unit hypercube is 1
    ☐ The volume of a hypercube with sides of length 2 goes to ∞
    ☐ The "corners" of a hypercube occupies more space than the inscribed hypercube
    ☐ \(\mathcal{O}(d)\)
    ☐ \(\mathcal{O}(d^2)\)
    ☐ \(\mathcal{O}(d^3)\)
    ☐ a line with length = 1
    ☐ a circle with radius = 1
    ☐ a circle with diameter = 1
    ☐ \(0.4x_1 + 0.3x_2 + 0.6x_3\)
    ☐ \(4^2 x_1 + 3^2 x_2 + 6^2 x_3\)
    ☐ \(4x_1 + 3x_2 + 6x_3\)
    ☐ PCA projects the attributes into a space where covariance matrix is diagonal
    ☐ The first Principal Component points in the direction of maximum variance
    ☐ PCA is useful for exploratory data analysis
    ☐ The first PC points to maximum variance
    ☐ PCA computes eigen-value eigen-vector decomposition of the covariance matrix
    ☐ PCA works well for ellipsoidal data
    ☐ \(x \times u\)
    ☐ \((x - \mu_x) \cdot (u - \mu_u)\)
    ☐ \(||x||||u||\)
    ☐ selecting principal components with maximum variance
    ☐ combining many features into one
    ☐ selecting principal components that are not orthogonal to each other
    ☐ \(\Sigma = \lambda_1 u_1^T u_1 + \lambda_2 u_2^T u_2 + \dots \lambda_d u_d^T u_d\)
    ☐ \(\Sigma = \lambda_1 u_1^T + \lambda_2 u_2^T + \dots \lambda_d u_d^T\)
    ☐ \(\Sigma = \lambda_1 u_1 + \lambda_2 u_2 + \dots \lambda_d u_d\)
    ☐ Eigen value/eigen vector corresponding to second-largest variance
    ☐ Eigen value/eigen vector corresponding to largest variance
    ☐ Eigen value/eigen vector corresponding to the smallest variance
    ☐ \(X = U\Delta V^T\)
    ☐ \(\Sigma = U\Delta V^T\)
    ☐ \(X = V\Delta V^T\)
    ☐ \(\Sigma = U\Delta V^T\)
    ☐ \(X = V\Delta V^T\)
    ☐ \(\Sigma = V\Delta V^T\)
    ☐ Eigenvectors of covariance of data-points
    ☐ Matrix of eigenvalues on diagonal
    ☐ Deflated matrix after removing first Principal Component
    ☐ \(\max \frac{|m_1 - m_2|}{s_1^2 + s_2^2}\)
    ☐ \(\min \frac{s_1^2 + s_2^2}{(m_1 - m_2) . (m_1 - m_2)}\)
    ☐ \(\max \frac{|m_2 - m_1|}{s_1^2 + s_2^2}\)
    ☐ PCA and LDA project data to 1 < d' <= d dimensions
    ☐ PCA projects data to 1 dimension, LDA projects data to 1 < d' <=d dimensions
    ☐ PCA and LDA project data to 1 dimension
    ☐ LDA inputs data only. PCA inputs data and labels
    ☐ Both PCA & LDA input dataset only
    ☐ Both PCA & LDA input dataset and labels
    ☐ Both methods project data to higher dimension
    ☐ Both maximize variance in ℝd
    ☐ Both minimize variance in ℝd
    ☐ \(Ax = \lambda x\)
    ☐ \(Ax = A^{-1}x\)
    ☐ \(Ax = x\)
  • David’s Notes \
  • old-classes \
  • ParallelComputing \
    • mock_exam
    ☐ having fewer, but more complex processors, [x] having more, but less complex processors, [ ] maximizing the speed of the processor clock [ ] increasing the complexity of the control hardware
    ☐ specify that block x will run at the same time as block y [ ] specify that block x will run after block y. [ ] specify that block x will run on same SM as y [x] none of the above
    ☐ All threads from a block can access the same variable in that block's shared memory. [ ] Threads from two different blocks can access the same variable in global memory [ ] Threads from different blocks have their own copy of local variables in local memory. [ ] Threads from the same block have their own copy of local variables in local memory.
    ☐ sort an array [x] add one to each element of an array [ ] summing all elements in array [ ] apply a predicate to each element in an array [x] move data in parallel based on array of scatter addresses
    ☐ it takes at least n operations [ ] its work complexity is order of n [ ] its work complexity is order n*n [ ] its step-complexity is order of 1, independent of the size of the input.
    ☐ square root of n [ ] log base 2 of n [ ] n [ ] n times log base 2 of n
    ☐ map operations have arguments that are functions with a single argument [ ] map operations can be applied to arrays of any number of dimensions [ ] map operations are generally very efficient on GPUs [ ] a compact operation requires a map operation to be performed.
  • David’s Notes \
  • weirauchlab \
  • PU1 \
    • Agenda For PU1
    ☐ Matt wants supplemental figures for antibody and experiment
    ☐ Use fold enrichment instead of odds ratio
    ☐ Generate Tables
    ☐ Integrate great script with makefile?