| SmartGram App: Models\ App |
| Programming: | Python - Mathematica |
| Analytics: | iPython - Tableau - R - Google Analytics |
| Data Storage + Control: | Github - SQL |
| Web Development: | HTML/CSS/Javascript |
| Logical thinking: | Communication: | Business: |
|---|---|---|
| I love puzzles and problem solving. I believe logic and critical thinking is more important than memorization. | In college, I was invited to work as radio host for a musical show. But instead, I asked to have a section in a science oriented show. A couple of times people recognized my voice in the street. | I took MBA courses during my Ph.D.
I learned about Business Operations, Marketing, Supply Chain, and Finance. I also met awesome people. |
• Collaborating in the AI challenge: “Analyzing the role of connectivity on economic and human development”. This challenge is hosted by UNDP and the goal is to build an AI-based solution for identifying the relationship between connectivity and human development indicators (life expectancy, education, and/or per capita income)..
• Executed exploratory descriptive data analysis, cleaning, and wrangling in large open source data sets.
• Created visualizations to provide an accessible way to understand the data.
• Employed classification machine learning models (logistic regression, random forest and neural networks) for financial problems using public data.
• Structured regression models to predict marketing metrics.
• Planed, evaluated, and revised course content, laboratory sessions, and methods of instruction for science students.
• Applied the Socratic method to engage the students to think like scientists.
Assessed mathematical models and developed simulations on Python to study viral infections dynamics. Sample Projects:
• Capitalized the limited viral experimental data using regression techniques.
• Quantified the effect of proteases on viral in vitro experiments using statistical techniques such as bootstrapping and Markov Chain Monte Carlo.
• Instructed and supervised laboratory sessions for Astronomy, Classical Mechanics and Electrodynamics for freshman students with non-science majors.
• Conducted an in-depth analysis of United Airlines customer surveys big data using R and Python. Identified through machine learning that the key
factors to improve customer experience.
• Delivered visualizations of my findings using Tableau and recommendations to a panel of industry experts.
Detected areas of opportunity to take advantage of the emergence of AI and presented recommendations to accelerate market share growth.
• Planned and managed a marketing strategy based on data available on the simulation.
• Counseled my team members in the areas of R&D, supply chain and finance.
Texas Christian University and Neeley School of BusinessPh.D. in Physics with Business Option |
2013 - 2018 GPA: 3.6 |
Texas Christian UniversityM.S. Physics |
2013 - 2016 GPA: 3.9 |
Universidad Autonoma de ZacatecasB.S. in Physics |
2008 - 2013 GPA: 3.1 |
| Structuring Machine Learning Projects by deeplearning.ai | February 2020 |
| Machine Learning by Stanford University on Coursera | January 2020 |
| Level 1 Intelligence Analyst on Udemy | April 2019 |
| SmartGram App: Models\ App | ||
|
Code here |
Code |
Code |
|
Code |
Code |
Apart from coding, I enjoy physical challenges. If I have free time, I like to go to the gym to practice boxing (I do it since highschool). In the weekends, I practice yoga at home.
I enjoy sci-fi movies. And in Netflix, I mostly watch crime and mistery shows.
I can't decide if I'm a cat or dog person, I adore both.
I love to talk and learn about science. I spend a lot of time reading about tech.
I work closely with a mentee, we use statistics and mathematical models to develop biophysics simulations using Python.
Developed a program for the enrichment classes at Alice Carlson Elementary School (Fort Worth, TX).
Taught Magnetism through an interactive course to 3rd grade students.
Created science content for the radio show “A Ciencia cierta." Participated as a host in the live show.
| WWWCode | 2019 - Present |
| Tech Ladies | 2018 - Present |
| Society for Industrial and Applied Mathematics | 2013 - 2017 |
| American Physics Society | 2013 - 2017 |
| A comparison of methods for extracting influenza viral titer characteristics. | In this article, our aim is to compare the estimates of different viral titer characteristics using three different approaches. The first approach is the traditional method that uses estimates based on experimentally measured data. The second approach relies on the use of a linear model to fit the viral titer data. The third approach uses an exponential model for the fitting process and the parameters of interest are extracted from there. |
| Investigating different mechanisms of action in combination therapy for influenza | Here, we use a mathematical model of influenza to model combination treatment with antivirals having different mechanisms of action to measure peak viral load, infection duration, and synergy of different drug combinations. |


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Ebullienza
Round 1: Brain Feast
Round 2: Filmstrips
Round 3: Spellen
Round 4: Word Rush
Round 1: Brain Feast
Round 2: Filmstrips
Round 3: Spellen
Round 4: Word Rush
Welcoming the new team for 2018-2019
Round 1: Brainbuzz
Round 2: Games.com
Round 3: The Game of Life
1.Triathlon
2.Starchitech
Round 1: Brainbashers
Round 2: Minute to win
Round 3: Wealth from waste
Faculty Advisor
Assistant Professor(M.Tech), Department of Computer Science & Engineering
Committee Member
IV Civil-C
Executive Member
III CSE-A
Executive Member
III CSE-A
Executive Member
III CSE-C
Executive Member
III Civil-D
Executive Member
III ECE-A
Executive Member
III CSE-C
Executive Member
III ECE-B
Executive Member
III Civil-D
Executive Member
III Civil-C
Executive Member
III ECE-B
Executive Member
III ECE-C
Executive Member
III CSE-A
Executive Member
III ECE-C


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I completed a Bsc Computer Science hons degree at the University of Hertfordshire in 2012 of which the third of the four year course was spent on an academic placement with IBM Cognos. After the completion of my academic education I re-joined IBM as a Software Developer in their WebSphere Application Server department with a strong focus on Cloud technologies (CloudFoundry, Buildpack’s, BlueMix) and the Spring Eco System.
In 2015 I joined a consultancy company called Succeed Consultancy as a Java Software Engineer, specialising in Monitoring and Alerting. I am currently a Senior Full Stack Software Engineer and Monitoring and Alerting Solutions Architect working on a product known as ElementSuite.
This role has required me to maintain focus on elementsuite's underlying Java platform and monitoring solution as well as in-depth knowledge of each layer of the technical stack from the underlying infrastructure all the way up to the web front end.
Key Responsibilities:
This role required me to design, build, implement and maintain an infrastructure and software monitoring solution that provided key insights across a distributed software architecture, alert on key events such as hardware and integration failures, and provide graphic visulations to the development and management teams.
Key Responsibilities:
This role required me to contribute to an existing Java J2EE Web Application for the purposes of fulfilling backend HR management services via restful interfaces using the Spring Framework.
Key Responsibilities:
This role required me to plan workitems, design technical solutions to gather metrics, motivate the UK team and provide status to both the UK and global teams on progress.
Key Responsibilities:
Here is a video of a very young and nervous me at one of the first talks I attended and presented at as part of this role: Sam O'Donnell - BlueMix Demo
This role required active ruby development and testing to enable the manipulation and configuration of an isolated file system for the purposes of enabling the deployment web applications.
Key Responsibilities:
Java and Python development of a Liberty Workload Deployer Plugin to enable users to make use of the Liberty Profiles functionality in an IBM Workload Deployer environment.
Key Responsibilities:
This role required a great understanding of Java J2EE technologies for the purpose of adding, fixing and testing different aspects of the Liberty Web Application Server.
Key Responsibilities:
Many of the tutorials I wrote as part of this role are still being used today. Sam O'Donnell - WebSphere Liberty Profile Tutorials
2:1
Triple Merit
A*-C
Here is a representation of some of the programming languages and tools I have interacted with throughout my career.


Machine Learning Notes
Table of Contents
- 1. Foundations
- 2. Logistic Regression
- 3. Support Vector Machine
- 4. Decision Tree
- 5. Questions
- 6. Machine Learning Handson
- 7. Feature engineering
- 8. Visualization of High Dimensional Data
- 9. Experiment Design for ML algorithm Evaluation
- 10. XGBoost
- 11. Pipelines
- 12. Cross Validation
- 13. Data leakage
- 14. General Visualization
- 15. Debugging and Error Analysis
- 16. misc
- 17. Probability Distributions
1 Foundations
1.1 Probability Theory
Three school of thoughts
- Classical: Assume everything has equal probability of occuring. e.g. coin flip results in 50% chance of either head or tail
- Frequentist: Derive probability based on known relative frequency. e.g. on average, 1 in 3 students is a computer science student, so a randomly chosen student has 1/3 chance being a cs student
- Bayesian: the probability depends personal perspective. e.g. being a doctor with 20 years of experience, based some test results, I believe this patient has 80% chance of survival
new stuff
1.1.1 Bayesian Probability
An intepretation of the concept of probability. Instead of frequency or propensity of some phenomenon, probability is intepreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.
- Bayes’ therom to convert prior probability to posterior probability based on evidences provided by the observed data
- The effect of observed data \(D = {t_1,..., t_n}\) is expressed through conditional probability \(p(D|w)\)
\[ p ( \mathbf { w } | \mathcal { D } ) = \frac { p ( \mathcal { D } | \mathbf { w } ) p ( \mathbf { w } ) } { p ( \mathcal { D } ) } \] \(p(D|w)\) is learned from observed data, is called the likelihood function. Given the definition of likelihood. We state Bayes’ therom as \[ \text { posterior } \propto \text { likelihood } \times \text { prior } \]
- negative log of the likelihood function is called an error function. Because error function is monotonically decreasing, maximizing the likelihhood is equivalent to minimizing the error
- bootstrap: suppose orginal data set consists of N data points at random from X. We create a new dataset \(X_b\) by drawing N points at random from X, with replacement, so some points in X may be replicated in \(X_B\), where other points in X may be absent from \(X_B\). This process is repeated L times to generate L data sets each of size N and each obtained by sampling from the original data set X
- One advantage of the Bayesian viewpoint is that inclusion of prior knowledge arises naturally. Suppose, that a fair-looking coin is tossed three times and lands head each time. A classical maximum likelihood estimate of the probability of landing heads would give 1. By contrast, a Bayesian approach with any reasonable prior will lead to much less extreme conclusion
1.1.2 The Gaussian distribution
For a single real-valued variable x, the Gaussian distribution is defined by \[ \mathcal { N } \left( x | \mu , \sigma ^ { 2 } \right) = \frac { 1 } { \left( 2 \pi \sigma ^ { 2 } \right) ^ { 1 / 2 } } \exp \left\{ - \frac { 1 } { 2 \sigma ^ { 2 } } ( x - \mu ) ^ { 2 } \right\} \] Two key parameters are \(\sigma^2\), the standard deviation and \(\mu\), the mean. The reciprocal of variance is called precision, written as \(\beta = 1 / \sigma ^ { 2 }\)
1.1.3 Curve fitting
1.2 Model Selection
1.3 The Curse of Dimensionality
1.4 Decision Theory
1.5 Information Theory
2 Logistic Regression
2.1 Three linear models
- linear classification(perceptron): \(h(x)=sign(s)\)
- output is 1 or 0
- linear regression: \(h(x)=s\)
- output is linear transformation
- logistic regression: \(h(x)= \theta(s)\)
- output is a probability given by a sigmoid function
where \(s=\sum_{i=0}^{d}w_{i}x_{i}\)
2.2 Sigmoid function
also known as the sigmoid or logistic function \[ \sigma(w^Tx) = \frac{1}{1+e^{-w^T x}} \]
2.3 Logistic Regression Function
The logistic regression model specifies the probability of a binary output \(y_i \in \{0,1\}\) given the input \(x_i\) as follows: \[ \left.\begin{aligned} p ( \mathbf { y } | \mathbf { X } ,\theta ) & = \prod _ { i = 1} ^ { n } \operatorname{Ber} \left( y _ { i } | \operatorname{sigm} \left( \mathbf { x } _ { i } \theta \right) \right) \\ & = \prod _ { i = 1} ^ { n } \left[ \frac { 1} { 1+ e ^ { - x _ { i } \theta } } \right] ^ { y _ { i } } \left[ 1- \frac { 1} { 1+ e ^ { - x _ { i } \theta } } \right] ^ { 1- y _ { i } } \end{aligned} \right. \] where \(x _ { i } \theta = \theta _ { 0} + \sum _ { j = 1} ^ { d } \theta _ { j } x _ { i j }\)
2.4 Error measure(Cross-entropy Error)
Based on Likelihood: if hypothesis h = f, how likely to get y from x. Given a set of training data points $(x_i, y_i), i = 1,…,n,$ where \(y_i \in \{0, 1\}\). We need to find a weight vector w s.t. the probability of the correct \(y_i \text{ for } x_i\) is high for \(i=1,...n\) \[ max P(y=y_i|x_i; w) \] (maxmize the log likelihood) Equiv to \[ min -\sum_{i=1}^n log P(y=y_i|x_i; w) \] (minimize the negative log likelihood)
2.5 Gradient Descent
Compared to linear regression, logistic regression does not have a closed-form solution, instead of a iterative solution is used, which is called gradient descent
- Start at w(0)
- Takes a step along the steepest slope
- Takes a step toward that direction
- Repeat until no local improvement is possible
2.6 Algorithm
This is the algorithm for batch learning of logistic regression. It is very similar to linear classification’s algorithm (perceptron). Both learns a linear decision boundary.
\begin{algorithmic} \State Given: $(x_i, y_i), i = 1,...n$ \State Initialize $w = (0,...,0)$ \Repeat \State $\Delta = (0,...,0)$ \For{$i = 1, ..., n$} \State $\hat { y } _ { i } = \frac { 1} { 1+ e ^ { - w } T _ { x _ { i } } } $ \State $\nabla = \nabla + \left( \hat { y } _ { i } - y _ { i } \right) x _ { i } $ \EndFor \State $w = w - \eta \Delta $ \Until{$|\Delta|\leq \epsilon$} \end{algorithmic}2.7 Termination Criteria Considerations
The setting of terminating condition can be tricky for gradient descent
- If terminates prematurely, the algorithm may not reach the global minimum
- If there is a local minimum, the algorithm may get stuck in local minimum
- If set an expected global minimum, it may never be reached by the algorithm
2.8 Date snooping
Looking at the data before choosing the model is problematic, can lead to a fallacy
- this is different from using human expertise knowledge for feature engineering, which can help the model
3 Support Vector Machine
One of the most successful classification algorithm in machine learning. Given multiple decision boundaries that split the data, SVM seeks to find a hyperplane that separate the data, such that the margin is maximized to the nearest trained data points. It is a constrained optimization problem.
3.1 Margin
Given a linear decision boundary defined by \(w^Tx+b=0\). The functional margin for a point \((x^i, y^i)\) is defined as \[ y^i(w^Tx^i + b) \] For a fixed w and b, the larger the functional value, the more confident we have about the prediction. However, the functional margin can be arbitrarily changed without changing the boundary at all. So we use geometric margin instead. Geometric Margin The distance between an example and the decision boundary. For training set \(S=\{x^i, y^i\}: i = 1, ... N\) and boundary \(w^T+ b = 0\), compute the geometric margin of all points: \[\gamma^i = \frac{y^i(W \cdot X^i + b}{||W||}, i = 1, ..., N\] Note: \(\gamma^i > 0\) if point i is correctly classified We want to see if the smallest \(\gamma^i\) is large. \[\gamma =\min_{i=1...n} \gamma^i\]
3.2 Optimization Problem
Maximizing the geometric margin is equivalent to minimizing the magnitude of w subject to maintaining a functional margin of at least 1. \[ \min _ { w ,b } \frac { 1} { 2} \| \mathbf { w } \| ^ { 2} \] \[ \text{ subject to } : y ^ { i } \left( \mathbf { w } \cdot \mathbf { x } ^ { i } + b \right) \geq 1,i = 1,\cdots ,N \] Results in a quadratic optimization problem with linear inquality constraint. There are several algorithms for solving for QP. We can regard them as black box. The solution can be written in forms of \[ \mathbf { w } = \sum _ { i = 1} ^ { N } \alpha _ { i } y ^ { i } x ^ { i } ,\quad s .t .\sum _ { i = 1} ^ { N } \alpha _ { i } y ^ { i } = 0 \] The above equation provide the form for w, the value of b can be computed with some additional steps
- w is a linear combination fo all training exampls
- many points have zero \(\alpha\)’s, which are the data points that have larger geometric margin
- points that have non-zero \(\alpha\)’s are called support vector, which are the data points that have smallest geometric margin
3.3 Dealing with none linearly separable data
If data are not linearly separable or data have noise. It becomes difficult to use SVM. We have two ways to deal with these issues.
3.4 Soft Margin
Allow functional margin to be less than 1, or in some cases less than 0 . Adding the software margin to our equation, we have \[ \min _ { w ,b } \| \mathbf { w } \| ^ { 2} + c \sum _ { i = 1} ^ { N } \xi _ { i } ^ { k } \] \[ \text{ subject to } : y ^ { i } \left( \mathbf { w } \cdot \mathbf { x } ^ { i } + b \right) \geq 1- \xi _ { i } ,i = 1,\cdots ,N \] \[ \xi _ { i } \geq 0,i = 1,\cdots ,N \] With solution of \[ w = \sum _ { i = 1} ^ { N } \alpha _ { i } y ^ { i } x ^ { i } ,\quad s .t \sum _ { i = 1} ^ { N } \alpha _ { i } y ^ { i } = 0\text{ and } 0\leq a _ { i } \leq c \]
- ξ can be viewed as errors
- Tradeoff between maxmizing decision boundary margin and minimizing error
- Parameter c controls this tradeoff, c also puts a box constraints on \(\alpha\) and limits the influence of individual support vector
- C is set by the algorithm implementer, and can be derived using cross-validation
3.5 Mapping input space to higher dimension
When dataset is too hard to seperate linearly using soft margin. We can map the input space to higher dimension such that the data points become linearly seperatable.
3.6 Kernel Function
Kernel function is a function that maps input space to higher dimension. It can also be viewed measuring similarity. As a result, the decision boundary will be non-linear in the original input space.
- There are many kernel functions, the choice can be derived by cross-validation
Strengths
- solution is globally optimal
- Scales well with higher dimensional data
- Can handle non-traditional data like strings, trees
Weakness
- Need to choose a good kernel
- Can be computational expensive for large dataset
4 Decision Tree
Use a tree structure for solving classification problems. Its strengths are
- Similar to human decision, high interpretability
- Deals with discrete and continuous features without the need for transformation unlike perceptron and logistic regression
- Highly flexible, can represent more complex decision boundaries by increasing nodes and depth
The learning objective using decision tree is to find a decision tree h that achieves minimum error on training data.
4.1 Algorithm
A top-down, greedy search approach
- Choose the best test to be the root of tree
- Create a descendant node for each test outcomes
- Examples in training set S are sent to the appropriate descendent node based on the test outcome
- Recursively apply at each descendent node using the subset of training samples
- If all samples belong to the same class, turn it into a leaf node
Choosing the best test, we aim to maximize the information gain. In other words, minimize entropy.
4.2 Entropy
Entropy is a measure of uncertainty. If the probability is 1.0, there is no entropy. However, if the probability of an outcome over another is 0.5, the entropy is maximized.
Let y be a categorical random variable that can take k different values: \(v_1, v_2,...,v_k\) and \(p_i=P(y=v_i)\) for \(i=1,..,k\). The entropy of y, denoted as \(H(y)\) is defined as: \[ H ( y ) = - \sum _ { i = 1} ^ { k } p _ { i } \log _ { 2} p _ { i } \]
4.3 Building decision tree
We need to choose the split that maximizes benefit of split which effecitvely measures the mutual information between the features x and class label y. The root is then selected based on information gain. \[ \text{ Benefit of split } = U ( S ) - \sum _ { i } ^ { m } p _ { i } U \left( S _ { i } \right) \]
4.4 Dealing with multi-nomial features and continuous features
Multi-nominal: If a feature has more than two possible values.
- can be problemic because there is a bias to prefer multinominal features to binary features.
Continuous features
- Compute a threshold that maximizes information gain, essentially convert it to a binary feature
- Both continuous features and discrete features can be used to formulate a decision tree
4.5 Over-fitting
Due to being highly flexible, the decision tree is prone to over-fitting. Two interventions can combat that
- Early stop
- stop growing the tree when data split does not offer large benefits
- Post-pruning
- Separate training data into a training set and validating set
- Compute the impact on validation set when pruning each possible node
- Prune the node that improves the validation set performance in a greedy fashion
4.6 Regression Tree
Using decision tree to apply for regression problems. Prediction is computed as the average of the target values of all examples in the leaf node. Uncertainty is measured by the sum of squared errors within the node.
5 Questions
- The mechanism of Kernel function in SVM in mapping to higher dimension?
- The concept of information gain in decision tree?
- In choosing between linear models and SVM? Can overfitting be an issue in SVM?
6 Machine Learning Handson
Investigate a dateset of cancer microarray, and use machine learning model to to predict the outcome
6.1 Make machine learning research reproducible, make it pubic
- All optimal tuning parameters chosen for each technique evaluated
- The pseudocode for the data partitions
- The number of replicates performed to obtain the average test errors
- The seed used as the entry point into the random number generator during replication process
7 Feature engineering
7.1 Feature subset selection
Removing features that are not relevant or are redundant, very helpful for high dimensional data. There are three type of feature selection algorithms
7.2 The filters which extract features from the data without any learning involved
Gene ranking as a popular statistical method, which ranks gene in the dataset by their significance
- Unconditional Mixture Modelling (univariate): assume two different states of gene on and off, and checks whether the underlying binary state of gene affects the classification using mixture overlap probability
- Information Gain Ranking (univariate): approximates the conditional distribute P(C|F), where C is the class label and F is the feature vector. Information gain is used as a surrogate for the conditional distribution
- Markov Blanket Filtering (univariete): finds the features that are independent of the class label so that removing them will not affect the accuracy
7.3 Dealing with missing values
Three ways
- Remove the column with missing values, may be used if column contains mostly missing values
- loss of information
- Imputation: replace the missing value with some number. Scikit-learn’s imputation library replace values with mean by default.
- a better option most of time
7.4 Dealing with Categorical data
- One-Hot Encoding: create new binary columns for each category
8 Visualization of High Dimensional Data
8.1 TSNE
- TSNE: converts similarities between data point to join probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data
9 Experiment Design for ML algorithm Evaluation
Sparse: when a feature have most its entries as zeros.
- sparse maxtrix: a matrix contains mostly zero values
- dense matrix: a matrix contains mostly non-zero values
9.1 Why high sparsity is desired in many ML applications?
- Many real datasets such as texts and Microarray data are represented as very high dimensional vectors
- Most features in high dimensional vectors are usually non-informative or noisy and may serious affect the generalization performance
- A sparse classifier can lead to a simplified decision rule for faster prediction in large-scale problems
9.2 Performance Matrice
- accuracy
- AUC: area under ROC curve
- squared loss
9.3 Models that perform well across low-dimension to high-dimension data
- Random Forest, Neural nets, Boosted Tree, and SVMS
10 XGBoost
XGBoosst is the leading model for working with standard tabular data (eg. in Pandas Dataframe). It requires more knowledge and model tuning. It is an implementation of the gradient boosted decision trees algorithm.
- start with a baseline prediction, create cycles that repeatedly builds new models and combines them into an ensemble model
- to make a prediction, we add the predictions from all previous models
- n estimator is key parameter to tune, too small leads to underfitting, and too large leads to overfitting
- early stopping rounds is another parameter can stop the algorithm automatically when model stops improving
- learning rate allows early predictions to have smaller weight, and later ones have a larger weight. So we can use a larger n estimator value with learning rate
- n jobs is a parameter can be set to number of cores to take advantage of parallism
11 Pipelines
A pipeline bundles preprocessing and modeling steps so you can use the whole bundle as if it were a single step. It involves defining the steps of applying transformors to the data, then train the models. Benefits include
- cleaner code
- fewer bugs
- easier to productionize
- more options for model testing
12 Cross Validation
Provide a more accurate measure of model quality by fold the data into partitions.
- lower score means better model quality
13 Data leakage
Leakage causes a model to look accurate until making predictions with the model. Any variable that updates after target values is realized can cause data leakage, so they should be excluded from the feature set.
- Data leakage can cause major problem in ML production, need to be careful
14 General Visualization
14.1 Partial Dependence Plots
Show how each variable or predictor affects the model’s predictions after the model is fitted. Improve the interpretability.
15 Debugging and Error Analysis
Error analysis is crucial when applying machine learning to real world problems. [http://cs229.stanford.edu/materials/ML-advice.pdf]
15.1 Debugging learning algorithms
- high variance or overfitting: if error is low on training but high on testing. Overlearning the training data but no generalizing well
- increase training samples
- a smaller set of features
- high bias: if error is high on training regardless of training samples. Not enough features to learn the problem
- a larger set of features
- design better features
- algorithm not converging
- run gradient descent for more iterations
- use Newton’s method
- Not optimizing the right objective function
- parameter tuning current algorithm
- try another algorithm
15.2 Error analysis vs. ablative analysis
- Error analysis tries to explain the difference between current performance and perfect performance.
- Ablative analysis tries to explain the difference between some baseline performance and current performance. Eg. consider a image recognition algorithm with baseline performance of 94%, and best performance of 99%.
15.3 Two approaches to ML problems
- careful design: try to design the right features, dataset and algorithm architecture.
- pro: maybe more scalable
- con:Be careful with premature optimization
- build and fix: implement something quick, then run error analyses to fix its errors.
- pro: faster to market
16 misc
16.1 treatment effect
The difference between treated and untreated group
16.2 parameteric
Assume data is drawn from normal distribution
16.3 non-parameteric
Does not assume data to have normal distribution
16.4 unconfoundedness
An assumption that confounding variables are measured in the dataset


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NYSE stocks by NASDAQ industry classification from differrent data providers:
Alpha Vantage IEX Cloud
Market cap calculated on sale price 10-04-2019.


PAM_RHOSTS
Section: Linux\-PAM Manual (8)Updated: 03/02/2009
Index Return to Main Contents
pam_rhosts - The rhosts PAM module
Synopsis
- pam_rhosts.so
DESCRIPTION
The authentication mechanism of this module is based on the contents of two files; FC/etc/hosts.equivF[] (or and FC~/.rhostsF[]. Firstly, hosts listed in the former file are treated as equivalent to the localhost. Secondly, entries in the user's own copy of the latter file is used to map "remote-host remote-user" pairs to that user's account on the current host. Access is granted to the user if their host is present in FC/etc/hosts.equivF[] and their remote account is identical to their local one, or if their remote account has an entry in their personal configuration file.
The module authenticates a remote user (internally specified by the item PAM_RUSER connecting from the remote host (internally specified by the item PAM_RHOST). Accordingly, for applications to be compatible this authentication module they must set these items prior to calling pam_authenticate(). The module is not capable of independently probing the network connection for such information.
OPTIONS
debug
- Print debug information.
silent
- Don't print informative messages.
superuser=account
- Handle account as root.
MODULE TYPES PROVIDED
Only the auth module type is provided.
RETURN VALUES
PAM_AUTH_ERR
- The remote host, remote user name or the local user name couldn't be determined or access was denied by FC.rhostsF[] file.
PAM_USER_UNKNOWN
- User is not known to system.
EXAMPLES
To grant a remote user access by FC/etc/hosts.equivF[] or FC.rhostsF[] for rsh add the following lines to FC/etc/pam.d/rshF[]:
-
#%PAM-1.0 # auth required pam_rhosts.so auth required pam_nologin.so auth required pam_env.so auth required pam_unix.so
SEE ALSO
rootok(3), hosts.equiv(5), rhosts(5), pam.conf(5), pam.d(5), pam(8)
AUTHOR
pam_rhosts was written by Thorsten Kukuk<EMAIL_ADDRESS>Index
This document was created by man2html, using the manual pages.
Time: 05:34:28 GMT, December 24, 2015


French arms firm Thales to appeal Zuma corruption charge ruling in South Africa's top court
JOHANNESBURG (Reuters) - French defense firm Thales said on Tuesday it would ask South Africa’s highest court for permission to appeal an October ruling dismissing its request to have charges that it bribed former President Jacob Zuma permanently dropped.
Thales is accused of agreeing to pay Zuma 500,000 rand ($34,000) annually for protection from an investigation into a $2 billion arms deal in 1999.
The charges against Thales and Zuma were originally filed a decade ago but then set aside by the National Prosecuting Authority (NPA), paving the way for Zuma to successfully run for president in 2009.
The charges were reinstated in March 2018 by the NPA following appeals and lobbying by opposition parties and local anti-corruption groups.
In mid-October the Pietermaritzburg High Court dismissed an application by Zuma and Thales for a permanent stay of prosecution and set a provisional trial date of Feb. 4, 2020.
“Thales confirms that on 1 November 2019 it applied to the Constitutional Court of South Africa for leave to appeal the High Court’s decision which dismissed its challenge to the lawfulness of the decision to reinstate charges against it,” the French firm said in a statement.
Zuma has also said he will appeal the decision, meaning the trial would likely begin only in late in 2020.
Thales’ local public relations firm did not immediately reply to an email sent by Reuters seeking details of the argument it would make in the appeal application.
Thales, known as Thompson-CSF in 1999, has consistently argued that it has no knowledge of any transgressions having been committed by any of its employees in relation to the awarding of the contracts.
($1 = 14.7294 rand)


These navs will always stay in DOM and have no roles
Because default values have been customized in the config


Before Change
test_type = "rl" // type of test being implemented (see comment at start of file)
num_aggressive = 1 // number of aggressive drivers
num_cars = 22 // total number of cars in simulation
percent_rl = 0.7 // percent of vehicles that are rl
num_auto = int(percent_rl*num_cars) // number of controllable (rl) vehicles
num_human = num_cars - num_auto // number of uncontrollable (human) vehicles
After Change
stub(globals())
sumo_params = {"time_step": 0.1, "traci_control": 1, "rl_lc": "no_collide", "human_lc": "strategic",
"rl_sm": "no_collide", "human_sm": "no_collide", "port": 8813}
sumo_binary = "sumo-gui"
env_params = {"target_velocity": 8, "max-deacc": -6, "max-acc": 3, "lane_change_duration": 5,
"fail-safe": "None"}


| DOSE: |
2.5 mg |
oral |
DOB |
(powder / crystals) |
Two good friends and I decided to take a weekend and trip on DOB. My
initial expectations were of a trip very much like 2C-B, but spanning
sixteen to twenty hours and fueled by the presence of amphetamine.
What occurred was something not entirely different, but not something
I'd been anticipating.
[T+0h] Saturday noon. After I munched on a breafast of bacon and eggs,
we three wanderers each ingest 2.5mg DOB dissolved in distilled water.
Mixed with orange juice; taste imperceptible. Here goes.
[T+0:30] First twinges of a psychedelic drug in my system.
[T+0:45] The psychedelic is clearly taking over my brain. Interacting
with the world around me is difficult.
[T+1h] Two of the three of us arrive at a Safe location. We have a
friend who'd volunteered to drive us around that weekend, and so we
wait for him to get ready to take us home. We step inside (out of the
sunlight) and immediately my field of vision explodes into
characteristically 2C-B visuals. I notice as well a wave of
amphetamine crash over my body, and I am soon feeling like the
strongest man alive. I help my chauffeur lug some very heavy cargo to
the van -- effortless.  I am smoking pot because
I am compulsed towards it; it seems like a good idea to head off any
GI discomfort at the pass.
[T+1:30] We two arrive at home and meet the third, who's been coming
up on the same schedule. At this point, when I am in a darkened area,
I am completely unable to distinguish visual effects from real
objects. Amphetamine high is still CRUISING.
[T+2h] I am so amped, I could gnaw the head off a puppy. Fortunately,
the fact that my entire perspective is moving, breathing, pulsating,
billowing, and crawling keeps me from doing anything useful. More
smoking, and it is noticeably hard to keep it together long enough to
pack a bowl. I am floored.
[T+2:30] I am thanking my lucky stars that I am going to have to
interact only with people I know very well for the next 20 hours. I
am quite sure that I am having a good time, though -- still feels like
2C-B, but HARDER.
[T+3h] I would trade a kidney for a pack of Bazooka gum. I have been
becoming more and more disoriented with my surroundings throughout the
whole three hours, and it is here that I would call myself 'lost' in
my own house.
[T+4h] A delightful, non-tripping-his-eyeballs-out friend shows up,
and we three enjoy talking to him. I think it is at this point that
the trip begins its second leg; the 2C-B visuals start to fade (very
slightly, at this point) and get the hell out of the way, and a very
profound, intellectual DOB experience begins.
[T+4:20] I pack a bowl. 'See you in 12 hours, guys.' *rrrrrrrip*
[T+5h] I can feel a slight tension lifted from my body as I
simultaneously feel my brain kick into high gear. I eat something
because I can somehow tell I need to.
[T+6h] Over T+5-6h, I am able to clear the visuals out of the way
(although they're still _very_ strong) and think, think, think. I
suddenly questioned every fact about reality I'd taken for granted.
[T+7h] The amphetamine din has settled into a dull roar, and I am
leading my appreciative brain through an odyssey of metaphysically-
deep insights into the real and perceived worldview I call 'Reality',
leaving open at every corner the chance for anything to be possible.
The three of us are happily and hungrily announcing and discussing our
current thoughts. We are settling down into a dark room in the middle
of our house, almost like a cocoon, each person in turn putting on the
music he believed to be perfect for the situation.
[T+9h] We are devouring any knowledge we can, eager to share our
amazing experiences and contemplations. We eat when we need food,
drink when we need water, smoke when it feels right. I feel as though
I'm overseeing my body at a higher level than I ever have; I have a
good and innate sense of how to take care of myself (food, water,
pot, music) without dwelling on the individual feelings and pains in
my body. It is apparent that the 'peak' (T+1-8h) has passed and the
effects are beginning a very slow, gradual taper. Three non-tripping
friends appear, and it is very comforting to see faces from the
outside world. We talk and enjoy each other.
[T+12h] Less visual, more brainy! DOB thoughts are leaving me
dumbfounded. I am anxious to wrap my brain around any scenario I can
conceive. I have a correct and instinctual understanding of
electricity and magnetism, quantum mechanics, thermodynamics, organic
chemistry, algorithmic mathematics in such a way that I can simply
'feel' in what trend a particular system will act. By possessing this
deep understanding of physics and mathematics, the rules of my
universe, I can detect patterns shared among wildly different systems
(the ties that bind between social, chemical, and physical
interactions). I cook pasta for the six of us, and as it is
well-received I ponder the satisfaction of crafting something for my
friends and having it appreciated.
It's interesting to note that this experience was more what I would
call 'Ecstasy' than MDMA is. My past drug experiences were
embarrassed and belittled by the DOB's gravity, its unique
characteristic of allowing me to invent a constant stream of Heavy
Shit for my mind to happily plow through. I felt like I'd never
thought about anything so important; my most fundamental concepts of
reality were shaken to the core for the first time, and in comparison
it makes the visceral high of a 'quality' MDMA or mushroom trip seem
positively rude. Whereas MDMA might allow me to reach a comfortable
synergy with the people around me, DOB provided a glimpse of what it
might be like to achieve Nirvana, to exist as pure thought. When it
comes right down to it, this is why I ingest psychedelics. This is
what it's all about.
I had some very momentous insights into heritage and family ties, from
my relationship with my dad to my kinship with the creatures from
which I evolved. I was given a chance to grasp the infinity of the
universe; understanding its specifics was clearly out of reach, but I
understood why it was acting how it was acting, on the whole. DOB is
completely orthogonal to the emotional realm; it does not alter my
mood at all, and only allows me to enjoy it because I enjoy thinking.
[T+14h] 2am Sunday, and it is here that things really begin to screech
to a halt. Our conversation becomes less frequent, and I become more
introspective. It's time to let the music take over.
[T+16:20] 4:20am and I'm keeping true to the previous afternoon's
promise. We are content to keep ourselves warm, hydrated and fed, and
bathed in music. Very faint visuals. Coming down, no question about it.
[T+20h] It is around this point in time when each of us three dozes
off in turn; we're right on the tail end of the DOB's effects.
[T+22h] At 10am Sunday, I declared the DOB's mental effects 'over'.
[T+33h] After a full day of doing stuff and feeling generally ok, it's
sleepytime.  I cannot describe it without its power
making itself apparent in my speech; DOB will forever hold a special
place in my heart, a place earned as a Truly Psychedelic Drug. If
treated with respect, I believe DOB can show us how awesome the
human condition can be.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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For smooth closed simply connected 4-manifolds, the only homotopy invariant is the intersection form in the second homology with integer coefficients: two manifolds of this kind are homotopy equivalent iff their intersection forms are isomorphic (Whitehead [Whitehead1949], Pontryagin [Pontryagin1949]).
As Freedman [Freedman1982] proved, for manifolds of this type homeomorphism is equivalent to homotopy equivalence. Thus, two smooth closed simply connected 4-manifolds are homeomorphic iff their intersection forms are isomorphic.
In contrast, on many closed simply connected 4-manifolds there are infinitely many smooth structures. Of course, the number of smooth structures on a closed 4-manifold cannot be uncountable (on a non-compact 4-manifold it can, as is the case for $ \mathbb R^4$). There is no conjectural classification scheme for smooth structures on any closed 4-manifold. Stern entitled his survey paper [Stern2006] by a question "Will we ever classify simply-connected smooth 4-manifolds?". In the introduction to his paper he wrote: "The subject is rich in examples that demonstrate a wide variety of disparate phenomena. Yet it is precisely this richness which, at the time of these lectures, gives us little hope to even conjecture a classification scheme."
It is a long term challenge for topologists to find invariants which would distinguish smooth structures on a 4-manifold, but would be independent on analytic tools. This is not just an aesthetic issue: the analytic tools are not convenient in some situation.
For example, in dimension 4 smooth structures are closely related to PL-structures (piecewise linear structures). In any dimension a smooth structure on a manifold determines on it a specific PL-structure uniquely defined up to PL-homeomorphism. In dimensions $ \le 6$ any PL-structure can be obtained in this way from a smooth structure and the smooth structure is unique up to diffeomorphism, see [Siebenmann1970]. Hence any invariant of a smooth structure on a 4-manifold depends only on the PL-structure. However, in order to prove that two homeomorphic PL-manifolds of dimension 4 are not PL-homeomorphic, now one has to equip them with smooth structures, and then calculate the invariants. The techniques for calculation are not easy, especially if the smooth structure is not defined naturally (say, as the underlying structure on a complex surface), but just obtained by smoothing of a PL-structure.
All the known invariants distinguishing smooth structures on closed simply connected 4-manifold $ X$ require non-trivial intersection form on $ H_2(X)$. In particular, for the 4-sphere these invariants give nothing, they cannot help in disproving of the 4-dimensional differential Poincaré conjecture, according to which any closed 4-dimensional manifold homeomorphic to $ S^4$ is diffeomorphic to $ S^4$.
Thus, a development of new approaches to building invariants of smooth structures is desirable for several good reasons.
This identification makes the Seiberg-Witten invariant resembling the Alexander polynomial of a link. For a classical link $ L\subset S^3$, the Alexander polynomial is an element of $ \Z[H_1(S^3\sminus L)]$, the integer group algebra of the first homology group.
The Alexander polynomial can be defined in many ways. In particular, it admits purely topological definitions.
The similarity between Seiberg-Witten invariant and Alexander polynomial gives a hope to find either a definition of Seiberg-Witten invariant free of heavy analysis, or, at least, to invent similar invariants that solve the same problems, but are defined and calculated in ways more traditional for topology. This hope is supported by a relation between the Seiberg-Witten invariant and the Alexander polynomial discovered by Fintushel and Stern in 1996 [Fintushel and Stern1998]:
A knot surgery on $ T$ takes away from $ X$ a tubular neighborhood $ T\times D^2$ of $ T$ and attaches $ S^1\times(S^3\sminus N_K)$ instead, where $ N_K$ is an open tubular neighborhood of a smooth knot $ K\subset S^3$. Observe that the boundary of $ S^3\sminus N_K$ is diffeomorphic to the 2-torus $ S^1\times S^1$, and hence the boundary of the inserted piece $ S^1\times(S^3\sminus N_K)$ is diffeomorphic to the 3-torus $ S^1\times S^1\times S^1$ as well as the boundary $ T\times\p D^2$ of the piece removed. The attaching is performed by a diffeomorphism $ S^1\times\p(S^3\sminus N_K)\to T\times\p D^2$ which maps $ \{pt\}\times l$, where $ l$ is a longitude, i.e., a circle bounding in $ S^3\sminus K$, to a fiber $ \{pt\}\times\p D^2$. This requirement on the attaching map does not determine the map up to diffeotopy, and hence does not necessarily determine $ X_K$ up to diffeomorphism. However, by the following theorem, under some assumptions, all the manifolds obtained from the same $ (X,T)$ and $ K\subset S^3$ by knot surgeries have the same Seiberg-Witten invariant.
Present definitions of the Seiberg-Witten invariant are not applicable to $ (S^3\sminus K)\times S^1$ or $ (S^3\sminus N_K)\times S^1$. So, one cannot speak about $ {SW}_{(S^3\sminus K)\times S^1}$. However, if the $ {SW}$ was extended to this setup and satisfied a reasonable additivity property, Fintushel-Stern Theorem would suggests that $ {SW}_{(S^3\sminus K)\times S^1}$ should be $ \Delta_K$.
This module is called the Alexander module of $ K$. It is finitely generated and admits a square matrix of relations. The determinant of this matrix is the Alexander polynomial. It is defined up to multiplication by units of $ \Z[\Z]$ (that is by monomials $ \pm t^k$).
According to this construction, the Alexander polynomial happens to belong to $ \Z[H_1(S^3\sminus K)]$. The Seiberg-Witten invariant of $ X$ belongs to $ \Z[H_2(X)]$. What could be a space $ Y$ such that $ H_1(Y)=H_2(X)$?
However, the loop space per se cannot do the job:
First, let us make it closer to the smooth structure of $ X$. The loop space $ \Omega X$ contains the space $ \Omega_{Diff}X$ of differentiable loops $ S^1\to X$ that have at the base point fixed non-vanishing differential. The replacement of $ \Omega X$ by $ \Omega_{Diff}X$ does not change the homotopy type: well-known approximation theorems imply that $ \Omega_{Diff}X$ is a deformation retract of $ \Omega X$.
Let $ KX\subset \Omega_{Diff}X$ be the subspace which consists of loops that are smooth embeddings. Denote $ \Omega_{Diff}X\sminus KX$ by $ DX$. Observe that $ \codim_{\Omega_{Diff}X}DX=2$, hence the inclusion homomorphism $ \pi_1(KX)\to\pi_1(\Omega_{Diff} X)=\pi_1(\Omega X)$ is onto.
Obvious suggestion: consider the covering $ \widetilde{KX}\to KX$ induced by the universal covering $ \widetilde{\Omega X}\to \Omega X$. The automorphism group of the covering $ \widetilde{\Omega X}\to \Omega X$ is $ H_1(\Omega X)=H_2(X)$. Therefore $ H_*(\widetilde{\Omega X})$ and $ H_*(\widetilde{KX})$ are modules over $ \Z[H_1(\Omega X)]=\Z[H_2(X)]$.
The action of $ H_1(\Omega X)$ in $ H_*(\widetilde{\Omega X})$ is trivial, since $ \Omega X$ is an $ H$-space, while $ KX$ is not an $ H$-space and $ H_*(\widetilde{KX})$ may be an interesting $ \Z[H_2(X)]$-module. It has a broad range of invariants belonging to $ \Z[H_2(X)]$ similar to the Alexander polynomial. Indeed, if $ X$ is simply connected, then $ H_2(X)$ is a free abelian group of finite rank $ r$, and $ \Z[H_2(X)]$ is isomorphic to a ring $ \Lambda_r=\Z[t_1,t_1^{-1},t_2,t_2^{-1},\dots t_r,t_r^{-1}]$ of Laurent polynomial in $ r$ variables with integer coefficients, the same ring as in the situation of the Alexander module of a classical link.
A finitely generated module $ M$ over $ \Lambda_r$ gives rise to a filtration of $ \Z[H_2(X)]$ \begin{equation*}\Fitt_0(M)\subset\Fitt_1(M)\subset\dots\subset\Z[H_2(X)]\end{equation*} by Fitting ideals . The $ i$th Fitting ideal $ \Fitt_i(M)$ is generated by the minors (determinants of submatrices) of order $ r-i$ of the matrix of defining relations for $ M$ In the topological literature Fitting ideals are called also elementary ideals . A generator of the minimal principal ideal containing $ \Fitt_i(M)$ is denoted by $ \Delta_i(M)$.
In the context of link theory, when $ \Lambda_r=H_1(S^3\sminus L)$, where $ L$ is an $ r$-component link and $ M=H_1(\widetilde{S^3\sminus L})$, the Laurent polynomial $ \Delta_i(M)$ is called the $ i$th Alexander polynomial of $ L$. The $ 0$th Alexander polynomial is one of the oldest link invariants. It was introduced by Alexander [Alexander1928].
Similarly, for a smooth simply connected closed 4-manifold $ X$, each $ H_i(\widetilde{KX})$, as a module over $ \Z[H_2(X)]$, gives rise to a sequence of Laurent polynomials. They resemble the Seiberg-Witten invariant. At least, they belong to the same $ \Z[H_2(X)]$. The modules $ H_i(\widetilde{KX})$ are also invariants of $ X$.
The problem: Does there exist homeomorphic smooth simply connected closed 4-manifolds $ X_1$, $ X_2$ such that $ H_*(\widetilde{KX_1})$ and $ H_*(\widetilde{KX_2})$ are not isomorphic?
The most convincing argument is that for different smooth structures on the same topological manifold $ X$ a topologically observable difference between smooth structures was the minimal genus of a smoothly embedded surface realizing an element of $ H_2(X)$. For any smooth structure each class can be realized by an immersed sphere with transverse self-intersection (by the Hurewicz theorem and transversality), but the minimal number of double point of such immersion depends on the structure. It is greater than or equal to the minimal genus of a smoothly embedded surface realizing the class.
An immersion $ f:S^2\to X$ can be used to produce a loop in the space of loops $ \Omega_{Diff} X$. A loop in $ \Omega_{Diff}S^2$ sweeping the whole $ S^2$ composed with $ f$ gives a loop in $ \Omega_{Diff} X$. Generically this gives a loop in $ KX$, because under a generic choice of loop in $ \Omega_{Diff} S^2$, points in the preimage of a double point of $ f$ are passed at different moments. However, in families of loops realizing elements of high-dimensional homology groups $ H_k(\Omega_{Diff} S^2)=\Z$ these pairs of points appear on some of the loops. Such a family does not realize a homology class in $ KX$ or $ \widetilde{KX}$.
The space $ DX$ consists of differentiable loops that are not embeddings. It fits to the collection of discriminant hypersurfaces studied by Vassiliev [Vassiliev1994]. The universal covering $ \widetilde{\Omega X}\to \Omega X$ defines a covering $ \widetilde{DX}\to DX$.
Resolve singularities of the discriminant $ DX$, as Vassiliev did. This gives rise to a filtration in $ H^*(\widetilde{DX})$. The first terms of these filtration are easier to calculate than the whole cohomology group.
At first glance, the situation is much more complicated than in the original setup in the theory of Vassiliev knot invariants. Let us examine the extra difficulties.
First, instead of the space of singular knots, we have to deal with its covering space. What does happen, when we pass from knots to points of a covering space?
In general, if $ p:X\to B$ is a covering, $ b_0\in B$, $ x_0\in X$ are points such that $ p(x_0)=b_0$ and $ X$ is path connected, then a point $ x\in X$ is uniquely determined by its image $ p(x)\in B$, a path $ s:I\to B$ such that $ s(0)=b_0$, and its covering path $ \widetilde s:I\to X$ starts at $ x_0$ and finishes at $ x$. The path $ s$ is defined up to path homotopy and multiplying by loops covered by loops in $ X$.
In particular, a point of $ \widetilde{\Omega X}$ (no matter if it belongs to $ \widetilde{KX}$ or $ \widetilde{DX}$) is defined by a loop $ u:S^1\to X$ and a continuous map $ f:D^2\to X$ with $ f|_{S^1}=u$ considered up to homotopy which is fixed on $ S^1$. The action of $ H_2(X)$ in $ \widetilde{\Omega X}$ is realized in this model as addition to the homotopy class of $ f$ homotopy classes of maps $ S^2\to X$ realizing elements of $ H_2(X)$.
Second, the space of all loops in the Vassiliev setup is contractible. (Recall that loops there have the base point and the tangent vector at the base point fixed.) This allowed to apply the Alexander duality between the homology of the discriminant and cohomology of its complement, the space of knots. This was done in a finite dimensional (say, polynomial) approximation of the loop space.
Here the loop space is not contractible. Therefore, the Alexander duality is not applicable. However, it may be replaced by the Alexander-Poincaré duality between the homology of $ \widetilde{KX}$ and a relative cohomology, the cohomology of $ (\widetilde{\Omega_{Diff} X},\widetilde{DX})$.
The homology of loop space $ \Omega_{Diff}X$ and its universal covering accommodate a rich structure of the string topology operations [Chas and Sullivan1999]. Geometrically, one can expect that this structure incorporates the same information as the natural filtration of the discriminant and the Alexander-Poincaré duality. Apparently the connection have not been investigated. Nonetheless, it would be natural to expect that the string topology is as strong as the invariants discussed above.
Problem. Is the string topology sensitive to smooth structures on 4-manifolds?
It would be interesting also to find direct relations between the Vassiliev invariants theory and the string topology in the setup of classical knot theory.


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--- layout: default title: MMME'17 - Events meta: Organize a symposium or workshop within the conference! keyword: mining conference, material conference, mineral processing conference, metal processing conference, solidification conference, metallurgical conference, casting conference, composite materials conference, corosion, metal processing, phase diagram, surface phenomena, heat transfer, fluid flow, fluid dynamics, fluid mechanics, mining, material, mineral processing, metal processing, solidification, metallurgical, casting, composite materials, corosion conference, metal processing conference, phase diagram conference, surface phenomena conference, heat transfer conference, fluid flow conference, fluid dynamics conference, fluid mechanics conference ---
There are currently two types of opportunities for an exhibit at MMME. They are as followed (prices are in Euros):
Table exhibit, €1028.50 (VAT included):
Booth exhibit, €1028.50 (VAT included):
The exhibit provides a platform for companies and institutions to present their products and distribute brochures and business cards. The exhibit also provides a unique networking opportunity with many experts and researchers in the field of multimedia and human computer interaction.
If you are interested in an exhibit at MMME, please do not hesitate to contact us at<EMAIL_ADDRESS>The 4th International Conference on Mining, Material and Metallurgical Engineering (MMME'17) invites you to propose a workshop.
The proposed workshop, if accepted, will be co-located with and under the umbrella of the conference.
Proposals by prospective chairs should be submitted to<EMAIL_ADDRESS>and will be evaluated by the Organizing and Scientific Committees. Upon approval, the Organizing Committee will disseminate the news through the conference website and call for papers. To obtain a high attendance, we kindly ask that the prospective chairs also publicize the event and personally contact researchers who they think will be interested to contribute. The minimum number of registered presenters for the event to take place is 7. Should attendance reach this minimum requirement, registration fees for the Chair will be waived.
If you have any questions, please do not hesitate to contact us at info@mmmeconference.com.


Choose from a variety of themes or develop your own theme easily.
PrimeNG is a design agnostic library so unlike other UI libraries it does not enforce a certain styling such as material or bootstrap. In order to achieve this, styling has been separated into core and theme. Core resides inside PrimeNG to implement the structure of the components such as positioning whereas theme brings the colors, paddings and margins.
PrimeNG offers various free themes and premium themes along with premium templates that provide an application layout as well. All the free themes are built with the Theme Designer and the npm package brings the CSS output of the theme whereas SCSS is kept as a premium feature in the designer. This means free themes are open source and for customization with SASS, a designer license needs to be acquired.
CSS of the themes share the same license as PrimeNG which is MIT, this means the generated CSS can be customized per your needs however this should be avoided if your customizations are not simple. For instance even to change a primary color, since there is no variable a find and replace should be performed various times. On the other hand, this can be achieved by changing a single variable e.g. $primaryColor. Visit the SASS API for the documentation of available customization options.
Designer is the ultimate tool to create your own PrimeNG experience powered by a SASS based theme engine with 500+ variables and a Visual Designer. PrimeNG only ships the generated CSS of Material, Bootstrap, Saga, Vela, Arya, FluentUI and legacy themes whereas Designer provides full access to the whole SASS structure and the variables of these pre-built themes for easier customization. In addition, designer provides exclusive premium themes to subscribers including Soho, Viva, Mira and Nano that are not available in core PrimeNG distribution.
Whether you have your own style guide or just need a custom theme, Designer API is the right tool to design and bring them to existence.
Visit Designer API HomePage for more information and live demos.
Themes can be switched on-the-fly so that users of your app can define their own experience. View article and the video tutorial for the implementation details.
PrimeNG utilizes rem units to make sure the components blend in with the rest of your UI perfectly. This also enables scaling, for example changing the size of the components is easy as configuring the font size of your document. Code below sets the scale of the components based on 16px. If you reqire bigger or smaller components, just change this variable and components will scale accordingly.
Some commonly used components such as inputs, buttons and datatable also provide per component scaling with special classes. Components with specific scaling options are documented in their own documentation.
Theming styles the components globally, in case you required to change the style of a certain component for a specific use case use the class property and override the defaults. Example below changes the background of the panel. Note that this is only for local styling, if you require to change the background color of all the panels, a custom theme is a far better choice.
If the style does not apply due to view encapsulation, try ng-deep scoping.
A couple of utility classes are provided as a solution to common requirements.
| Name | Description |
|---|---|
| p-component | Applies component theming such as font-family and font-size to an element. |
| p-disabled | Applies an opacity to display as disabled. |
| p-sr-only | Element becomes visually hidden however accessibility is still available. |
| p-reset | Resets the browsers defaults. |
| p-link | Renders a button as a link. |
| p-error | Indicates an error text. |
| p-text-secondary | Applies the text color of the theme with the secondary priority. |


CopyMemoryString()
Syntax
Result = CopyMemoryString(*String [, @*DestinationMemoryID])Description
Copy the string from the specified address to the destination memory address if specified, or at the end of the previous buffer if omitted.
Parameters
*String The address of the string to copy. The string must be terminated with a null-character. The string is expected to be in the PB string format. @*DestinationMemoryID (optional) The pointer to a variable holding the address with the destination buffer. After the string has been copied, the variable *DestinationMemoryID will point to the null-character at the end of the copied string, so a further call to this function will append the new string to the previous one.
If this parameter is omitted, the address from the previous call is used.
Return value
Returns the value of *DestinationMemoryID after the string was copied.
Example
*Buffer = AllocateMemory(1000) *Pointer = *Buffer CopyMemoryString("Hello", @*Pointer) CopyMemoryString(" World") ; This one will be put just after "Hello" *Pointer-2 ; Come back from 2 characters (on the 'l' of 'World') CopyMemoryString("LD") ; Finally the last two letters will be uppercase Debug PeekS(*Buffer)
See Also
CopyMemory(), PeekS(), PokeS()
Supported OS
All


Click on any group to view group information, or "Show Students" to choose a student.
Click on any student to view student information, or "Show Groups" to choose a group.
Click on any group, student, category, or word to find out more information. You can also select any number of groups or students and categories or words, and simply click "Assign Words" to assign them.
Click on any group, student, category, or word to find out more information. You can also select any number of groups or students and categories or words, and simply click "Assign Words" to assign them.
Click on any group, student, category, or word to find out more information. You can also select any number of groups or students and categories or words, and simply click "Assign Words" to assign them.
Click on any group, student, category, or word to find out more information. You can also select any number of groups or students and categories or words, and simply click "Assign Words" to assign them.
Click on any group, student, category, or word to find out more information. You can also select any number of groups or students and categories or words, and simply click "Assign Words" to assign them.
Click on any category to view category information, or "Show Words" to choose a word.
Click on any word to view word information, or "Show Categories" to choose a category.


To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video


As I made my way through the world, along lanes and pathways lined by larch, laburnum, and pine, I came at last to the village of the cheesegraters. Here, I thought, is somewhere I can lay my hat. My hat is not a cheese-hat. It will not be grated.
And it was not. But nor was it admired, as I felt sure it would be. No villager came up to me to say “Cor blimey, what a splendid titfer you've got there atop your head, meinheer!” Rather, they seemed a sullen people, jowly and hoarse, and sullen, sullen.
In the village square I sat, to observe their ways. I jotted no notes in my jotting pad. It struck me, and forcibly, forcibly, that they seemed to grate more marzipan than cheese. The air was scented with almond. Theirs was a sullen grating.
There were many gratings, too, punctuating the paving slabs of the path crisscrossing the square. Metal gratings that gleamed in the sunlight in lattice patterns of stupendous complexity, like the most delicate of lacework. What lay beneath those gratings?, I wondered. Drains and sewers, drains and sewers, came the reply, whispered on the breeze.
Do not scoff when I suggest I was spoken to by the wind, by the stirrings of the almond-scented air. That is how it is in the village of the cheesegraters, that sullen place, where cheese comes cheap, and marzipan cheaper.
It was a tiny village, and when I left the square I was soon enough out in open country. Now there was no wind to speak of, or to speak to me. All was still and silent. I saw a bird in the sky, but I know nothing of ornithology. I passed on, my splendid titfer atop my head, towards another village.


[1]
it is fronted by an array of glass cubes coloured green blue purple red.
[2]
this hotel looks fantastic - it is no balcony there are the rooms in the sun.


{% extends "base.html" %} {% comment %} Copyright 2011 Google Inc. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This is the Django template for the snapshots listing page. {% endcomment %} {% block title %}Snapshots{% endblock %} {% block head %} {% if refresh %} {% endif %} {% if msg == "SNAPSHOT_DELETING" %} {% endif %} {% endblock %} {% block heading %}Your snapshots are listed below{% endblock %} {% block body %}
You cannot delete a snapshot until it is done building.
{% endif %} {% if msg == "NO_ID_EXPORT" %}You must specify a snapshot in order to export it.
{% endif %} {% if msg == "NO_ID_DELETE" %}You must specify a snapshot in order to delete it.
{% endif %} {% if msg == "INVALID_SNAPSHOT" %}The ID you specified does not correspond to a snapshot.
{% endif %} {% if msg == "SNAPSHOT_DELETING" %}Your snapshot is being deleted in the background.
{% endif %}


{% extends 'templates/navigation.html' %} {% load static %} {% load custom_tags %}
Start with processing your smallRNA-Seq data, build a custom annotation (eg, for transgenes) or use official ones to align your data, filter reads by species and group by genes, and compare samples using scatter plots and venn diagrams.
You need to process your FASTQ data first to do analysis. Processed data can be accessed by your group members unless hidden by owner.
Using already processed data, you can perform your own analysis in the following order.
{% if samples > 1 %} There are {{samples}} samples in the repository from {{experiments}} experiments. {% elif samples > 0 %} There is only 1 sample in the repository. {% else %} There is no data yet in the repository. {% endif %}
You can check your performed analysis or interactive visualizations. Only you can see your results unless you share them.
Build alignment file for your transgene, create a whole analysis workflow, or add FASTQ processing rule for a smallRNA-Seq protocol.


application
/
net.corda.v5.application.flows
/
FlowInfo
/
flowVersion
flowVersion
val
flowVersion
:
Int
The integer flow version the other side is using.
See Also
InitiatingFlow


We will post photos here after the wedding, until then here is some photo information for you.
Please do not feel you have to get any pictures. We have an amazing photographer who is getting tons of great pictures + all the important ones. You can view, download, print, and post for free. Register to access the professional photo taken by Shain from Once Like a Spark Photography REGISTER HERE FOR ACCESS TO PROFESSIONAL PHOTOS.


Source code for betterboto.client
import boto3
from . import cloudformation
from . import servicecatalog
from . import organizations
def make_better(service_name, client):
if service_name == 'cloudformation':
return cloudformation.make_better(client)
elif service_name == 'servicecatalog':
return servicecatalog.make_better(client)
elif service_name == 'organizations':
return organizations.make_better(client)
return client
[docs]class ClientContextManager(object):
"""
ClientContextManager allows you to use boto3 client as a python context manager.
This allows you to perform the following::
with ClientContextManager('cloudformation') as cloudformation_client:
cloudformation_client.create_stack(**args)
"""
def __init__(self, service_name, **kwargs):
super().__init__()
self.service_name = service_name
self.kwargs = kwargs
def __enter__(self):
self.client = boto3.client(
self.service_name,
**self.kwargs
)
self.client = make_better(self.service_name, self.client)
return self.client
def __exit__(self, *args, **kwargs):
self.client = None
[docs]class MultiRegionClientContextManager(object):
"""
MultiRegionClientContextManager allows you to use boto3 client as a python context manager for multiple regions.
This allows you to perform the following::
with MultiRegionClientContextManager('cloudformation', ['us-east-1','eu-west-1']) as cloudformation_clients:
for region_name, cloudformation_client in cloudformation_clients.items():
cloudformation_client.create_stack(**args)
If you want to deploy to multiple regions at the same time then you should use Python Threads
"""
def __init__(self, service_name, regions, **kwargs):
super().__init__()
self.service_name = service_name
self.regions = regions
self.clients = {}
self.kwargs = kwargs
def __enter__(self):
for region in self.regions:
c = make_better(
self.service_name,
boto3.client(
self.service_name,
region_name=region,
**self.kwargs
)
)
self.clients[region] = c
return self.clients
def __exit__(self, *args, **kwargs):
self.clients = None
[docs]class CrossAccountClientContextManager(object):
"""
CrossAccountClientContextManager allows you to use boto3 client as a python context manager for another account.
This allows you to perform the following::
with CrossAccountClientContextManager(
'cloudformation',
'arn:aws:iam::0123456789010:role/deployer',
'deployment_account_session',
) as deployment_account_cloudformation:
deployment_account_cloudformation.create_stack(**args)
"""
def __init__(self, service_name, role_arn, role_session_name, **kwargs):
super().__init__()
self.service_name = service_name
self.role_arn = role_arn
self.role_session_name = role_session_name
self.kwargs = kwargs
def __enter__(self):
sts = boto3.client('sts')
assumed_role_object = sts.assume_role(
RoleArn=self.role_arn,
RoleSessionName=self.role_session_name,
)
credentials = assumed_role_object['Credentials']
kwargs = {
"service_name": self.service_name,
"aws_access_key_id": credentials['AccessKeyId'],
"aws_secret_access_key": credentials['SecretAccessKey'],
"aws_session_token": credentials['SessionToken'],
}
if self.kwargs is not None:
kwargs.update(self.kwargs)
self.client = boto3.client(**kwargs)
self.client = make_better(self.service_name, self.client)
return self.client
def __exit__(self, *args, **kwargs):
self.client = None


Lean angle must be within the range 0-120.
Lean angle > 100 deg - check it again.
If main stem is 2 then crown % must be < 100%.
Crown percentage must be within the range 0-100.
Tree % cannot be less then crown %.
Tree % cannot be less than 100% if Main_Stem=2 (broken).
If tree % < 100 then Main_Stem must be 2 or 3 (broken).
Tree percentage must be within the range 0-100.
Ground percentage must be within the range -1 - 100.
Support percentage must be within the range -1 - 100.


The App object manages shared resources (such as images
and sounds) and redirects the GUI events to its
current Scene.
A Scene object responds to
each event and interacts with the user. A game can have
multiple Scenes (such as a title and cutscene screen) and
there's always only one that is active.
Each Scene can
contain zero or more Layers. This is
where Tasks, Sprites
and Entitys reside. Each of these objects can run
independently and can be created or destroyed as
needed.
A Task is just an independent process
whose update() method is called at every frame. A
new Task can be created and added to a Layer
at any time. When a Task dies because it is terminated externally or
its speficied lifetime is passed, it is automatically removed from
its Layer.
A Sprite is a visible
Task. It has a position and its own rendering function
render() and can be shown at a screen.
An Entity is a Sprite that has a
physical extent. In Euskit, pure Sprites are mostly used
as particles or text labels, and Entitys are often used
as a player or other characters in a game.
let entity = new Entity(pos);
layer.addObject(entity);
entity.collider = new Rect(-5,-5,10,10);
entity.collider = new Circle(0,0,5);
entity.isMovable(v);
entity.getMove(pos, v, force);
entity.moveIfPossible(v, force);
class Player extends Entity {
collidedWith(entity: Entity) {
if (entity instanceof Enemy) {
this.die();
}
}
}


This is a wearable maze game where the player tilts their head according to directions given by another player, with the goal of getting a ball through the maze.
The second player, not wearing the maze first drops the ball into the top of the maze. Both of the players goal is to manuver the ball so that it falls out the back of the pyramid. The front of the pyramid includes a space for the user to attach their phone and try to solve the phone maze at the same time as trying to solve the pyramid maze. The hat is Illuminati themed because of the way two people interact with each other while playing: The person inside the hat has to follow every step the person on the outside tells them to do, with no freewill for themselves. They must trust that their partner outside is telling them the right moves in order to beat the game.


This is an external application that needs to add two 3x3 matrices. It has no prior knowledge of any other application that can offer such a feature.


Home
Bus
Create
Delete
Update
Route
Create
Delete
Update
Schedule
Create
Delete
Update
Bus Update
Please enter the busid you want to update
Busid:
Go back


|
Memory-mapped files stream
Provide read-write access to memory-mapped files on Windows and POSIX systems.
|
File mapping is the association of a file's contents with a portion of the virtual address space of a process. The system creates a file mapping object (also known as a section object) to maintain this association. A file view is the portion of virtual address space that a process uses to access the file's contents. File mapping allows the process to use both random input and output (I/O) and sequential I/O. It also allows the process to work efficiently with a large data file, such as a database, without having to map the whole file into memory. Multiple processes can also use memory-mapped files to share data.
Processes read from and write to the file view using pointers, just as they would with dynamically allocated memory. The use of file mapping improves efficiency because the file resides on disk, but the file view resides in memory.
The following illustration shows the relationship between the file on disk, a file mapping object, and a file view.
The file on disk can be any file that you want to map into memory, or it can be the system page file. The file mapping object can consist of all or only part of the file. It is backed by the file on disk. This means that when the system swaps out pages of the file mapping object, any changes made to the file mapping object are written to the file. When the pages of the file mapping object are swapped back in, they are restored from the file.
A file view can consist of all or only part of the file mapping object. A process manipulates the file through the file views. A process can create multiple views for a file mapping object. The file views created by each process reside in the virtual address space of that process. When the process needs data from a portion of the file other than what is in the current file view, it can unmap the current file view, then create a new file view.
When multiple processes use the same file mapping object to create views for a local file, the data is coherent. That is, the views contain identical copies of the file on disk.
fmstream provides a high-level interface to read/write data from/to memory-mapped files as input/output streams. It interfaces a file-based streambuffer (filemappingbuf) with the high-level interface of std::iostream. fmstream can be used in place of std::fstream or std::ofstream.
ifmstream provides a high-level interface to read data from memory-mapped files as input streams. It interfaces a file-based streambuffer (filemappingbuf) with the high-level interface of std::istream. ifmstream can be used in place of std::ifstream.
Download source code on SourceForge : http://sourceforge.net/projects/fmstream/
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.


Leicester will not sell players in January: Rodgers
by ReutersLONDON: Leicester City manager Brendan Rodgers says they will not be selling players in the January transfer window but could make additions if an opportunity to improve the squad comes their way.
Rodgers, who took charge in February, has guided Leicester to second in the Premier League on 26 points from 12 games this season, eight points behind leaders Liverpool.
The club sold centre back Harry Maguire to Manchester United for around 80 million pounds ($103.30 million) in the close season and Rodgers does not want to lose any more of his core group as they seek a return to the Champions League.
“We have absolutely no plans to lose anyone in January,” Rodgers told reporters. “Our job is to keep what we have.
“If there’s an opportunity to improve the squad we will look to do that. If not, we will carry on with the squad we have, which is very strong.”
British media have linked 22-year-old midfielder James Maddison to United since his breakthrough campaign last season and his stock has risen since he was called up by England manager Gareth Southgate during the recent international break.
Maddison, who had seven goals and seven assists last season, has already scored four times and notched two assists in 11 league games in the current campaign.
“He has shown he is a big-game player,” Rodgers said. “He has scored goals against Liverpool, against Tottenham, against Arsenal (this season).
“James is among a lot of very good players with England and it’s just about preference, whether Gareth and his staff want to pick him. You can see he is at the level.
“If not, he will continue to work. He doesn’t expect to play, he’s not entitled, but he loves playing and will hopefully get many more caps for his country.”


Type in the boxes then click submit to activate your Spark Core or Photon.
Device Core ID:
Get this from the core area when logged in
Access Token :
Get this from the settings area when logged into http://spark.io
Click this first
Enter the value of your Spark Cores IP Address here: Port = 80
Then click the buttons or mouse over the buttons and watch th D7 LED on your core
.....


I'm very active with different projects related to nomadism and free-economics. My goal is to make websites that allow people spend less time online and more time meeting each other. I'm visioning a world with trust, shared resources and sustainable living.
I'm active open source contributor. For instance Mean.js and Agenda.


Rafael Rodriguez
E-Mail Sent!
Your message has been sent, I'll reply as soon as I can, meanwhile you can go back to the
main page.


Datafiles
predicsis.jsSDK.models
| get /data_files | Datafiles.all() | |
| get /data_files/:id | Datafiles.get() | |
| get /data_files/:id/signed_url | Datafiles.getSignedUrl() | |
| get /data_files/:id/signed_url | Datafiles.download() | |
| Official documentation is available at https://developer.predicsis.com/doc/v1/data_management/data_file/ |
Output example of a data_file:
{ data_file: { id: "54edf76c6170700001870000", filename: "hello.csv", type: "S3", size: 24, signed_url: { links: { self: "https://api.predicsis.com/data_files/53c7e7b668796493d3010000/signed_url" } } } }
Output example of a signed_url:
{ data_file: { signed_url: "http://prod.kml-api.s3-us-west-2.amazonaws.com/uploads/5347b31750432d45a5020000/sources/1415101671848/source.csv" } }


§235-24 Specified nonbusiness income. Rents and royalties from real or tangible personal property, capital gains, interest, dividends, or patent or copyright royalties, to the extent that they constitute nonbusiness income, shall be allocated as provided in sections 235-25 to 235-27. [L 1967, c 33, pt of §1; HRS §235-24]


org.pb.x12
Class X12Parser
java.lang.Object
org.pb.x12.X12Parser
- All Implemented Interfaces:
- Parser
public class X12Parser
- extends java.lang.Object
- implements Parser
The class represents methods used to translate a X12 transaction represented
as a file or string into an X12 object.
- Author:
- Prasad Balan
|
Method Summary |
EDI |
parse(java.io.File fileName)
The method takes a X12 file and converts it into a X2 object. |
EDI |
parse(java.io.InputStream source)
The method takes a InputStream and converts it into a X2 object. |
EDI |
parse(java.lang.String source)
The method takes a X12 string and converts it into a X2 object. |
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
POS_SEGMENT
public static final int POS_SEGMENT
- See Also:
- Constant Field Values
POS_ELEMENT
public static final int POS_ELEMENT
- See Also:
- Constant Field Values
POS_COMPOSITE_ELEMENT
public static final int POS_COMPOSITE_ELEMENT
- See Also:
- Constant Field Values
X12Parser
public X12Parser(Cf cf)
parse
public EDI parse(java.io.File fileName)
throws FormatException,
java.io.IOException
- The method takes a X12 file and converts it into a X2 object. The X12
class has methods to convert it into XML format as well as methods to
modify the contents.
- Specified by:
parse
in interface Parser
- Parameters:
fileName
- a X12 file
- Returns:
- the X12 object
- Throws:
FormatException
java.io.IOException
parse
public EDI parse(java.io.InputStream source)
throws FormatException,
java.io.IOException
- The method takes a InputStream and converts it into a X2 object. The X12
class has methods to convert it into XML format as well as methods to
modify the contents.
- Specified by:
parse
in interface Parser
- Parameters:
source
- InputStream
- Returns:
- the X12 object
- Throws:
FormatException
java.io.IOException
parse
public EDI parse(java.lang.String source)
throws FormatException
- The method takes a X12 string and converts it into a X2 object. The X12
class has methods to convert it into XML format as well as methods to
modify the contents.
- Specified by:
parse
in interface Parser
- Parameters:
source
- String
- Returns:
- the X12 object
- Throws:
FormatException
java.io.IOException


§604-17 Courts of record; testimony and proceedings. The district courts shall be courts of record. The district courts shall in all cases preserve in writing, on tape, or such other mechanical device as may be appropriate, the minutes, proceedings, and testimony of their trials, transactions, and judgments, and the facts upon which their decisions rest. [L 1892, c 57, §13; RL 1925, §2285; am L 1927, c 71, §1; RL 1935, §3774; RL 1945, §9684; RL 1955, §216-15; HRS §604-17; am L 1970, c 188, §22; am L 1971, c 144, §6E]
Rules of Court
Transcript of proceedings, see DCRCP rule 80(c).


dijit/layout/BorderContainer tests
Headline layout (default), left is constrained - min:150, max:250
top bar (resizable)
left (resizable b/w 150 → 250)
main panel with
a link.
(to check we're copying children around properly).
Here's some text that comes AFTER the combo box.
right (fixed size)
bottom bar (resizable)
Sidebar layout, BiDi sensitive, liveSplitters: false
leading (fixed size)
top bar (fixed size)
main panel with
a link.
(to check we're copying children around properly).
Here's some text that comes AFTER the combo box.
bottom bar (resizable)
trailing (resizable)
gutters=false layout
leading
top bar
main panel with
a link.
(to check we're copying children around properly).
Here's some text that comes AFTER the combo box.
bottom bar
trailing
Vertical panels only with splitters
top bar
main panel with
a link.
(to check we're copying children around properly).
Here's some text that comes AFTER the combo box.
bottom bar
More fun with layouts
Watching the splitter events
Splitter coords output:
nothing moving
dijit/layout/BorderContainer programmatic test


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[CHAPTER 390]
CHILD LABOR LAW
Section
390-1 Definitions
390-2 Employment of minors under eighteen years of age
390-3 Certificates of employment and age
390-4 Suspension, revocation, invalidation of certificate
390-5 Exceptions
390-6 Rules
390-7 Child labor crime


Webservices API styles
28 Dec 2014- Distinct APIs
- Attend multiple client types
- API reflecting client needs (top-down)
- External vs Internal API
Design considerations
Encapsulation
- Services should hide implementation details
- Prevents coupling the clients
- Service design should avoid exposing its internals
- "Bottom-up" design if making domain entities accessible to external clients
- Indirection enables internals evolve or even to be replaced
Service contract
- Agreement on how clients and services may interact
- Pre / Post conditions indicating the client expectations
Autonomy
- Dependencies on outside forces
- External entities, services, etc
- Distributed transactions
Latency
- Serialization / Deserialization cost
- Network
- Payload size
- Few "chunky" messages are normally better than "chatty conversation" (several smaller messages)
Partial failures
- Failure scenarios handling
- "Clients need to be prepared to service failures and vice-versa"
Binary message encoding of text-based data
- Information exchanged is generally text-based
- Text information may be encoded as binary data before being serialized
- Compression
RPC API
How can clients execute remote procedures over HTTP?
- Not easy (low-level communication protocols)
- Different platforms use different encodings
- Remoting technologies (CORBA, DCOM) made it easier to share and use remote procedures
- Request: Procedure(i.e. service) name + arguments
- Response: Procedure results
- Service frameworks: JAX-WS & WCF for abstracting structural formats (JSON / XML), encoding, generating XSD, etc
Considerations
- Flat APIs inclination: Tendency to create service signatures just as class method signatures
- Breaking changes: Argument order - Add, remove and reorder breaks the client
- Single-Message argument pattern to resolve the issue (+ flexibility)
- Proxies and service descriptors: Clients applications typically use proxies to connect to services on this style. Typically reads service descriptors (e.g. WSDL)
- Location transparency
- Async: RPC APIs often use request-acknowlege instead of request-response interaction pattern.
- Avoid blocking: Clients can use an async response handler pattern to avoid being blocked during the service processment
Message API
How can clients send commands, notifications or other information to remote systems over HTTP while avoiding direct coupling to remote procedures
- Signature changes may be breaking
- Determines the procedure based on message content
- Style is normally used when external entities drive the requirements definition
- Messages are typycally defined using XML Schema language, however messages can be defined through other means (e.g. Google protocol buffers / ProtoBuff).
- API provides endpoints
- Messages are generically of three types: Command messages (triggers a specific task on the receiver) Event messages (notify) or Document messages (like business documents).
- WSDL as service descriptor and for specifications (WS-Policy, WS-Security, etc)
Considerations
- Service descriptors and connectors: Message APIs often employ a service descriptor (most common is WSDL) to aid in client-side connectors. Operations are grouped into an abstract type (an interface on WSDL 2.0)
- Delegation of work: The logic used to process specific message types is often triggered by a command invoker - when the logic is sufficiently complicated. An alternative is to use a workflow connector - when triggering a complex, long-running job.
- Async: Message APIs often use request-acknowlege instead of request-response interaction pattern.
- Avoid blocking: Clients can use an async response handler pattern to avoid being blocked during the service processment
- Late binding: Often provide addresses to related services clients may use
Resource API
A client application that consumes or manipulates text, images, documents or other media files managed by a remote system.
How can a client manipulate data managed by remote system, avoid direct coupling to remote procedures, and minimize the need for domain-specific API
- Provides access to resources (media file, collection of related data, database information, a logical transaction, etc)
- Often adhere to REST
- Since each URI refers to a single resource / collection, services can often be added, changed or removed with minimal impact
- Contracts are composed of: protocol, media types produced or consumed by the service, status codes, and URI schemes to identify the resources.
Considerations
- Use with disparate clients: Good choice when having a wide mix of clients
- Addressability: Make it easy to save and share links to services
- Code generation of service connectors: Client developers can't take advantage of code generation tools (no service descriptors).
- Async: Resource APIs often use request-response, but can use request-acknowlege interaction pattern as well.
- Avoid blocking: Clients can use an async response handler pattern to avoid being blocked during the service processment
- Support to client preferences: Supports multiple representation of the same resource. Rather than a different URI, you can use media type negotiation to enable clients to indicate their preferences.
- Late binding: Often provide addresses to related services clients may use
- Ability to leverage commodity caching technologies
- Resources API and REST: Not every Resource API adhere to REST, but they often do.


Preparation time
30 mins to 1 hour
Cooking time
30 mins to 1 hour
Serves
4
Creamy and smoky, this classic Scottish fish soup is great for cold winter nights.
For the fish stock, put the oil into a heavy saucepan and heat; then add the vegetables and stir well to coat with the oil. Cover and sweat over a low heat for about ten minutes; do not allow them to colour. Take the lid off the pan and pour in the white wine. Boil for one minute. Now add 1.2 litres/2 pints of water and the smokies. Bring up almost to the boil; then skim well. Reduce the heat and simmer very gently for 20 minutes. Allow to cool. Once cold, strain the stock through a very fine sieve, preferably lined with a double layer of muslin. (The stock can be made two days ahead and kept chilled until needed.)
To make the soup, put a large saucepan over a medium heat. Add the butter and, when melted, add the shallots, leek and garlic. Cover and sweat for 5-10 minutes, but don't allow the vegetables to colour.
Add the cooked potatoes. Flake the smokies and add to the pan. Cover and sweat for a further two minutes, then add 1 litre/1¾ pints of the reserved stock and season with freshly ground black pepper. Bring to the boil and simmer for 8-10 minutes.
Take the pan off the heat and allow to cool slightly. Carefully pour the mixture into a blender and put on the lid.  Blend for a few seconds; then gradually add the milk and cream through the hole. When the milk and cream is all added and the mixture is smooth, pass through a fine sieve to give a fine, velvety texture.
To serve, pour the soup into a clean pan and heat gently. Flake the remaining smokie and divide among four warmed shallow bowls. Ladle in the soup and garnish with chopped chives.


Gump keeps backgrounded apps running amid device resprings
by Anthony BouchardPerhaps one of the most bothersome parts about respringing your jailbroken handset is that when you have music playing in the background, the respring interrupts it, forcing you to manually tap on the ‘Play’ button to continue music playback.
Those who’ve been plagued by this nuisance for as long as I have will be thrilled to learn about the launch of a new jailbreak tweak called Gump by iOS developer Kurrt, as it effectively keeps backgrounded apps and music running even throughout the course of a standard respring.
The concept behind Gump isn’t new, as we’ve seen similar functionality in tweaks like Don’t Stop the Party!, but Gump’s implementation is slightly more versatile. Gump plays nicely with just about any music playback application, and according to the developer, it should also work with other apps that perform background processes.
Once installed, Gump adds a preference pane to the Settings app where you can configure it to your liking:
Here, you can:
- Toggle Gump on or off on demand
- Enable or disable Now Playing music playback during a respring
- Choose which apps will continue running in the background during a respring
- Force-restart all services
Apart from manually configuring which apps you’d like to exempt from being restarted during the course of a respring, this works most of its magic automatically in the background.
It’s worth noting that while Gump is a free download from the BigBoss repository, users will need to purchase the tweak from the preference pane for $1.99 before it can be used. Gump supports all jailbroken iOS 12 and 13 devices.
Will you be installing Gump on your pwned handset? Tell us why or why not in the comments section.


Build applications decoupled from data, and avoid maintaining a separate back-end and what comes with it (logging, monitoring...)
Access the data you need straight from the user, without depending on any API provider-specific feature.
Reuse other apps' data, and let other reuse yours.
Be empowered to read and improve the specification: the Solid standards are open, as well as some of their implementations.


[1]
room in the newer guesthouses when i was a corner king room in the newer guesthouses .
[2]
the room was quiet and i was n't going to spend all day in the room .


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Comment on pitch


TFW you fix a bug you'd been kinda mostly ignoring for ages because it looked gnarly... and it wasn't that bad! 😤
5/6/2017, 9:54:09 PM
Favs: 3
Retweets: 0
link--- layout: tweet ---
TFW you fix a bug you'd been kinda mostly ignoring for ages because it looked gnarly... and it wasn't that bad! 😤
5/6/2017, 9:54:09 PM
Favs: 3
Retweets: 0
link