Working on bi-dimensional cellular automata is not surprising if the underlying representation of the set \(Q\) of substates is bi-dimensional as well (see listing [code:memoryPattern2D]) and hence implemented as:
// Substates
double **Sz; //Altitude
double **Sh; //Lava thickness
double **nSh;
double **ST; //Lava temperature
double **nST;
double **SEk; //Kinetic energy
double **nSEk;
a series of double canonical dynamical C/C++ matrixes for each substate (we’ll see in section [1Dto2Dmemory] that this memory setting is problematic in CUDA), one for reading cell neighbor ( updatedCA) substates and a second for writing the new substate values (currentCA).
Parameters are organized as a series of scalar variables:
double Prho; //density
double Pepsilon; //emissivity
double Psigma; //Stephen-Boltzmann constant
double Pcv; //Specific heat
while an abstraction of the vent is given by the class TVent
that encapsulates the class TEmissionRate
which store the
values, variables and methods to represent a particular vent emission
rate. The main function of the vent abstraction class is to give back
an amount of produced lava after a certain amount of simulated
event’s time. The emission of lava from the vent was discretized into
time slot of
\(t_e\) and it means that when the simulation time
\(t_s\) is such that
\(i t_e \leq t_s < (i+1) t_e \) the emitted lava thickness value at the
slot number
\(i\) is returned.
double thickness(double sim_elapsed_time, double Pt,unsigned int emission_time,
double Pac) {
unsigned int i = (unsigned int)(sim_elapsed_time/emission_time);
if (i >= _emission_rate.size())
return 0;
else
return _emission_rate[i]/Pac*Pt;
}


class: center, middle, inverse, title-slide # Linear Optimization: Model Building and Fitting ### David T. Frazier ### 19/09/2017 --- # Outline - In general, optimization is a tool for decision making. - Tells us the optimal input to our objective function `\(f(b)\)` - So far, only considered `\(f(b)\)` that is not linear in `\(b\)`. - What about `\(f(b)\)` linear in `\(b\)`? - EX: `\(f(b) = b_1x_1 + b_2x_2-b_3x_3\)` - What if there are restrictions on `\(x_1,x_2,x_3\)`? - Can we just apply the same tools? --- # Linear Optimization Methods - A linear optimization problem depends on - decision variables, `\(x_1,x_2,...,x_n\)` - objective function `\(f=b_1x_1+b_2x_2+...+b_nx_n\)` - known `\(b_1,...,b_n\)` - linear constraints: restrictions on `\(x_1,...,x_n\)`. - Specific case of optimization where `\(f\)` is linear. --- # Linear Optimization Methods - Allow us to deal with cases where - `\(f(b)\)` linear in `\(b\)`? - there are restrictions on `\(x_1,x_2,x_3\)`. - Solve real world problems. - AIM: minimize or maximize some objective function `\(f\)` (**same as before**) - Subject to restrictions, called **constraints**, on `\(x_1,x_2,x_3\)`. - Linearity of `\(f(b)\)` in `\(b\)`. --- # Linear optimization Structure - `\(\max_{x_1,...,x_n}b_1x_1+b_2x_2+...+b_nx_n\)` subject to - `\(a_{11}x_{1}+...+a_{1n}x_{n}\leq RHS_{1}\)` - `\(\cdots\)` - `\(a_{m1}x_{1}+...+a_{mn}x_{n}\leq RHS_{n}\)` - `\(x_{1},...,x_{n}\geq0\)` --- # Example A farmer has 240 acres of land to plant. She needs to decides how many acres of corn to plant and how many acres of oats. She can make **$40 per acre profit for corn** and **$30 per acre for oats**. However, the *corn takes 2 hours of labour per acre to harvest* and the *oats take only 1 hour per acre*. She only has *320 hours of labour* she can invest. **To maximize her profit**, how many acres of each should she plant? - What is the objective function? - What are the constraints? --- # From words to math - Decision variables: corn `\(x\)`, oats `\(y\)` (in acers) - Obj. Fun. - Profit: `\(\Pi=40x+30y\)` - Constraints? - `\(2x+y\leq 320\)` - `\(x+y\leq 240\)` - Mathematically: `$$\max_{x,y}\Pi\;s.t.\;2 x+y\leq320;\;x+y\leq240.$$` --- # Modelling assumptions in linear programming - **Proportionality**: the contribution to the objective function and the left-hand-side of each constraint by each decision variable is proportional to the value of the decision variable. - **Additivity**: the contribution to the objective function and the left-hand-side of each constraint by any decision variable is completely independent of any other decision variable and is additive (involving addition). - **Divisibility**: the quantities represented by each decision variable are innately divisible (i.e., fractional answers make sense). - **Certainty**: the coefficients in the objective function and constraints are known with certainty. --- # Example: Par, INC. - The PAR, Inc company produces golf bags. - The managers are interested in expand production and also want to move to **medium** & **high** price bags. - Bag production involves 4 steps: - Cutting and dyeing the material - Sewing - Finishing - Inspection & packaging --- # Example: Par, INC. - The director of manufacturing estimated that each of the two options will result in the following number of hours at each department: - Cutting and dyeing the material: Standard Bag: 7/10, Delux Bag: 1 - Sewing: Standard Bag: 1/2, Delux Bag: 5/6 - Finishing: Standard Bag: 1, Delux Bag: 2/3 - Inspection & packaging: Standard Bag: 1/10, Deluxe Bag: 1/4 --- # Example: Par, INC. - After studying each department, the director estimates the following production capacities: - 630 hours of cutting and dyeing, - 600 hours of sewing, - 708 hours of finishing, and - 135 hours for inspection and packaging - The accounting manager estimates that the company's profit from a standard bag and a deluxe bag will be **$10** and **$9**, respectively. --- # Example: Par, INC. - Decision Variables? - Objective function? - Constraints? --- # Example: Par, INC. - Decision Variables? - Number of bags of each type: - `\(S\)` = standard bags - `\(D\)` = delux bags --- # Example: Par, INC. - Objective function? - Maximize profit: `\(max_{S,D}10S+9D\)` --- # Example: Par, INC. - Constraints? - Cutting and dyeing the material: Standard Bag: 7/10, Delux Bag: 1; 630 hours; `\((7/10)S+D\leq 630\)` - Sewing: Standard Bag: 1/2, Delux Bag: 5/6; 600 hours; `\((1/2)S+(5/6)D\leq 600\)` - Finishing: Standard Bag: 1, Delux Bag: 2/3; 708 hours; `\(S+(2/3)D\leq 708\)` - Inspection & packaging: Standard Bag: 1/10, Delux Bag: 1/4; 135 hours; `\((1/10)S+(1/4)D\leq 135\)` - Number of bags can't be negative!! `\(S,D\geq0\)`. --- # Example: Par, INC. - ![](par_inc_1.jpg) --- # Example: Par, INC. - ![](par_inc_2.jpg) --- # Example: Par, INC. - ![](par_inc_3.jpg) --- # Example: Par, INC. - ![](par_inc_4.jpg) --- # Example: Par, INC. - ![](par_inc_5.jpg) --- # Example: Par, INC. - ![](par_inc_6.jpg) --- # Example: Par, INC. - ![](par_inc_7.jpg) --- # Example: Par, INC. - ![](par_inc_8.jpg) --- # How to solve this in `R`? - Just like `optim`, we need some numerical method to solve this optimization problem - We'll make use of the `lpSolve` package in `R`. - "Modeling and Solving Linear Programming with `R`." - Similar to `optim` function - Takes function as input - initial values for parameters - Main difference: Must now add in constraint information. --- # Constraints in `lpSolve` - Constraint information must be entered in a particular way - Three pieces of information - Constraint coefficients - Constraint direction - RHS value of the constraint - Must be entered as a matrix - Each row of the matrix corresponds to a constraint - Include inequality information in a separate line --- # Example: Par, INC - Easiest to see from an example. ```r library(lpSolve) obj_fun <- c(10,9) constr <- matrix(c(7/10,1,1/2,5/6,1,2/3,1/10,1/4),ncol = 2, byrow=TRUE) const_dir <- c("<=","<=","<=","<=") RHS <- c(630,600,708,135) prod_sol <- lp("max",obj_fun,constr,const_dir,RHS) prod_sol$solution ``` ``` ## [1] 540 252 ``` --- # Example: Solve Farm Prob. - Problem: `$$\max_{x,y}40x+30y,\;s.t.\;x+y\leq320;\;x+y\leq240.$$` ```r library(lpSolve) obj_fun <- c(40,30) constr <- matrix(c(2,1,1,1),ncol = 2, byrow=TRUE) const_dir <- c("<=","<=") RHS <- c(320,240) prod_sol <- lp("max",obj_fun,constr,const_dir,RHS) prod_sol$solution ``` ``` ## [1] 80 160 ``` --- # Example - A small business sells two products, named Product 1 and Product 2. Each tonne of Product 1 consumes 30 working hours, and each tonne of Product 2 consumes 20 working hours. The business has a maximum of 2,700 working hours for the period considered. As for machine hours, each tonne of Products 1 and 2 consumes 5 and 10 machine hours, respectively. There are 850 machine hours available. - Each tonne of Product 1 yields 20 Me of profit, while Product 2 yields 60 Me for each tonne sold. For technical reasons, the firm must produce a minimum of 95 tonnes in total between both products. We need to know how many tonnes of Product 1 and 2 must be produced to maximize total profit. --- # Example: Objective function? - A small business sells two products, named Product 1 and Product 2. Each tonne of Product 1 consumes 30 working hours, and each tonne of Product 2 consumes 20 working hours. The business has a maximum of 2,700 working hours for the period considered. As for machine hours, each tonne of Products 1 and 2 consumes 5 and 10 machine hours, respectively. There are 850 machine hours available. - Each tonne of Product 1 yields **20 Millon of profit**, while Product 2 yields **60 Millon** for each tonne sold. For technical reasons, the firm must produce a minimum of 95 tonnes in total between both products. We need to know how many tonnes of Product 1 and 2 must be produced to **maximize total profit**. --- # Example: Objective function? - Mathematically: `$$\max_{p_1,p_2}20p_1+60p_2$$` --- # Example: Constraints? - A small business sells two products, named Product 1 and Product 2. Each tonne of **Product 1 consumes 30 working hours**, and each tonne of **Product 2 consumes 20 working hours**. The business has a maximum of **2,700 working hours** for the period considered. As for machine hours, each tonne of **Products 1 and 2 consumes 5 and 10 machine hours**, respectively. There are **850** machine hours available. - Each tonne of Product 1 yields 20 Millon of profit, while Product 2 yields 60 Millon for each tonne sold. For technical reasons, the firm must produce **a minimum of 95 tonnes in total** between both products. We need to know how many tonnes of Product 1 and 2 must be produced to maximize total profit. --- # Example: Constraints? - Mathematically: - `\(30p_1+20p_2\leq 2700\)` - `\(5p_1+10p_2\leq850\)` - `\(p_1+p_2\geq 95\)` - `\(p_1,p_2\geq0\)`. --- # Example: Solution? ```r library(lpSolve) obj_fun <- c(20,60) constr <- matrix(c(30,20,5,10,1,1),ncol = 2, byrow=TRUE) const_dir <- c("<=","<=",">=") RHS <- c(2700,850,95) prod_sol <- lp("max",obj_fun,constr,const_dir,RHS) prod_sol$solution ``` ``` ## [1] 20 75 ``` --- # Additional Examples - Portfolio selection - Transportation planning - Advertising campaign planning --- # Example: Portfolio Selection - Select a specific investment strategy - Variety of investment alternatives - Goal: maximize profit or minimize risk. --- # Example: Portfolio Selection - Investment opportunity for a fund of $100,000. - The company has been recommended to invest in the oil industry, steel industry or government bonds. - Five investment opportunities: Atlantic Oil (7.3% return), Pacific Oil (10.3 %), Midwest Steel (6.4%), Huber Steel (7.5%) and Government bonds (4.5%). --- # Example: Portfolio Selection - Investment guidelines: - Neither oil or steel industry should receive more than $50,000. - Amount invested in government bonds should be at least 25 percent of the steel industry investments. - The investment in Pacific Oil, the high-return but high-risk investment, cannot be more than 60 percent of the total oil industry investment. --- # Example: Portfolio Selection - Define the following decision variables: - X1 - Atlantic Oil - X2 - Pacific Oil - X3 - Midwest Steel - X4 - Huber Steel - X5 - Government bonds --- # Example: Portfolio Selection - Objective function? --- # Example: Portfolio Selection - Objective function? - `\(\max_{x_{1},...,x_{5}}.073x_{1}+.103x_{2}+.064x_{3}+.075x_{4}+.045x_{5}\)` --- # Example: Portfolio Selection - Constraints? --- # Example: Portfolio Selection - Constraints? - Max fund 10000 - `\(x_{1}+x_{2}+...+x_{5}\leq 100,00\)` - Oil or steel no more than $50,000. - `\(x_{1}+x_{2}\leq 50,000\)` - `\(x_{3}+x_{4}\leq 50,000\)` - Government bonds at least 25 percent of the steel industry investments. - `\(x_{5}\geq .25*(x_{3}+x_{4})\)` - Pacific Oil investment, no more than 60 percent of the total oil industry investment. - `\(x_{2}\leq .6*(x_{1}+x_{2})\)` - `\(x_{1},...,x_{5}\geq0\)` --- # Putting it together - `\(\max_{x_{1},...,x_{5}}.073x_{1}+.103x_{2}+.064x_{3}+.075x_{4}+.045x_{5}\)` - `\(x_{1}+x_{2}+...+x_{5}\leq 100,00\)` - `\(x_{1}+x_{2}\leq 50,000\)` - `\(x_{3}+x_{4}\leq 50,000\)` - `\(x_{5}\geq .25*(x_{3}+x_{4})\)` - `\(x_{2}\leq .6*(x_{1}+x_{2})\)` - `\(x_{1},...,x_{5}\geq0\)` --- # Integer Optimization - So far, assumed `\(x_{1},...,x_{n}\)` infinitely divisible - i.e., continuous. - Not true in many cases - Can we sell partial apartments? - - **Integer linear programs**: Problems that are modeled as linear programs with the additional requirement that at least one variable must be integer. --- # Integer Programming and Optimization - Types of Integer Linear Optimization Models: - All-integer linear program - Mixed-integer linear program - Binary integer linear program --- # Linear optimization Structure - `\(\max_{x_1,...,x_n}b_1x_1+b_2x_2+...+b_nx_n\)` subject to - `\(a_{11}x_{1}+...+a_{1n}x_{n}\leq RHS_{1}\)` - `\(\cdots\)` - `\(a_{m1}x_{1}+...+a_{mn}x_{n}\leq RHS_{n}\)` - `\(x_{1},...,x_{n}\geq0\)`; `\(x_{j}=0,1,2,...\)` all `\(j=1,...,n\)` - `\(x_{1},...,x_{n}\geq0\)`; `\(x_{j}=0,1,2,...\)`; `\(x_{i}\)`, `\(i\neq j\)`, continuous - `\(x_{1},...,x_{n}\geq0\)`; `\(x_{j}=0,1\)` for all `\(j=1,...,n\)` --- # Linear optimization Structure - Using `R`, can solve nearly exactly the same way - All-integer - `lp("min", f, const.mat, const.dir, const.rhs, all.int=TRUE)` - All-binary - `lp("min", f, const.mat, const.dir, const.rhs, all.bin=FALSE)` - Mixed - `lp("min", f, const.mat, const.dir, const.rhs, int.vec)` - Numeric vector giving the indices of variables that are required to be integer. - The length of this vector will therefore be the number of integer variables --- # LP Relaxation - In many cases, we can simply treat the decision variables as continuous - Then solve the optimization problem, - Round (up or down) to nearest integer. - Called LP relaxation --- # LP Relaxation - Gives a near-optimal solution - Rounding may not always be a good strategy. - If decision variables take on small values - Can have major impact on the value of the objective function or feasibility, - In such cases, optimal integer solution is needed. --- # All Integer Example: Eastborne Realty - Eastborne Realty has $2 million available for the purchase of new rental property. - After an initial screening, Eastborne reduced the investment alternatives to townhouses and apartment buildings. - Each townhouse can be purchased for $282,000. - Five are available. - Each apartment building can be purchased for $400,000. - The developer will construct as many buildings as Eastborne wants to purchase. --- # All Integer Example: Eastborne Realty - Eastborne Realty has $2 million available for the purchase of new rental property. - After an initial screening, Eastborne reduced the investment alternatives to townhouses and apartment buildings. - `\(T\)`- townhouse, `\(A\)`- apartment. - Each townhouse can be purchased for 282,000 - Five available: `\(T\leq 5\)` - Each apartment can be purchased for 400,000. Develop construct as many as desired. - `\(282T+400A\leq 2000\)` --- # All Integer Example: Eastborne Realty - Property manager can devote 140 hours per month to properties. - Each townhouse requires 4 hours per month. - Each apartment requires 40 hours per month. --- # All Integer Example: Eastborne Realty - Property manager can devote 140 hours per month to properties. - Each townhouse requires 4 hours per month. - Each apartment requires 40 hours per month. - `\(4T+40A\leq 140\)` --- # All Integer Example: Eastborne Realty - Annual cash flow (After deducting mortgage payments and operating expenses) estimated to be: - $10,000 per townhouse. - $15,000 per apartment building. - The objective: Determine the number of townhouses and the number of apartment buildings to purchase to maximize annual cash flow. --- # All Integer Example: Eastborne Realty - Defining the decision variables: - `\(T\)` = number of townhouses - `\(A\)` = number of apartment buildings - Objective function for cash flow (in thousands): - `\(max_{T,A}10T+15A\)` - Constraints? - Available funds: `\(282T+400A\leq 2000\)` - Management: `\(4T+40A\leq 140\)` - Available funds: `\(282T+400A\leq 2000\)` - Available Townhouses: `\(T\leq 5\)` - Non-negative: `\(T,A\geq0\)` - Integer: `\(T,A\)` integers --- # Example: Eastborne Realty - ![LP Relax](lp_relax.png) --- # Example: Eastborne Realty ```r library(lpSolve) obj_fun <- c(10,15) constr <- matrix(c(282,400,4,40,1,0),ncol = 2, byrow=TRUE) const_dir <- c("<=","<=","<=") RHS <- c(2000,140,5) # LP relax prod_sol <- lp("max",obj_fun,constr,const_dir,RHS) prod_sol$solution ``` ``` ## [1] 2.479339 3.252066 ``` ```r # Exact relax prod_sol <- lp("max",obj_fun,constr,const_dir,RHS,all.int=TRUE) prod_sol$solution ``` ``` ## [1] 4 2 ``` --- # A note on LP - Rounding is a trial-and-error approach. - Each rounded solution must be evaluated for feasibility as well as for its impact on the value of the objective function. - Even when a rounded solution is feasible, there is no guarantee that the optimal integer solution has been found. --- # Example: Eastborne Realty - What - What is the true feasible region for the Eastborne Realty problem? - As shown in the following plot, the feasible region is the set of integer points that lie within the feasible region of the LP Relaxation. - There are 20 such feasible solutions (designated by blue dots in the figure). - The region bounded by the dashed lines is the feasible integer solutions. --- # Example: Eastborne Realty - ![LP Relax](lp_relax2.png) --- # Binary Optimization Example - Binary variables - `\(x_{j}=1\)` if activity *IS* undertaken - `\(x_{j}=0\)` if activity *NOT* undertaken - Many Examples: - Capital budgeting - fixed cost - area covering problems --- # Capital budgeting - The Ice-Cold Refrigerator Company is considering investing in several projects that have varying capital requirements over the next four years. - Faced with limited capital each year, management would like to select the most profitable projects that it can afford. - Estimated net present value for each project, the capital requirement and the available capital over 4-year period are: --- # Capital budgeting Estimated net present value for each project, the capital requirement and the available capital over 4-year period are: ![Data](binary1.jpg) --- # Capital budgeting - Capital budgeting problem: the objective function is to maximize the net present value of the capital budgeting projects. - Decision variables - `\(P=1\)` plant expansion - `\(W=1\)` warehouse expansion - `\(M=1\)` new machinery - `\(R=1\)` new research - Constraints? Objective function? --- # Capital budgeting: Objective Function - Maximize NPV - `\(\max 90P+40W+10M+37R\)` --- # Capital budgeting: Constraints ![Capital Requirements](binary1.jpg) --- # Captial budgeting: Constraints - `\(15P+10W+10M+15R\leq40\)` (Year 1) - `\(20P+15W+00M+10R\leq50\)` (Year 2) - `\(20P+20W+00M+10R\leq20\)` (Year 3) - `\(15P+5W+4M+10R\leq35\)` (Year 4) - `\(P,W,M,R\)` are either `\(0,1\)` --- # Capital budgeting: Putting it all together - `\(\max 90P+40W+10M+37R\)` subject to - `\(15P+10W+10M+15R\leq40\)` - `\(20P+15W+00M+10R\leq50\)` - `\(20P+20W+00M+10R\leq40\)` - `\(15P+5W+4M+10R\leq35\)` - `\(P,W,M,R\)` are either `\(0,1\)` --- # Capital budgeting: Solution ```r library(lpSolve) obj_fun <- c(90,40,10,37) constr <- matrix(c(15,10,10,15,20,15,00,10,20,20,00,10,15,5,4,10),ncol = 4, byrow=TRUE) const_dir <- c("<=","<=","<=","<=") RHS <- c(40,50,40,35) # Solution prod_sol <- lp("max",obj_fun,constr,const_dir,RHS,all.bin=TRUE) prod_sol$solution ``` ``` ## [1] 1 1 1 0 ``` --- # Binary mixed-integer Problems --- # Mixed problems - Many problems require binary variables, integers, and continuous part - Same process, but must specify which variables are which... --- # Fixed-Cost problem - In many applications, the cost of production has two components - A setup cost, which is fixed (hence, fixed cost) - A variable cost, directly related to production - Fixed cost problem: binary mixed-integer programming problem where binary variables represent whether an activity, such as a production run, is undertaken (variable = 1) or not (variable = 0). --- # Fixed-Cost problem - ![Data](binary2.jpg) --- # Fixed-Cost problem: Decision Variables - `\(F\)` tons of fuel additive produced - `\(S\)` tons of solvent produced - `\(C\)` tons of carpet cleaning fluid produced - How to incorporate fixed cost? - `\(SF=1\)` if fuel additive is produced - `\(SS=1\)` if fuel additive is produced - `\(SC=1\)` if fuel additive is produced --- # Fixed-Cost problem: Objective function? - Maximize profits from production - Need to deal with the fixed costs - Revenue - `\(40F+30S+50C\)` - Fixed Costs - `\(200SF+50SS+400SC\)` - Var costs dealt with through material constraints --- # Fixed-Cost problem: Constraints? - `\(.4F+.5S+.6C\leq20\)` - `\(F+0S+0C\leq50SF\)` - `\(0F+.2S+.1C\leq5\)` - `\(0F+S+0C\leq25SS\)` - `\(.6F+.3S+.3C\leq21\)` - `\(0F+0S+C\leq40SC\)` - `\(F,S,C\geq0\)` - `\(SF,SS,SC\)` are `\(0,1\)` --- # Fixed-Cost problem: Putting it together - `\(\max 40F+30S+50C-(200SF+50SS+400SC)\)` subject to - `\(.4F+.5S+.6C\leq20\)` - `\(F+0S+0C\leq50SF\)` - `\(0F+.2S+.1C\leq5\)` - `\(0F+S+0C\leq25SS\)` - `\(.6F+.3S+.3C\leq21\)` - `\(0F+0S+C\leq40SC\)` - `\(F,S,C\geq0\)` - `\(SF,SS,SC\)` are `\(0,1\)` --- # Fixed-Cost: Solution - Need to rewrite some of the constraints - `\(.4F+.5S+.6C+0SF+0SS+0SC\leq20\)` - `\(F+0S+0C+0SS+0SC-50SF\leq0F\)` - `\(0F+.2S+.1C+0SF+0SS+0SC\leq5\)` - `\(0F+S+0C+0SF+0SC-25SS\leq0\)` - `\(.6F+.3S+.3C+0SF+0SS+0SC\leq21\)` - `\(0F+0S+C+0SF+0SS-40SC\leq0\)` --- # Fixed-Cost: Solution ```r library(lpSolve) obj_fun <- c(40,30,50,-200,-50,-400) constr <- matrix(c(.4,.5,.6,0,0,0, 1,0,0,-50,0,0, 0,.2,.1,0,0,0, 0,1,0,0,-25,0, .6,.3,.3,0,0,0, 0,0,1,0,0,-40),ncol = 6, byrow=TRUE) const_dir <- c("<=","<=","<=","<=") RHS <- c(20,0,5,0,21,0) # Solution prod_sol <- lp("max",obj_fun,constr,const_dir,RHS,binary.vec=c(4,5,6)) ``` ``` ## Warning in rbind(const.mat, const.dir.num, const.rhs): number of columns of ## result is not a multiple of vector length (arg 2) ``` ```r prod_sol$solution ``` ``` ## [1] 25 20 0 1 1 0 ``` --- class: inverse middle # Share and share alike <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.


California Drought
recent drought crisis in california
Sept 2, 2014
A quick look at the severity of the recent drought in California
abnormally dry
moderate drought
severe drought
extreme drought
exceptional drought
Source: National Drought Migration Center


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Processing is done inside the computer in an area called CPU. Processing is the conversion of input to output


positioning
Years ago, two smart guys (Al Ries & Jack Trout) wrote a book about positioning. In a world now filled to the ozone with undifferentiated, competing products and services, and endless, fragmented media tactics, their advice is now biblical. We suggest you either practice the art of positioning for preference through differentiation or prepare to drop your margins.
Tenzing has created unique positioning strategies in financial services, business and professional services, technology, healthcare, economic development, consumer products and retail. Oddly enough, though, most of our relationships don't start there. A prospective client typically starts by asking us to make an ad or shoot a film or produce a web site. That reflects the current state of our industry - everyone wants the tactical stuff. We respond with a few questions; like what will make the ad, the film or the web site unique? What's your value proposition? Do your staff know your value proposition? The light comes on, we step back together, we find the position that results in preference - and then we produce the ads, the films and the web sites. Funny how that works (out so well).


POLYHEDRONSLICE intersect a convex polyhedron with a plane. SLICE = polyhedronSlice(NODES, FACES, PLANE) NODES: a Nx3 array FACES: either a cell array or a Nf*3 or Nf*4 array PLANE: a plane representation [x0 y0 z0 dx1 dy1 dz1 dx2 dy2 dz2]. return the intersection polygon of the polyhedra with the plane, in the form of a set of ordered points. Works only for convex polyhedra. Example polyhedronSlice See also polyhedra, clipConvexPolyhedronHP ------ Author: David Legland e-mail<EMAIL_ADDRESS>Created: 2007-09-18, using Matlab <IP_ADDRESS>7 (R2007a) Copyright 2007 INRA - BIA PV Nantes - MIAJ Jouy-en-Josas.


Map Idea: Capital City
Posted: Sun Dec 31, 2006 1:41 am
Concept behind this:
Cities should be race-based.  Each of it's guards would use the skills of the default class of that race (ex: Dreg-fighter, Elf- Ranger, Human- Anything, Scallion- Wizards and Warlocks, Gnome- Clerics
Capital city can follow two paths, which needs to be decided upon.
1.) A ruling race.
2.) A cultural mix
I'm going to go along a cultural mix for this Map Idea.
Town square would be a large circle. A circular pool of water in the middle.
Buildings would surround the town square-- since the town square is, after all, the center of town.
Moat around the town.
Merchants would stand outside their doors, and be within reach of the town square.
A large castle would be in the back, kind of like in terthius. This castle would have multiple levels, and a dungeon beneath it, perhaps a portal to ds in the basement.
Town guard concepts:
Scallions:
Guards: Mystic Staff, Mage Hat, and Mystic Leather. (soa would also make guards tougher)
Captains: Altus Lucrum Staff, Adept Hat, Spirit Leather
General: Ominex Staff, Mystic Hat, Dirus Armor
Gnomes:
Guards: Staff of Almus, Mystic Plate, Mystic Helmet
Captains: Spirit Wand, Spirit Shield, Spirit Helm, Spirit Plate
General: Dirus Armor, Dirus Wand, Shield of Ominex, Omni Helm
Dregs:
Guards: Mystic Plate, Vita Halberd, Mystic Helm
Captains: Spirit Plate, Sword of Blood, Spirit Shield, Spirit Helm
General: Dirus Armor, Dirus Battle Axe, Omni Shield, Omni Helm
Elf:
Guards: Lightning Bow, Mystic Scale, Mystic Helmet
Captains: Mystic Bow, Mystic Bone Mail, Spirit Helmet
General: Dirus Armor, Omni Bow, Omni Helmet
Humans:
Random mix of these
Cities should be race-based.  Each of it's guards would use the skills of the default class of that race (ex: Dreg-fighter, Elf- Ranger, Human- Anything, Scallion- Wizards and Warlocks, Gnome- Clerics
Capital city can follow two paths, which needs to be decided upon.
1.) A ruling race.
2.) A cultural mix
I'm going to go along a cultural mix for this Map Idea.
Town square would be a large circle. A circular pool of water in the middle.
Buildings would surround the town square-- since the town square is, after all, the center of town.
Moat around the town.
Merchants would stand outside their doors, and be within reach of the town square.
A large castle would be in the back, kind of like in terthius. This castle would have multiple levels, and a dungeon beneath it, perhaps a portal to ds in the basement.
Town guard concepts:
Scallions:
Guards: Mystic Staff, Mage Hat, and Mystic Leather. (soa would also make guards tougher)
Captains: Altus Lucrum Staff, Adept Hat, Spirit Leather
General: Ominex Staff, Mystic Hat, Dirus Armor
Gnomes:
Guards: Staff of Almus, Mystic Plate, Mystic Helmet
Captains: Spirit Wand, Spirit Shield, Spirit Helm, Spirit Plate
General: Dirus Armor, Dirus Wand, Shield of Ominex, Omni Helm
Dregs:
Guards: Mystic Plate, Vita Halberd, Mystic Helm
Captains: Spirit Plate, Sword of Blood, Spirit Shield, Spirit Helm
General: Dirus Armor, Dirus Battle Axe, Omni Shield, Omni Helm
Elf:
Guards: Lightning Bow, Mystic Scale, Mystic Helmet
Captains: Mystic Bow, Mystic Bone Mail, Spirit Helmet
General: Dirus Armor, Omni Bow, Omni Helmet
Humans:
Random mix of these


Interfaces which plugins must implement, and global plugin functionality. The interfaces and required functionality are described more fully in the
PluginSpec
.


Alberto Blázquez
Software Engineer and Frontend Developer
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GOVERNOR ANDRES PASCUAL COLLEGE
BACHELOR OF SECONDARY EDUCATION
Effective June 2009-2010
Science & Health
First Year
First Semester
| Subject | Description | Units |
|---|---|---|
| Eng 101 | English Communication 1 | 3 |
| Fil 101 | Sining ng Komunikasyon | 3 |
| Hist 101 | Philippine History | 3 |
| Math 101 | College Algebra | 3 |
| Nat Sci 101 | Earth Science | 3 |
| Hum 101 | Art Appreciation | 3 |
| Psycho 101 | General Psychology | 3 |
| CWTS 101 | Civic Welfare 1 | 3 |
| PE 101 | Self Testing Activities | 2 |
| 26 |
Second Semester
| Subject | Description | Units |
|---|---|---|
| Eng 102 | English Communication 2 | 3 |
| Fil 102 | Pagbabasa at Pagsulat sa Ibat-ibang Disiplina | 3 |
| Nat Sci 2 | Biological Science | 3 |
| PS 101 | Politics & Governance w/ Phil. Constitution | 3 |
| SS 101 | Life, Works & Writing of Rizal | 3 |
| Science 101 | Science, Technology & Society | 3 |
| ICT 101 | Introduction to Information Technology | 3 |
| CWTS 101 | Civic Welfare 2 | 3 |
| PE 102 | Fundamentals of Rhythmic Activities | 2 |
| 26 |
Second Year
First Semester
| Subject | Description | Units |
|---|---|---|
| Eng 103 | Modern Communication (Writing & Oral Comm | 3 |
| Fil 3 | Ang Panitikang Filipino | 3 |
| Music 101 | Fund of Music | 3 |
| CL 101 | Children's Literature | 3 |
| Educ 101 | Child & Adolescent Development | 3 |
| Educ 102 | Educational Technology 1 | 3 |
| Science 102 | General Zoology | 3 |
| PE 103 | Group Games | 2 |
| 25 |
Second Semester
| Subject | Description | Units |
|---|---|---|
| Educ 103 | Educational Technology 2 | 3 |
| Educ 104 | Principles of Teaching | 3 |
| Educ 105 | Facilitating Learning | 3 |
| Lit 101 | Philippine Literature | 3 |
| DR 101 | Developmental Reading 1 | 3 |
| Science 103 | General Botany | 3 |
| PE 104 | Social Recreation | 2 |
| 25 |
Third Year
First Semester
| Subject | Description | Units |
|---|---|---|
| Educ 107 | Teaching Profession | 3 |
| Educ 108 | Principles of Teaching 2 | 3 |
| Educ 109 | Assessment of Student Learning | 3 |
| Educ 110 | Field Studies 1 & 2 | 2 |
| Educ 201 | Logical & Critical Thinking | 3 |
| Science 104 | History & Philosophy of Science | 3 |
| Science 105 | General & Inorganic Chemistry | 5 |
| Science 106 | Cell Biology | 3 |
| 25 |
Second Semester
| Subject | Description | Units |
|---|---|---|
| SS 102 | Principles of Economics w/ TAR | 3 |
| Educ 111 | Assessment of Student Learning 2 | 3 |
| Educ 112 | Field Studies 3 & 4 | 2 |
| Educ 113 | Intro to Curriculum Development | 3 |
| Educ 202 | Educational Psychology | 3 |
| Science 107 | Organic Chemistry | 5 |
| Science 108 | General Ecology | 5 |
| Science 109 | Biotechniques | 3 |
| 27 |
Fourth Year
First Semester
| Subject | Description | Units |
|---|---|---|
| Lit 102 | Literature of the World | 3 |
| Educ 203 | Non-Formal Education | 3 |
| Educ 114 | Field Studies 5 & 6 | 2 |
| Educ 118 | Special Topics 1, 2 & 3 | 3 |
| Science 110 | Microbiology | 5 |
| Science 111 | Genetics | 4 |
| Science 112 | Physics for Health Science | 3 |
| Science 113 | Anatomy & Physiology | 4 |
| 27 |
Second Semester
| Subject | Description | Units |
|---|---|---|
| Educ 116 | Practicum | 6 |
| Summary Units | |
|---|---|
| General Education | 68 units |
PROFESSIONAL EDUCATION |
|
| Theory & Concepts | 12 units |
| Methods | 24 units |
| Field Study | 12 units |
| Special Topics | 3 units |
| Specialization | 65 units |
| TOTAL | 164 units |


To restrict access to a specific file, add the following code block and edit the file name, secretfile.jpg, with the name of the file that you wish to protect:
# prevent viewing of a specific file
<files secretfile.jpg>
Order allow,deny
Deny from all
</files>
For further infos, check out Protect files with .htaccess.


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Dummy
Home food delivery systems
Order food online in a few clicks from your nearest restaurants in Bangladesh.Order food online from you loved restaurants and enjoy at home!!


In any nontrivial software project, bugs are simply a fact of life. Careful planning, programming, and testing can help reduce their pervasiveness, but somehow, somewhere, they'll always find a way to creep into your code. This becomes especially apparent as new features are introduced and your code base grows in size and complexity.
Fortunately, some bugs are easier to detect than others. Compile-time bugs, for example, can be detected early on; you can use the compiler's error messages to figure out what the problem is and fix it, right then and there. Runtime bugs, however, can be much more problematic; they don't always surface immediately, and when they do, it may be at a point in the program that is far removed from the actual cause of the problem.
Generics add stability to your code by making more of your bugs detectable at compile time. After completing this lesson, you may want to follow up with the Generics tutorial by Gilad Bracha.
Press ‘Tab’ to Insert Child, ‘Enter’ to Insert Sibling Node. For more tips/news, follow @my_mind_app.
My Mind
Layout
Shape
Value
Status
Color
Storage
Local files are suitable for loading/saving files from other mindmapping applications.
Format
Export your design as a PNG image.
Your browser's localStorage can handle many mind maps and creates a permalink, but this URL cannot be shared.
Saved maps
Firebase offers real-time synchronization for true multi-user collaboration.
Server
Auth
Saved maps
Use this to access a generic DAV-like REST API.
URL
Maps stored in Google Drive have a permalink URL and can be shared with other users, if you allow this by setting proper permissions (inside Google Drive itself).


Duration: 1 Year
Role: Developer, PM, Tester
Technical Tools: Microsoft Visual Studio, Unity, C#, Github, HTC Vive
Using Extended Reality Technology, create an application/simulation to help students better understand the Physics concept of Projectile Motion.
Virtual Reality applications are used for entertainment purposes, but the goal of this project was to use technology to improve education for students to better engage and be more involved with their learning.
UBC AGDA Club (AMS Game Development Association) is a student run video game development club at the University of British Columbia. Every year, there are workshops, guest speakers, and projects to focus on educating members of technical tools used in game development. Upon joining this club, I joined the AR/VR division and worked with group members on a specific project to learn more about Unity and the various tools that are used in extended reality development.
Working with the Emerging Media Lab at UBC, teams were able to utilize the resources and space to collaborate, experiment and discuss.
Due to company policy and their ownership of all work I have done. I am unavailable to showcase some of the feature implementation and work I have done.
The Software Development Team was transitioning from a Waterfall style of workflow to Agile Sprint Planning Practices. Part of my role was to assist in the onboarding integration of such practices while working on flight testing software.
The team transition from using Mercurial version control to Github and VSTS services. This enabled better clarity for maintainance, code review and feedback of tasks for a project. In addition, I participated in weekly scrum meetings to perform requirements analysis, feature prioritization, pre-planned scheduling of tasks, testing, user validation and feedback. This would help the software team gauge and organize tasks into manageable weekly chunks, and enable better communication amongst team members to jointly work on various features within the same project.
Software, IT and Dev-Ops teams gathered every other day to discuss goals, plans and ongoing work for each individual member.
During my implementation of features for software, I was to research and learn a variety of concepts such as user interfacing practices, HTML/CSS features, M-V-VM Model. I have taken notes and done enough research to perform my tasks efficiently and to the best of my ability.
In addition I had documented weekly progress reports for the work I completed, and completed goal setting exercises that were evaluated by my mentors to gauge my learning and experience.
Here is a sample of how we would conduct our meetings.


Switch between Spotify and Youtube playback from a single web-page.
Play directly plays the song while QPlay adds
it to the queue and jumps to it (assuming queue was empty before).
Play start the Youtube video and stops Spotify.
When video ends or when you close it, Spotify playback will resume what it was playing before (assuming the device did not go offline).
Youtube videos are directly played on this page while Spotify plays from your active device.
Code hosted on GitHub.
Spotify icon by Icons8.


Urban Airship Titanium Module
Version 3.1.1 - January 26, 2017
- Updated Urban Airship iOS library to 8.1.6
Version 3.1.0 - December 9, 2016
- Updated Urban Airship Android Library to 8.2.2
- Updated Urban Airship iOS library to 8.1.4
Version 3.0.0 - November 22, 2016
- Update to Titanium 6.0.0 GA.
- Added iOS foreground presentation support.
- Added support for associated identifiers.
- Added support for custom events.
Version 2.0.2 - November 21, 2016
- Fixed getLaunchNotification on iOS not returning the notification when app is started from a push notification.
Version 2.0.1 - October 4, 2016
- Updated Urban Airship iOS Library to 8.0.2
Version 2.0.0 - September 28, 2016
- Updated Urban Airship iOS Library to 8.0.1 (requires Xcode 8)
- Updated to Titanium 5.5.0 GA SDK.
Version 1.3.0 - June 23, 2016
- Updated Urban Airship Android library to 7.2.0
- For iOS set the launchNotification in the startup method.
Version 1.2.0 - June 2, 2016
- Updated Urban Airship iOS Library to 7.2.0
- Updated Urban Airship Android library to 7.1.5
Version 1.1.1 - May 12, 2016
- Updated Urban Airship Android Library to 7.1.3
- Updated Urban Airship iOS library to 7.1.2
Version 1.1.0 - May 4, 2016
- Updated Urban Airship Android Library to 7.1.2
- Updated Urban Airship iOS library to 7.1.0
Version 1.0.0 - April 15, 2016


Services
Do you have big dreams of making changes to your site but never seem to find time or know where to start?
Monthly Recurring Services: $250/month
Types of Tasks
- Make site mobile-ready
- Make site fully-responsive
- Migrate site to WordPress
- Add new page
- Upload blog posts
- Maintain plug-ins
- Search Engine Optimization (SEO)
Experiment & have fun!!!
The whole point of this offering is to make changing your website fun! We can experiment together and decide what works and what doesn’t without worrying about messing up y our live site!
Has your brand outgrown your current site leaving you wanting more? Or are you wanting to create your own blog for the first time?
Project-based Services: Contact for quote
What you get
- Full mobile-first website design
- Typography
- Color Palette
- User Persona
- Custom WordPress template, if wanted
- Add purchasing ability and shopping cart
- Fully responsive & mobile-ready custom website
Everything you need!!!
This offering has everything you need for a site you will love! Each entrepeneur’s needs are unique, so there’s no “one size fits all” solution. And that’s a good thing! So let’s chat about your business and site needs and go from there!


Flight Details
Flight No: 511
Airline: IndiGo
Airline Code: 6E
Departure Date and Time: 2021-01-14 17:30
Departure Airport:
Departure City & Country:
Departure Terminal:
Arrival Airport: Indira Gandhi International Airport
Arrival City & Country: Delhi, India
Total Travel Time: 2 Hours 5 Min
Delay Time: --
Gate No: --
Flight Details
Flight No: 8246
Airline: IndiGo
Airline Code: 6E
Departure Date and Time: 2021-01-15 05:35
Departure Airport:
Departure City & Country:
Departure Terminal:
Arrival Airport: Velana International Airport
Arrival City & Country: Male, Maldives
Total Travel Time: 4 Hours 5 Min
Delay Time: --
Gate No: 4A
Flight Details
Flight No: 8247
Airline: IndiGo
Airline Code: 6E
Departure Date and Time: 2021-01-18 16:15
Departure Airport:
Departure City & Country:
Departure Terminal:
Arrival Airport: Indira Gandhi International Airport
Arrival City & Country: Delhi, India
Total Travel Time: 4 Hours 35 Min
Delay Time:
Gate No:
Flight Details
Flight No: 2757
Airline: IndiGo
Airline Code: 6E
Departure Date and Time: 2021-01-19 07:10
Departure Airport:
Departure City & Country:
Departure Terminal:
Arrival Airport: Swami Vivekananda Airport
Arrival City & Country: Raipur, India
Total Travel Time: 1 Hours 50 Min
Delay Time:
Gate No:
Manager Details
Name: GAUTAM KAKAR
Mob No:<PHONE_NUMBER>
Email: info@saarth.co.in


SYNOPSIS: Along with my primary responsibility as a designer and technologist at IBM, I create intellectual property material for the company either alone or by collaborating with designers, engineers, and project managers.
I will disclose the inventions that are either filed in the patent office, in patent pending status or published in a intellectual property article repository.


ACME Garden Planner
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You will have trouble surviving in this place if you don't have enough gold. Willing to get a job...?
Remaining time: {{helperService.minutesToTimeWithSeconds(secondsRemaining)}}


St. Mary's Church Cogges Parochial Church Council
Transcript of Memorial Inscriptions at
The Parish Church of St. Mary,
Cogges, Oxfordshire.
Main Menu
Preface
History of the Church
Church Photographs - Inside
Church Photographs - Outside
Index of Names
Area Plan
Area Photographs
Using this Transcription
Copyright Conditions
Introduction to Cogges Parish Registers
Incumbents of Cogges
Churchwardens of Cogges during the 18th Century
Oxfordshire Family History Society
The Parish Church of St. Mary,
Cogges, Oxfordshire
Cogges Parish website
The Parish Church of St. Mary, Cogges, Oxfordshire.
Copyright © St. Mary's Church Cogges Parochial Church Council, 2005
Updated 9th April 2005
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Math Quiz 2
choose a difficulty
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Play
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--- title: Keydra description: Single Sign On for everyone --- Single Sign On for everyone Keydra secures your website, mobile app or IoT.
Keydra secures your website, mobile app or IoT.


import MessageFactory from 'service-framework/src/message-factory/MessageFactory.js'
MessageFactory
Constructor Summary
| Public Constructor | ||
| public |
Constructor to be used to instantiate an object of the Message Factory |
Member Summary
| Public Members | ||
| public |
myGenerator: * |
Method Summary
| Public Methods | ||
| public |
createCreateMessageRequest(from: URL.URL, to-: URL.URLList, value: String, policy: URL.URL): * Creates a Message of TYPE CREATE and Create Message Body |
|
| public |
createDeleteMessageRequest(from: URL.URL, to: URL.URLList, resource: URL.URl, attribute: *): Message.Message Create Delete Message of Type DELETE and delete message body |
|
| public |
createExecuteMessageRequest(from: URL.URL, to-: URL.URLList, method: string, params: Array): * Creates a Message of type EXECUTE |
|
| public |
createForwardMessageRequest(from: URL.URL, to-: URL.URLList, message: *): Message.Message Creates a Message of TYPE FORWARD and Forward Message Body, which contains the message to be forwarded |
|
| public |
createMessageResponse(message: *, code: *, value: *, source: *): * Creates the response to the Message |
|
| public |
createReadMessageRequest(from: URL.URL, to-: URL.URLList, resource: URL.URl, attribute: *): Message.Message Creates a Message of type READ |
|
| public |
createSubscribeMessageRequest(from: URL.URL, to-: URL.URLList, resource: URL.URL): * Creates a Message of type SUBSCRIBE |
|
| public |
createUnsubscribeMessageRequest(from: URL.URL, to-: URL.URLList, resource: URL.URL): * Creates a Message of type UNSUBSCRIBE |
|
| public |
createUpdateMessageRequest(from: URL.URL, value: *, resource: URL.URL, attribute: *): Message.Message Creates an Update Message Request |
|
| public |
generateMessageResponse(data: Message, code: ResponseCode, value: String): * Generate a response to the given Message Request - Response transactions A Response to a Request message should follow this rule: Response.from = Request.to Response.to = Request.from Response.id = Request.id It should be note, the Request.id MUST be incremented every time a new Request message is created. |
Public Constructors
Public Members
public myGenerator: * source
Public Methods
public createCreateMessageRequest(from: URL.URL, to-: URL.URLList, value: String, policy: URL.URL): * source
Creates a Message of TYPE CREATE and Create Message Body
Params:
| Name | Type | Attribute | Description |
| from | URL.URL | the sender of this message |
|
| to- | URL.URLList | One or more URLs of Message recipients. According to the URL scheme it may be handled in different ways |
|
| value | String | Contains the created object in JSON format |
|
| policy | URL.URL | the sender of this message |
Return:
| * |
public createDeleteMessageRequest(from: URL.URL, to: URL.URLList, resource: URL.URl, attribute: *): Message.Message source
Create Delete Message of Type DELETE and delete message body
Params:
| Name | Type | Attribute | Description |
| from | URL.URL | the sender of this message |
|
| to | URL.URLList | One or more URLs of Message recipients. According to the URL scheme it may be handled in different ways |
|
| resource | URL.URl | URL of Data Object Resource associated with the message |
|
| attribute | * | Identifies the attribute in the Object to be deleted |
Return:
| Message.Message | Message - the Delete Message Request |
public createExecuteMessageRequest(from: URL.URL, to-: URL.URLList, method: string, params: Array): * source
Creates a Message of type EXECUTE
Return:
| * |
public createForwardMessageRequest(from: URL.URL, to-: URL.URLList, message: *): Message.Message source
Creates a Message of TYPE FORWARD and Forward Message Body, which contains the message to be forwarded
Params:
| Name | Type | Attribute | Description |
| from | URL.URL | the sender of this message |
|
| to- | URL.URLList | One or more URLs of Message recipients. According to the URL scheme it may be handled in different ways |
|
| message | * | {Message.Message} message - the message to be forwarded |
Return:
| Message.Message | Message - the Forward Message Request |
public createMessageResponse(message: *, code: *, value: *, source: *): * source
Creates the response to the Message
Params:
| Name | Type | Attribute | Description |
| message | * | the message request from which the response should be generated |
|
| code | * | the response code compliant with HTTP response codes (RFC7231). |
|
| value | * | contains a data value in JSON format. Applicable to Responses to READ MessageType. |
|
| source | * | Contains the original creator of the response. Useful to identify the real source of the response to a one-to-many message delivery ie multiple responses coming coming from different sources. |
Return:
| * |
public createReadMessageRequest(from: URL.URL, to-: URL.URLList, resource: URL.URl, attribute: *): Message.Message source
Creates a Message of type READ
Params:
| Name | Type | Attribute | Description |
| from | URL.URL | the sender of this message |
|
| to- | URL.URLList | One or more URLs of Message recipients. According to the URL scheme it may be handled in different ways |
|
| resource | URL.URl | URL of Data Object Resource associated with the message |
|
| attribute | * | Identifies the attribute in the Object to be read |
Return:
| Message.Message | Message - the Read message request |
public createSubscribeMessageRequest(from: URL.URL, to-: URL.URLList, resource: URL.URL): * source
Creates a Message of type SUBSCRIBE
Params:
| Name | Type | Attribute | Description |
| from | URL.URL | the sender of this message |
|
| to- | URL.URLList | One or more URLs of Message recipients. According to the URL scheme it may be handled in different ways |
|
| resource | URL.URL | URL of the object |
Return:
| * |
public createUnsubscribeMessageRequest(from: URL.URL, to-: URL.URLList, resource: URL.URL): * source
Creates a Message of type UNSUBSCRIBE
Params:
| Name | Type | Attribute | Description |
| from | URL.URL | the sender of this message |
|
| to- | URL.URLList | One or more URLs of Message recipients. According to the URL scheme it may be handled in different ways |
|
| resource | URL.URL | URL of the object |
Return:
| * |
public createUpdateMessageRequest(from: URL.URL, value: *, resource: URL.URL, attribute: *): Message.Message source
Creates an Update Message Request
Params:
| Name | Type | Attribute | Description |
| from | URL.URL | the sender of this message |
|
| {URL.URLList}to | * | One or more URLs of Message recipients. According to the URL scheme it may be handled in different ways |
|
| value | * | The new value of the attribute to be updated |
|
| resource | URL.URL | URL of Data Object Resource associated with the message |
|
| attribute | * | Identifies the attribute in the Object to be updated |
Return:
| Message.Message | Message - the Update message request |
public generateMessageResponse(data: Message, code: ResponseCode, value: String): * source
Generate a response to the given Message Request - Response transactions A Response to a Request message should follow this rule: Response.from = Request.to Response.to = Request.from Response.id = Request.id It should be note, the Request.id MUST be incremented every time a new Request message is created.
Return:
| * |


|
Contact
Information
Address:
Cyber-Physical Energy System Laboratory (NTDP B241),
Department of Electrical Engineering, Discovery Park (3940 N Elm St), University of North Texas, Denton, TX 76207 [Map]
Tel: +1<PHONE_NUMBER>
Email: ShengjunZhang [at] my [dot] unt [dot] edu
LinkedIn: Daniel Zhang
GitHub: Daniel Zhang
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--- layout: page title: About header-img: "img/about-bg.png" ---
Mark is an instrument maker in Nashville, TN, and has recently begun a coding bootcamp to find a career in coding.


Introduction
The base url is https://uclapi.com/api/v1
Get All Rooms
/rooms/all
This endpoint gets a list of all available rooms and their metadata.
curl https://uclapi.com/api/v1/rooms/all
import requests
requests.get("https://uclapi.com/api/v1/rooms/all")
var xhr = new XMLHttpRequest();
xhr.open('GET', 'https://uclapi.com/api/v1/rooms/all', true);
xhr.send();
// response from the server
xhr.responseText;
Restrictions: nil
Allowed request type: GET
Response
Response
[
{
"roomName":"Gustave Tuck Lecture Theater",
"roomId": "abc123",
"capacity": 200,
"type": "Lecture theater"
}
]
| Field | Type | Description |
|---|---|---|
| roomName | String |
The name of the room |
| roomId | String |
The rooms id string |
| capacity | Number |
The total capacity of the room |
| type | String |
The type of room |
Get Bookings for a Room
/bookings
This endpoint shows the bookings for a room
Query Parameters
| Parameter | Type | Default Value | Description |
|---|---|---|---|
| roomId | String[] |
All room ids | A list of the ids of rooms you want to see the bookings for |
| startDate | date |
Current date and time rounded up to the nearest hour | The start date and time you want to search from |
| endDate | date |
One week from the current date and time rounded up to the nearest hour | The end date and time you want to search to |
Restrictions: nil
Allowed request type: GET
curl https://uclapi.com/api/v1/bookings?roomId=abc123&startDate=2016-10-23T11:00:00&endDate=2016-10-23T14:00:00
import requests
requests.get("https://uclapi.com/api/v1/bookings?roomId=abc123&startDate=2016-10-23T11:00:00&endDate=2016-10-23T14:00:00")
var xhr = new XMLHttpRequest();
xhr.open('GET', 'https://uclapi.com/api/v1/bookings?roomId=abc123&startDate=2016-10-23T11:00:00&endDate=2016-10-23T14:00:00', true);
xhr.send();
// response from the server
xhr.responseText;
Response
Response
[
{
"bookingId": "def456",
"roomId": "abc123",
"roomName": "Gustave Tuck Lecture Theater",
"startDate": "2016-10-23T12:00:00",
"endDate": "2016-10-23T13:00:00"
}
]
| Field | Type | Description |
|---|---|---|
| bookingId | String |
The id of the booking |
| roomId | String |
The roomId of the room |
| roomName | String |
The name of the room |
| startDate | date |
Start date and time of the booking |
| endDate | date |
End date and time of the booking |
Errors
The Kittn API uses the following error codes:
| Error Code | Meaning |
|---|---|
| 400 | Bad Request – Your request sucks |
| 401 | Unauthorized – Your API key is wrong |
| 403 | Forbidden – The kitten requested is hidden for administrators only |
| 404 | Not Found – The specified kitten could not be found |
| 405 | Method Not Allowed – You tried to access a kitten with an invalid method |
| 406 | Not Acceptable – You requested a format that isn’t json |
| 410 | Gone – The kitten requested has been removed from our servers |
| 418 | I’m a teapot |
| 429 | Too Many Requests – You’re requesting too many kittens! Slow down! |
| 500 | Internal Server Error – We had a problem with our server. Try again later. |
| 503 | Service Unavailable – We’re temporarially offline for maintanance. Please try again later. |


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Have verteran hip-hop artist Truth Trill check out Your song's Beat, Flow, Lyrics, and Sound Quality. One of the hardest things to get in this business is an honest opinion with no pandering or hate. Whether Rap RnB or just a Beat even in a genre I'm not in I will offer my ear and ofcourse, the TRUTH!
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What's Next?
Source: datasframe | Author: Tom Augspurger | Published: Nov 11, 2020
Some personal news: Last Friday was my last day at Anaconda.
Next week, I'm joining Microsoft's AI for Earth team. This is a very bittersweet transition. While I loved working at Anaconda and all the great people there, I'm extremely excited about what I'll be working on at Microsoft.
Reflections …
Read more
Maintaing Performance
Source: datasframe | Author: Tom Augspurger | Published: Apr 01, 2020
As pandas' documentation claims: pandas
provides high-performance data structures. But how do we verify that the claim
is correct? And how do we ensure that it stays correct over many releases.
This post describes
pandas' current setup for monitoring performance
My personal debugging strategy for understanding and fixing performance
regressions …
Read more
pandas 1.0
Source: pandas blog | Author: pandas team | Published: Jan 29, 2020
Today pandas celebrates its 1.0.0 release. In many ways this is just a normal release with a host of new features, performance improvements, and bug fixes, which are documented in
Read more
Towards consistent missing value handling in Pandas
Source: Joris Van den Bossche - pandas | Author: Joris Van den Bossche | Published: Nov 30, 2019
This blogpost gives some background and motivation for my proposal on better
missing value support in pandas, and the changes that have been merged in the
development version (to be released in pandas 1.0): a new pd.NA scalar is
introduced that can be used consistently across all data types..
Read more
2019 NumFOCUS Awards and New Contributor Recognition
Source: pandas | NumFOCUS | Author: Admin | Published: Nov 15, 2019
The post 2019 NumFOCUS Awards and New Contributor Recognition appeared first on NumFOCUS.
Read more
Chan Zuckerberg Initiative Funds Maintenance of NumFOCUS Projects
Source: pandas | NumFOCUS | Author: Admin | Published: Nov 14, 2019
The post Chan Zuckerberg Initiative Funds Maintenance of NumFOCUS Projects appeared first on NumFOCUS.
Read more
Highlights From The 2019 Pandas Hack
Source: pandas | NumFOCUS | Author: nf-admin | Published: Sep 13, 2019
The post Highlights From The 2019 Pandas Hack appeared first on NumFOCUS.
Read more
2019 pandas user survey
Source: pandas blog | Author: pandas team | Published: Aug 22, 2019
Pandas recently conducted a user survey to help guide future development.
Thanks to everyone who participated! This post presents the high-level results.
This analysis and the raw data can be found on
Read more
GeoPandas now uses the pandas ExtensionArray interface
Source: Joris Van den Bossche - pandas | Author: Joris Van den Bossche | Published: Aug 13, 2019
Short summary: the upcoming 0.6.0 release of GeoPandas will feature a refactor based on the pandas ExtensionArray interface. Although this change should keep the user interface mostly stable, it enables more robust integration with pandas and allows for more upcoming changes in the future. And given the invasive code changes under the hood, testing is very welcome!
Read more
pandas + binder
Source: datasframe | Author: Tom Augspurger | Published: Jul 21, 2019
This post describes the start of a journey to get pandas' documentation running
on Binder. The end result is this nice button:
For a while now I've been jealous of Dask's examples
repository. That's a repository containing a
collection of Jupyter notebooks demonstrating Dask in action. It stitches
together some …
Read more
pandas extension arrays
Source: pandas blog | Author: pandas team | Published: Jan 04, 2019
Extensibility was a major theme in pandas development over the last couple of
releases. This post introduces the pandas extension array interface: the
motivation behind it and how it might affect you
Read more
Inaugural NumFOCUS Awards and New Contributor Recognition
Source: pandas | NumFOCUS | Author: Admin | Published: Sep 27, 2018
The post Inaugural NumFOCUS Awards and New Contributor Recognition appeared first on NumFOCUS.
Read more
Tabular Data in Scikit-Learn and Dask-ML
Source: datasframe | Author: Tom Augspurger | Published: Sep 17, 2018
Scikit-Learn 0.20.0 will contain some nice new features for working with tabular data.
This blogpost will introduce those improvements with a small demo.
We'll then see how Dask-ML was able to piggyback on the work done by scikit-learn to offer a version that works well with Dask Arrays …
Read more
Distributed Auto-ML with TPOT with Dask
Source: datasframe | Author: Tom Augspurger | Published: Aug 30, 2018
This work is supported by Anaconda Inc.
This post describes a recent improvement made to TPOT. TPOT is an
automated machine learning library for Python. It does some feature
engineering and hyper-parameter optimization for you. TPOT uses genetic
algorithms to evaluate which models are performing well and how to choose …
Read more
Moral Philosophy for pandas or: What is .values?
Source: datasframe | Author: Tom Augspurger | Published: Aug 14, 2018
The other day, I put up a Twitter poll asking a simple question: What's the type of series.values?
Pop Quiz! What are the possible results for the following:>>> type(pandas.Series.values)— Tom Augspurger (@TomAugspurger) August 6, 2018
I was a bit limited for space, so I'll expand on …
Read more
Modern Pandas (Part 8): Scaling
Source: datasframe | Author: Tom Augspurger | Published: Apr 23, 2018
This is part 1 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
As I sit down to write this, the third-most popular pandas question on StackOverflow covers how to use pandas for large datasets. This is in …
Read more
The Worldwide Pandas Documentation Sprint: A Closer Look
Source: pandas | NumFOCUS | Author: Admin | Published: Mar 27, 2018
The post The Worldwide Pandas Documentation Sprint: A Closer Look appeared first on NumFOCUS.
Read more
Activity on the pandas github repo during the March 10 documentation sprint
Source: Joris Van den Bossche - pandas | Author: Joris Van den Bossche | Published: Mar 13, 2018
Last weekend, Marc Garcia and many others organised a world-wide pandas documentation sprint (https://python-sprints.github.io/pandas/). The goal was to improve the pandas API documentation, and I have to say, it was a great success!
Read more
dask-ml 0.4.1 Released
Source: datasframe | Author: Tom Augspurger | Published: Feb 13, 2018
This work is supported by Anaconda Inc and the Data
Driven Discovery Initiative from the Moore
Foundation.
dask-ml 0.4.1 was released today with a few enhancements. See the
changelog for all the changes from 0.4.0.
Conda packages are available on conda-forge
$ conda install -c conda-forge dask-ml …
Read more
Extension Arrays for Pandas
Source: datasframe | Author: Tom Augspurger | Published: Feb 12, 2018
This is a status update on some enhancements for pandas. The goal of the work
is to store things that are sufficiently array-like in a pandas DataFrame,
even if they aren't a regular NumPy array. Pandas already does this in a few
places for some blessed types (like Categorical); we'd …
Read more
Easy distributed training with Joblib and dask
Source: datasframe | Author: Tom Augspurger | Published: Feb 05, 2018
This work is supported by Anaconda Inc and the Data
Driven Discovery Initiative from the Moore
Foundation.
This past week, I had a chance to visit some of the scikit-learn developers at
Inria in Paris. It was a fun and productive week, and I'm thankful to them for
hosting me …
Read more
dask-ml
Source: datasframe | Author: Tom Augspurger | Published: Oct 26, 2017
Today we released the first version of dask-ml, a library for parallel and
distributed machine learning. Read the documentation or install it with
pip install dask-ml
Packages are currently building for conda-forge, and will be up later today.
conda install -c conda-forge dask-ml
The Goals
dask is, to quote the …
Read more
Scalable Machine Learning (Part 2): Partial Fit
Source: datasframe | Author: Tom Augspurger | Published: Sep 15, 2017
This work is supported by Anaconda, Inc. and the
Data Driven Discovery Initiative from the Moore Foundation.
This is part two of my series on scalable machine learning.
Small Fit, Big Predict
Scikit-Learn Partial Fit
You can download a notebook of this post here.
Scikit-learn supports out-of-core learning (fitting a …
Read more
Scalable Machine Learning (Part 1)
Source: datasframe | Author: Tom Augspurger | Published: Sep 11, 2017
This work is supported by Anaconda Inc. and the Data Driven Discovery
Initiative from the Moore Foundation.
Anaconda is interested in scaling the scientific python ecosystem. My current
focus is on out-of-core, parallel, and distributed machine learning. This series
of posts will introduce those concepts, explore what we have available …
Read more
Introducing Stitch
Source: datasframe | Author: Tom Augspurger | Published: Aug 30, 2016
Today I released stitch into the
wild. If you haven't yet, check out the examples
page to see an example of what stitch does,
and the Github repo for how to
install. I'm using this post to explain why I wrote stitch, and some
issues it tries to solve.
Why …
Read more
Modern Pandas (Part 7): Timeseries
Source: datasframe | Author: Tom Augspurger | Published: May 13, 2016
This is part 7 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Timeseries
Pandas started out in the financial world, so naturally it has strong timeseries support.
The first half of this post will look at pandas' …
Read more
Modern Pandas (Part 6): Visualization
Source: datasframe | Author: Tom Augspurger | Published: Apr 28, 2016
This is part 6 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Visualization and Exploratory Analysis
A few weeks ago, the R community went through some hand-wringing about plotting packages.
For outsiders (like me) the details aren't …
Read more
Modern Pandas (Part 5): Tidy Data
Source: datasframe | Author: Tom Augspurger | Published: Apr 22, 2016
This is part 5 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Reshaping & Tidy Data
Structuring datasets to facilitate analysis (Wickham 2014)
So, you've sat down to analyze a new dataset.
What do you do first?
In …
Read more
Modern Panadas (Part 3): Indexes
Source: datasframe | Author: Tom Augspurger | Published: Apr 11, 2016
This is part 3 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Indexes can be a difficult concept to grasp at first.
I suspect this is partly becuase they're somewhat peculiar to pandas.
These aren't like the …
Read more
Modern Pandas (Part 4): Performance
Source: datasframe | Author: Tom Augspurger | Published: Apr 08, 2016
This is part 4 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Wes McKinney, the creator of pandas, is kind of obsessed with performance. From micro-optimizations for element access, to embedding a fast hash table inside pandas …
Read more
Modern Pandas (Part 2): Method Chaining
Source: datasframe | Author: Tom Augspurger | Published: Apr 04, 2016
This is part 2 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Method Chaining
Method chaining, where you call methods on an object one after another, is in vogue at the moment.
It's always been a style …
Read more
Modern Pandas (Part 1)
Source: datasframe | Author: Tom Augspurger | Published: Mar 21, 2016
This is part 1 in my series on writing modern idiomatic pandas.
Modern Pandas
Method Chaining
Indexes
Fast Pandas
Tidy Data
Visualization
Time Series
Scaling
Effective Pandas
Introduction
This series is about how to make effective use of pandas, a data analysis library for the Python programming language.
It's targeted …
Read more
Why pandas users should be excited about Apache Arrow
Source: Wes McKinney - pandas | Author: Wes McKinney | Published: Feb 22, 2016
I'm super excited to be involved in the new open source Apache Arrow
community initiative. For Python (and R, too!), it will help enable
Substantially improved data access speeds
Closer to native performance Python extensions for big data systems like
Apache Spark
New in-memory analytics functionality for nested / JSON-like data
There's plenty of places you can learn more about Arrow, but this post is about
how it's specifically relevant to pandas users. See, for example:
"Python and Hadoop: A State of the Union"
"Introducing Apache Arrow: A Fast, Interoperable In-Memory Columnar Data Structure Standard"
"Introducing Apache Arrow: Columnar In-Memory Analytics"
Read more
NumFOCUS Announces New Fiscally Sponsored Project: pandas
Source: pandas | NumFOCUS | Author: nf-admin | Published: Oct 09, 2015
by Gina Helfrich NumFOCUS is pleased to announce pandas as our newest fiscally sponsored project. pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. pandas enables users to carry out their entire data analysis workflow in Python without having to switch to a more domain-specific language like […]
The post NumFOCUS Announces New Fiscally Sponsored Project: pandas appeared first on NumFOCUS.
Read more
dplyr and pandas
Source: datasframe | Author: Tom Augspurger | Published: Oct 16, 2014
This notebook compares pandas
and dplyr.
The comparison is just on syntax (verbage), not performance. Whether you're an R user looking to switch to pandas (or the other way around), I hope this guide will help ease the transition.
We'll work through the introductory dplyr vignette to analyze some flight …
Read more
Practical Pandas Part 3 - Exploratory Data Analysis
Source: datasframe | Author: Tom Augspurger | Published: Sep 16, 2014
Welcome back. As a reminder:
In part 1 we got dataset with my cycling data from last year merged and stored in an HDF5 store
In part 2 we did some cleaning and augmented the cycling data with data from http://forecast.io.
You can find the full source code …
Read more
Practical Pandas Part 2 - More Tidying, More Data, and Merging
Source: datasframe | Author: Tom Augspurger | Published: Sep 04, 2014
This is Part 2 in the Practical Pandas Series, where I work through a data analysis problem from start to finish.
It's a misconception that we can cleanly separate the data analysis pipeline into a linear
sequence of steps from
data acqusition
data tidying
exploratory analysis
model building
production
As …
Read more
Practical Pandas Part 1 - Reading the Data
Source: datasframe | Author: Tom Augspurger | Published: Aug 26, 2014
This is the first post in a series where I'll show how I use pandas on real-world datasets.
For this post, we'll look at data I collected with Cyclemeter on
my daily bike ride to and from school last year.
I had to manually start and stop the tracking at …
Read more
Using Python to tackle the CPS (Part 4)
Source: datasframe | Author: Tom Augspurger | Published: May 19, 2014
Last time, we got to where we'd like to have started: One file per month, with each month laid out the same.
As a reminder, the CPS interviews households 8 times over the course of 16 months. They're interviewed for 4 months, take 8 months off, and are interviewed four …
Read more
Using Python to tackle the CPS (Part 3)
Source: datasframe | Author: Tom Augspurger | Published: May 19, 2014
In part 2 of this series, we set the stage to parse the data files themselves.
As a reminder, we have a dictionary that looks like
id length start end
0 HRHHID 15 1 15
1 HRMONTH 2 16 17
2 HRYEAR4 4 18 21
3 HURESPLI 2 22 23 …
Read more
Tidy Data in Action
Source: datasframe | Author: Tom Augspurger | Published: Mar 27, 2014
Hadley Whickham wrote a famous paper (for a certain definition of famous) about the importance of tidy data when doing data analysis.
I want to talk a bit about that, using an example from a StackOverflow post, with a solution using pandas. The principles of tidy data aren't language specific …
Read more
Organizing Papers
Source: datasframe | Author: Tom Augspurger | Published: Feb 13, 2014
As a graduate student, you read a lot of journal articles... a lot.
With the material in the articles being as difficult as it is, I didn't want to worry about organizing everything as well.
That's why I wrote this script to help (I may have also been procrastinating from …
Read more
Using Python to tackle the CPS (Part 2)
Source: datasframe | Author: Tom Augspurger | Published: Feb 04, 2014
Last time, we used Python to fetch some data from the Current Population Survey. Today, we'll work on parsing the files we just downloaded.
We downloaded two types of files last time:
CPS monthly tables: a fixed-width format text file with the actual data
Data Dictionaries: a text file describing …
Read more
Using Python to tackle the CPS
Source: datasframe | Author: Tom Augspurger | Published: Jan 27, 2014
The Current Population Survey is an important source of data for economists. It's modern form took shape in the 70's and unfortunately the data format and distribution shows its age. Some centers like IPUMS have attempted to put a nicer face on accessing the data, but they haven't done everything …
Read more


---
layout: default
title: Home
---
{% include index/home.html %}
{% include index/intro.html %}
{% include index/video.html %}
{% include index/mission_vision.html %}
{% include index/who.html %}
{% include index/events.html %}
{% include index/image_blocks.html %}
{% include index/donate.html %}
{% include index/image_break.html %}
{% include index/featured.html %}


{% extends "navbar2.html" %} {% block body %}
I'm currently employed as a software engineer. I have degrees in Mechanical Engineering from Carnegie Mellon University and Princeton University. My interests include data pipelines, automation and robotics.
Finally, in my free time I experiment with micro controllers and build out small web apps on the internet. If you want to learn more, download my resume or check out some of my featured projects.


Stock Master
Type of Stock
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Weight of Bones
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Other Options
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Full Method
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(a) Agency heads are encouraged to delegate micro-purchase authority (see 1.603-3).
(b) The Governmentwide commercial purchase card shall be the preferred method to purchase and to pay for micro-purchases (see 2.101).
(c) Purchases at or below the micro-purchase threshold may be conducted using any of the methods described in Subpart 13.3, provided the purchaser is authorized and trained, pursuant to agency procedures, to use those methods.
(d) Micro-purchases do not require provisions or clauses, except as provided at 13.202 and 32.1110. This paragraph takes precedence over any other FAR requirement to the contrary, but does not prohibit the use of any clause.
(e) The requirements in Part 8 apply to purchases at or below the micro-purchase threshold.
(f) The procurement requirements in subparts 23.1, 23.2, 23.4, and 23.7 apply to purchases at or below the micro-purchase threshold.
(g)(1) For acquisitions of supplies or services that, as determined by the head of the agency, are to be used to support a contingency operation or to facilitate defense against or recovery from nuclear, biological, chemical, or radiological attack, the micro-purchase threshold is —
(i) $20,000 in the case of any contract to be awarded and performed, or purchase to be made, inside the United States; and
(ii) $30,000 in the case of any contract to be awarded and performed, or purchase to be made, outside the United States.
(2) Purchases using this authority must have a clear and direct relationship to the support of a contingency operation or the defense against or recovery from nuclear, biological, chemical, or radiological attack.
(h) When using the Governmentwide commercial purchase card as a method of payment, purchases at or below the micro-purchase threshold are exempt from verification in the System for Award Management database as to whether the contractor has a delinquent debt subject to collection under the Treasury Offset Program (TOP).
Many supplies or services are acquired subject to supplier license agreements. These are particularly common in information technology acquisitions, but they may apply to any supply or service. For example, computer software and services delivered through the internet (web services) are often subject to license agreements, referred to as End User License Agreements (EULA), Terms of Service (TOS), or other similar legal instruments or agreements. Many of these agreements contain indemnification clauses that are inconsistent with Federal law and unenforceable, but which could create a violation of the Anti-Deficiency Act (31 U.S.C. 1341) if agreed to by the Government. The clause at 52.232-39, Unenforceability of Unauthorized Obligations, automatically applies to any micro-purchase, including those made with the Governmentwide purchase card. This clause prevents such violations of the Anti-Deficiency Act (31 U.S.C. 1341).
(a) Solicitation, evaluation of quotations, and award.
(1) To the extent practicable, micro-purchases shall be distributed equitably among qualified suppliers.
(2) Micro-purchases may be awarded without soliciting competitive quotations if the contracting officer or individual appointed in accordance with 1.603-3(b) considers the price to be reasonable.
(3) The administrative cost of verifying the reasonableness of the price for purchases may more than offset potential savings from detecting instances of overpricing. Therefore, action to verify price reasonableness need only be taken if—
(i) The contracting officer or individual appointed in accordance with 1.603-3(b) suspects or has information to indicate that the price may not be reasonable (e.g., comparison to the previous price paid or personal knowledge of the supply or service); or
(ii) Purchasing a supply or service for which no comparable pricing information is readily available (e.g., a supply or service that is not the same as, or is not similar to, other supplies or services that have recently been purchased on a competitive basis).
(b) Documentation. If competitive quotations were solicited and award was made to other than the low quoter, documentation to support the purchase may be limited to identification of the solicited concerns and an explanation for the award decision.


The first step in the wizard is to search for and find the APIs you wish to import. The location of this data is dependent on how Apiman is configured, but in most cases you will be searching against some sort of registry of real, deployed APIs.
| API | ID | Type | Category | Endpoint API | Actions |
|---|---|---|---|---|---|
| {{ api.name }} | {{ api.description }} | {{ api.endpointType | uppercase }} | {{ api.tags[0] }} | {{ api.endpoint }} | Add |
| API | ID | Type | Category | Endpoint API | Actions |
|---|---|---|---|---|---|
| {{ api.name }} | {{ api.description }} | {{ api.endpointType | uppercase }} | {{ api.tags[0] }} | {{ api.endpoint }} | Remove |
Once you have located the API(s) you wish to import, choose whether these APIs should be public or should be offered through one or more of the plans defined by the organization.
Finally confirm that you want to import the selected APIs. Once you click Import, each of the APIs you have chosen will be added to the organization. Success or failure of each API import will be shown as it happens.
Please verify that the APIs listed below really are the ones you would like to import. Once you are satisfied, click Import!
| API | ID | Type | Category | Endpoint API | |
|---|---|---|---|---|---|
| {{ api.name }} | {{ api.description }} | {{ api.endpointType | uppercase }} | {{ api.tags[0] }} | {{ api.endpoint }} |


Hope.
Strength.
Innovation.
Hi! I'm Roman Bernardo, which you probably knew already! I am an aspiring web developer, hoping to make waves one day. It is my dream to code the world into a better place. I am just getting started on this journey, but I have learned a lot, with a front-end development certificate from Free Code Camp, and I plan to continue this momentum and hone my skills as a programmer.


This last set of effects is purely experimental. Unlike the examples above, all of which use multiple prefixes to ensure maximum browser compatibility, these only use the -webkit prefix because there’s no other support at the moment
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While CSS reset files take
all page elements and reset
all their defaults to 0,
normalize. css targets only
specific elements that need
a reset.
While CSS reset files take
all page elements and reset
all their defaults to 0,
normalize. css targets only
specific elements that need
a reset.
While CSS reset files take
all page elements and reset
all their defaults to 0,
normalize. css targets only
specific elements that need
a reset.
While CSS reset files take
all page elements and reset
all their defaults to 0,
normalize. css targets only
specific elements that need
a reset.
What I’ve done here is set the morph class to spin 360 degrees on hover. As it’s spinning, the border-radius will gradually climb its way to 50%, resulting in a circle.
What I’ve done here is set the morph class to spin 360 degrees on hover. As it’s spinning, the border-radius will gradually climb its way to 50%, resulting in a circle.


이번 릴리즈에서 변경사항은 아래와 같습니다:
| English | |
| Brazilian Portuguese | |
| Chinese | |
| French | |
| German | |
| Greek | |
| Indonesian | |
| Japanese | |
| Polish | |
| Spanish |
| Introduction | the introduction to ZAP | |
| Releases | the full set of releases | |
| Credits | the people and groups who have made this release possible |


FARM TRAGEDY
Elderly Louth man crushed to death by bales of hay
by Tia ClarkeAn elderly Ardee man was crushed to death by two bales of silage last July, an inquest has heard.
75-year-old Peter Osborne of Flowery, Ballybonia, Ardee, Co Louth died whilst being treated at Tallaght hospital in Dublin on July 26 after suffering multiple injuries from the farming accident on July 16.
The Ardee man's son Patrick Osborne provided evidence of the formal identification of his father during the opening of an inquest which was held at Dublin Coroner's Court today.
The inquest also heard that following the tragic accident, a Health and Safety (HSA) investigation was launched.


WITNESS LIST Appropriations Committee February 28, 2001-7:00A Budget On: Antwi, Katherine (Texas Commission on Human Rights) Baldwin, Jr., Howard (Office of the Attorney General) Batiste, John Paul (Texas Commission on the Arts) Benedict, Jerry (Office of Court Administration) Cornyn, John (Attorney General) Cunningham, Walter (Aerospace Commission) Daniels, Jerry (Aircraft Pooling Board) Davis, Tommy (Texas Department of Public Safety) DiTucci, Frank (Texas Polygraph Examiners Board) Durham, Drew (Office of the Attorney General) Fabelo, Tony (Criminal Justice Policy Council) Gilman, Tony (Office of State-Federal Relations) Harrison, Tom (Texas Ethics Commission) Keith, Laura Ayoub (Texas Commission on Human Rights) Kifowit, Dee (Texas Department of Criminal Justice/TCOMI) Logan, Linda (Texas Animal Health Commission) McHugh, Colleen (Texas Department of Public Safety) McNutt, David (Texas Department of Criminal Justice) Nier, James (Texas Veterans Commission) Oaks, Larry (Texas Historical Commission) Phillips, Thomas (Office of Court Administration) Purcell, Carolyn (Department of Information Resources) Scott, Wayne (Texas Department of Criminal Justice) Taylor, Andy (Office of the Attorney General) Thrasher, Jr., J.W. (Texas River Compact Commission) Vogel, Peter (Office of Court Administration)


Mayca is the incredibly delicious Mexican Goat's Milk Spread
Based on a recipe that has been passed down through several generations and made only from the best wholesome natural ingredients, Mayca is Mexico at its best. Established as a Limited Corporation since 1992 due mainly at the insistence of family and friends who on numerous occasions envisioned a broader market for this product than just its Coahuila boundaries. After some years of just producing the Mayca product, the Mexican Coahuila Factory has now been creating other sweet products with a high standard quality based on a Northern Mexican Heritage but keeping always the same principles of using only natural and wholesome ingredients.
The use of the Mayca product is a toffee like dessert which can complement other food products such as bread, fruit or ice cream or it can be eaten by itself combined with pecan nuts or almonds. The manufactured product has a thick consistency and a brownish appearance. Incredibly delicious on toast, ice cream, fruit and by the spoonful mixed with nuts. Store in a cool, dry, dark place. Once opened, store in a refrigerator and use witthin one month.
In a medium saucepan over medium-low heat, melt sugar until liquefied and golden in color. Carefully pour hot syrup into a 9 inch round glass baking dish, turning the dish to evenly coat the bottom and sides. Set aside...
Recipe...
Recipe...
Recipe...
Recipe...
Recipe...
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts.


These release notes cover new developer and user-facing incompatibilities, important issues, features, and major improvements.
Very large blocks can fragment the heap and cause bad issues for the garbage collector, especially the G1GC. Now there is a maximum size that a block can be and still stick in the LruBlockCache. That size defaults to 16mb but can be controlled by changing "hbase.lru.max.block.size"
This issue resolves a potential security vulnerability. For all versions we update our commons-collections dependency to the release that fixes the reported vulnerability in that library. In 0.98 we additionally disable by default a feature of code carried from 0.94 for backwards compatibility that is not needed.
Adds a timeout to server read from clients. Adds new configs hbase.thrift.server.socket.read.timeout for setting read timeout on server socket in milliseconds. Default is 60000;
If the configured RPC controller factory (via hbase.rpc.controllerfactory.class) cannot be found in the classpath or loaded, we fall back to using the default RPC controller factory in HBase.
And 1.1, 1.0, and 0.98.
Adds column displaying current aggregated call queues size in region server queues tab UI.
FIFO compaction policy selects only files which have all cells expired. The column family MUST have non-default TTL. Essentially, FIFO compactor does only one job: collects expired store files.
Because we do not do any real compaction, we do not use CPU and IO (disk and network), we do not evict hot data from a block cache. The result: improved throughput and latency both write and read. See: https://github.com/facebook/rocksdb/wiki/FIFO-compaction-style
Adds being able to Scan each column family with a different time range. Adds new methods setColumnFamilyTimeRange and getColumnFamilyTimeRange to Scan.


--- layout: post title: Reformed Praise date: 2010-01-15 tags: - Music meta: pvc_views: "10501" views: "178" dsq_thread_id: "766776161" author: jcuenod permalink: /content/reformed-praise/ excerpt: "I've just discovered a new website - perhaps others are more familiar with it... I find that often my love for reformed theology is at a loss when it comes to music because there are very few artists who produce consistently good songs (both musically and lyrically). Sovereign Grace Music is one notable exception as I have enjoyed much of their music.\r \r" hidden: false featured: false ---
I've just discovered a new website - perhaps others are more familiar with it... I find that often my love for reformed theology is at a loss when it comes to music because there are very few artists who produce consistently good songs (both musically and lyrically). Sovereign Grace Music is one notable exception as I have enjoyed much of their music.
Anyway, http://www.reformedpraise.org is the site I am talking about. Their featured song is "The Gospel of Grace" - really cool!