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[Project] Reproducing "An Embodied Approach for Evolving Robust Visual Classifiers"

Hello! This project will prepare you to reproduce my 2015 GECCO paper located here: https://github.com/notkarol/eatervc Although the code in this guide is different than in the GIT repository, you should be able to reproduce the same data. This experiment is similar to the Core1-10 projects off of Bullet 2.82 demos and will assume you have Core10 completed.
After each step please make sure your code compiles, runs, and matches any attached images!!!
1) Create robot's body and arms.
2) Connect the body parts with joints
3) Create a neural network.
4) Create the Python driver script and the fitness function.
5) Prepare the evolutionary algorithm code and evolve a robot that can categorize through proprioception (hugging ACP behaviour)
6) Adapt your code to do vision and scaffolds.
7) Implement scaffolds
8) Compare results
Full album: http://imgur.com/a/FqdU1
1.0) Find the setCameraDistance function in initPhysics(). Update it so that we can look directly in front of our robot.
 m_cameraTargetPosition = btVector3(0., 0., 10.); setCameraDistance(btScalar(20.)); setEle(btScalar(60.0)); 
1.1) Comment out all of myContactProcessedCallback()'s code except for "return false;" This will prevent possible segmentation faults in our next steps.
1.2) Comment out all of clientMoveAndDisplay()'s code in "if (m_dynamicsWorld)" so that the robot isn't being told to do any kind of movement. Verify that your robot is not walking around.
1.3) Comment out all of the existing hinge joints of the robot. Your robot should fall apart like in Core05.
1.4) Comment out all of the existing legs of the robot. Your robot should now just be the rectangular chassis.
1.5) Update center block of the robot becomes immovable and fixed to the ground. This is done by setting the mass of that bodypart to zero as part of your CreateBox function. Verify that when you start your robot, your mouse can not pick up and throw the center piece.
1.6) Continue to update the center block, through its CreateBox function, to the following values:
 Id: 0 Dimension xyz: 2.0000 1.0000 2.0000 Position xyz: 0.0000 1.0000 0.0000 Orientation xyz: 0.0000 0.0000 0.0000 Shape: Box Mass: 0.0000 Friction sliding, rolling: 1.0000 1.0000 
1.7) Create a new CreateCapsule function that will create capsule objects. This will likely look very different from your existing CreateCylinder. Primarily this is because we add damping and set the initial inertia of the object to allower for smoother behavior when the object is created.
 void RagdollDemo::CreateCapsule(int index, double x, double y, double z, double length, double radius, double anglex, double angley, double anglez, double mass, double friction, double rfriction) { geom[index] = new btCapsuleShape(radius, length); btVector3 pos = btVector3(x, y, z); btTransform offset_trans; offset_trans.setIdentity(); offset_trans.setOrigin(pos); offset_trans.getBasis().setEulerZYX(anglez, angley, anglex); btDefaultMotionState* myMotionState = new btDefaultMotionState(offset_trans); btVector3 localInertia(0,0,0); if (mass != 0.f) geom[index]->calculateLocalInertia(mass, localInertia); btRigidBody::btRigidBodyConstructionInfo rbInfo(mass, myMotionState, geom[index], localInertia); rbInfo.m_additionalDamping = true; body[index] = new btRigidBody(rbInfo); body[index]->setFriction(friction); body[index]->setRollingFriction(rfriction); m_dynamicsWorld->addRigidBody(body[index]); } 
1.8) Copy over the relevant code from CreateCapsule into CreateCylinder You might notice that the parameters are different for the btCylinderShape constructor and that the dimensions are reversed for girth and length.
1.9) Now add limbs using CreateCapsule (two segments per arm) with the following values:
Id: 1
 Dimension xyz: 16.0000 0.5000 0.0000 Position xyz: -8.5000 1.5000 2.5000 Orientation xyz: 0.0000 -1.5708 1.5708 Shape: Capsule Mass: 0.1333 Friction sliding, rolling: 1.0000 1.0000 
Id: 2
 Dimension xyz: 16.0000 0.5000 0.0000 Position xyz: 8.5000 2.5000 2.5000 Orientation xyz: 0.0000 1.5708 1.5708 Shape: Capsule Mass: 0.1333 Friction sliding, rolling: 1.0000 1.0000 
Id: 3
 Dimension xyz: 16.0000 0.5000 0.0000 Position xyz: -17.5000 1.5000 11.0000 Orientation xyz: 0.0000 0.0000 -1.5708 Shape: Capsule Mass: 0.1333 Friction sliding, rolling: 1.0000 1.0000 
Id: 4
 Dimension xyz: 16.0000 0.5000 0.0000 Position xyz: 17.5000 2.5000 11.0000 Orientation xyz: 0.0000 0.0000 -1.5708 Shape: Capsule Mass: 0.1333 Friction sliding, rolling: 1.0000 1.0000 
2.1) Create four new joints that have the following values:
HingeId: 0
 Id1 Id2: 1 0 Position: -0.5000 1.5000 2.5000 Orientation: 0.0000 1.0000 0.0000 Joint type: hinge Ang Min,Max: -3.1416 0.0000 
HingeId: 1
 Id1 Id2: 3 1 Position: -17.5000 1.5000 2.5000 Orientation: 0.0000 1.0000 0.0000 Joint type: hinge Ang Min,Max: -3.1416 0.0000 
HingeId: 2
 Id1 Id2: 0 2 Position: 0.5000 2.5000 2.5000 Orientation: 0.0000 1.0000 0.0000 Joint type: hinge Ang Min,Max: -3.1416 0.0000 
HingeId: 3
 Id1 Id2: 2 4 Position: 17.5000 2.5000 2.5000 Orientation: 0.0000 1.0000 0.0000 Joint type: hinge Ang Min,Max: -3.1416 0.0000 
2.2) Verify that you can move the arms closed or open with your mouse, but don't move in any other angle. Make sure that the joint limits. Are set and work to limit the movement.
 joints[0]->setLimit(-M_PI, 0); joints[1]->setLimit(-M_PI, 0); joints[2]->setLimit(-M_PI, 0); joints[3]->setLimit(-M_PI, 0); 
2.3) Update RagdollDemo.h to reflect the new number of bodyparts and joints:
 btRigidBody* body[5] btCollisionShape* geom[5] btHingeConstraint* joints[4] int IDs[6]; 
2.4) Inside of ClientMoveAndDisplay, just after stepSimulation (which should have a paramter of 0.1), test each of the joints using ActuateJoint2 (the one that uses enableAngularMotor) with a force of 2.0. First, make sure that the first joint can be moved to 45 degrees.
2.5) Then make sure it can be moved to -45 degrees as in the following image.
2.6) After following 2.4 and 2.5 for each of the joints individually, make sure that you can set the shoulder joints (0 and 2) to 60 degrees and the elbow joints (1 and 3) to -60 degrees to get the following image.
2.7) Let's make sure that the joint limits are working. Set the desired angle in the motors to 120 degrees. Verify that the robot's arms fold in on each other but are still limited.
2.8) Now that the robot is setup, we can add the cylinder that will be classified. Double check that you set rolling and sliding friction. Set the target angles to 60 for all joints. You should get the following image.
 Id: 5 Dimension xyz: 3.2000 2.0000 0.0000 Position xyz: 0.0000 1.0000 10.0000 Orientation xyz: 0.0000 0.0000 0.0000 Shape: Mass: 0.2 Friction sliding, rolling: 0.5000 1.0000 
3.1) In the previous assignments we used a neural network that directly mapped inputs to outputs, where the inputs were the 4 touch sensors and the outputs were the 8 motors of the legs. For this experiment we have 5 inputs (4 sensors and 1 bias neuron), five hidden neurons, and five outputs (4 motors and 1 guess neuron). To accomodate, comment out your weights array in RagdollDemo.h and replace it with the following six arrays. Make sure that you comment out all references to weights in RagdollDemo.cpp. Verify that your code still compiles.
 double weights_input[5][5]; double weights_hidden[5][5]; double weights_output[5][5]; double neurons_input[5]; double neurons_hidden[5]; double neurons_output[5]; 
3.3) Create two new function "void PrintNeurons();" that prints all neuron values and "void PrintWeights();" that prints out all the input, hidden, and output weights. I've included PrintNeurons as an example. PrintWeights should be something similar, except we have to iterate through all five subarrays of the input, hidden, and output weights.
 void RagdollDemo::PrintNeurons() { printf("neuron_input %6.3f %6.3f %6.3f %6.3f %6.3f\n", neuron_input[0], neuron_input[1], neuron_input[2], neuron_input[3], neuron_input[4]); printf("neuron_hidden %6.3f %6.3f %6.3f %6.3f %6.3f\n", neuron_hidden[0], neuron_hidden[1], neuron_hidden[2], neuron_hidden[3], neuron_hidden[4]); printf("neuron_output %6.3f %6.3f %6.3f %6.3f %6.3f\n", neuron_output[0], neuron_output[1], neuron_output[2], neuron_output[3], neuron_output[4]); } 
3.4) Initialize all the neurons with a value of 0.0 and weights to 0.1 in initPhysics. Verify that PrintNeuralNetwork prints out all zeros.
 std::fill_n(neurons_input, 5, 0.0); std::fill_n(neurons_hidden, 5, 0.0); std::fill_n(neurons_output, 5, 0.0); neurons_input[4] = 1.0; // bias for (int i = 0; i < 5; ++i) { std::fill_n(weights_input[i], 5, .1); std::fill_n(weights_hidden[i], 5, .1); std::fill_n(weights_output[i], 5, .1); } 
3.5) Create a new function UpdateNeurons() in RagdollDemo.cpp that updates the neural network. This function should update the values of the hidden and then the output neuron. Each hidden neuron's value is the error function applied to the sum of products of the input and hidden neurons and weights. For each hidden neuron i we iterate through each input neuron j to sum neurons_input[j] times weights_input[i][j] into a variable sum[i]. To this sum we iterate through each hidden neuron j to add to the sum neurons_hidden[j] times weights_hidden[i][j]. Then after we iterated through all the hidden neurons we set each hidden neuron value to erf(sum[i]). For the output layer we follow a simular process. We iterate through each output neuron i summing up, for each hidden neuron, the product of neurons_hidden[j] * weights_output[i][j. Then again, the value of the output neuron is erf(sum[i]).
3.6) Put this function into your clientMoveAndDisplay after stepSimulation. If it's working you should see the following values:
Initialized weights from initPhysics:
 neurons_input 0.000 0.000 0.000 0.000 1.000 neurons_hidden 0.000 0.000 0.000 0.000 0.000 neurons_output 0.000 0.000 0.000 0.000 0.000 
After 1 UpdateNeurons neurons_input 0.000 0.000 0.000 0.000 1.000 neurons_hidden 0.112 0.112 0.112 0.112 0.112 neurons_output 0.063 0.063 0.063 0.063 0.063
After 2 UpdateNeurons
 neurons_input 0.000 0.000 0.000 0.000 1.000 neurons_hidden 0.175 0.175 0.175 0.175 0.175 neurons_output 0.098 0.098 0.098 0.098 0.098 
After 3 UpdateNeurons
 neurons_input 0.000 0.000 0.000 0.000 1.000 neurons_hidden 0.209 0.209 0.209 0.209 0.209 neurons_output 0.118 0.118 0.118 0.118 0.118 
After 512 UpdateNeurons (At which point your program should close)
 neurons_input 0.000 0.000 0.000 0.000 1.000 neurons_hidden 0.249 0.249 0.249 0.249 0.249 neurons_output 0.140 0.140 0.140 0.140 0.140 
3.7) Connect your joints to the neural network by using the output neurons as the motor's desired angle. We multiply by 90 because the erf function has an output range of (-1,1).
 for (int i = 0; i < 4; ++i) ActuateJoint2(i, neurons_output[i] * 90., -90., time_step); 
3.8) In clientMoveAndDisplay right after stepSimulation(0.1) and right before UpdateNeurons() set each of our four non-bias (0-3) neuron inputs to the normalized joint angle. Your robot's arms should close up further than in the last image because now there is a slightly higher value feeding the circuit's input neurons. Also add new function for proprioception called CalculateProprioception(int joint) that will do the calculation.
double RagdollDemo::CalculateProprioception(int joint) { return (joints[i]->getHingeAngle() + M_PI_2) / M_PI_2; } 
3.9) One aspect of our experiments is that we don't want the last guess neuron value, but the average guess neuron value over the last 10% of timesteps. To be able to do so, add a double output_sum and int output_count variables to RagdollDemo.h. Be sure to initialize them to zero in initPhysics.
3.10) Then after UpdateNeurons() in clientMoveAndDisplay() add the value of neurons_output[4] to output_sum and increment output_count.
3.11) When the program exits on stepSimulation 512 print out output_sum divided by output_count, which will eventually correspond to the guess of the object size. Your robot's guess should be 0.248444.
[Fitness Function and Connecting Python]
4.0) You can either choose to work off of the Core10 runner file or make one from scratch. I'll assume you're working from a fresh python file which I've named runner.py. We'll need the following modules imported: sys, numpy as np, subprocess
4.1) First we'll need to prepare a basic framework for the script. Verify that it prints 0.0.
 def fitness(weights, positions, sizes): process = ['./AppRagdollDemo', '', '', ''] + list(map(lambda x: "%f" % x, weights)) guesses = [] errors = [] for size in sizes: process[1] = '%f' % size for x, z in positions: process[2] = '%f' % x process[3] = '%f' % z error = 0.0 guess = 0.0 errors.append(error) guesses.append(guess) return numpy.mean(errors) def main(): num_input_neurons = 5 num_hidden_neurons = 5 num_output_neurons = 5 num_synapses = (num_input_neurons * num_hidden_neurons + num_output_neurons) * num_hidden_neurons weights = np.random.rand(num_synapses) * 2 - 1 # initialized on [-1, 1) positions = [(6, 13), (-6, 13))] # initial positions of cylinder sizes = [2.4, 3.6] # radius values for cylinder that we try to learn print(fitness(weights, positions, sizes)) if __name__ == "__main__": sys.exit(main()) 
4.2) Unlike what you did for the previous assignments, you will be communicating to the C++ AppRagdollDemo through the command line and pipes rather than a temporary file. To do so we will first need to open a AppRagdollDemo processs. right before we set error and guess.
 proc = subprocess.Popen(process, stdout=subprocess.PIPE) 
4.3 Then we will need to let python know that we would like to get the output of the AppRagdollDemo process
 outs, errs = proc.communicate() 
4.4) Finally, we want to extract the guess neuron's value from the output of AppRagdollDemo to replace the "guess = 0.0" line.
 raw_guess = outs.decode().split("\n")[-2] guess = float(raw_guess) 
4.5) Now that we have our guess we can calculate our error based on the size. If the size is above average (we should only have two sizes) then compare the guess neuron to 0.5. If the size is average or below, then compare the guess neuron against -0.5. Remember to delete "error = 0.0"
 error = (0.5 - guess) if size > np.mean(sizes) else (-0.5 - guess) 
4.6) When you run the program, make sure that 0.5 is printed out. Why is it 0.5?
4.7) Go back to initPhysics in RagdollDemo.h and add two public variables "int argc" and "char** argv"
4.8) In main.cpp add "demoApp.argc = argc" and "demoApp.argv = argv"
4.9) Also, as a side step, we will want to allow for our program to either have the opengl window open or closed. First, add a "bool graphics;" to RagdollDemo.h. Then in main.cpp to do this, use an "if" statement that checks whether the first character of the second argument in argv is a 0.
 demoApp.graphics = argv[1][0] != '0'; demoApp.initPhysics(); if (!demoApp.graphics) while (true) demoApp.clientMoveAndDisplay(); # Replace this with your "return glutmain..." Feel free to play around with the window size and title! 
4.10) Then in RagdollDemo.cpp and RagdollDemo.h find every reference to "glClear" "debugDrawWorld" "renderme" "glFlush" and "glutSwapBufers" and put an "if (graphics)" before the line of code.
4.11) If you're on linux or a mac, test the speed differences of "time ./AppRagdollDemo 0" and "time ./AppRagdollDemo 1". For me it's 0.04 seconds versus 0.8 seconds, a 20x speedup!
4.12) Now that we passed the arguments to the RagdollDemo object, we can process it in initPhysics. The first step will be to create a position variable to keep track of which command line argument we are processing currently. The first one (position 0) is always the name of the program that's running. We will use the second argument to communicate whether or not our program will have a window open. Therefore, we start our counting at 2 for the arguments of the program. int pos = 2;
4.13) Our first command line value is the girth of the cylinder. The ++ after pos increments pos so that the next time the variable is used, it's value is one higher. double girth = atof(argv[pos++]);
4.14) Update CreateCylinder(5 ...) so that "girth" is used in place of the hard-coded cylinder cross-sectional width. Try running ./AppRagdollDemo with various numbers following it to see the cylinder change size. In this example I supplied a girth of 5.0, with the default X an Z values of the cylinder being 0 and 10 respectively.
4.15) Repeat steps 4.10 and 4.11 for the X and Z positions. The following image is for "./AppRagdollDemo 1 5.0 12.0 16.0"
4.16) For the next part you will need to continue calling atof(argv[pos++]) for each of the remaining 75 parameters and put them into neurons_input, neurons_hidden, and neurons_output. This is just two nested for loops, as the below example for weights_input shows. Do the same for weights_hidden and weight_output. Note, that if you do not supply all 78 requrired arguments, your program will likely crash with a Segmentation Fault. Also, be sure to comment out the code you created earlier to initialize the weights.
 for (int i = 0; i < 5; ++i) for (int j = 0; j < 5; ++j) weights_input[i][j] = atof(argv[pos++]); 
4.17) Try running ./AppRagdollDemo with the following settings. Verify that your output value is also 0.999593
 ./AppRagdollDemo 1 3.6 8.0 8.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 -.5 -.5 -.5 -.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 
4.18) Run runner.py and see how it works. You should see a variety of behaviour!
5.0) Use pip or another means to install the cma library for python. You can also copy the cma.py file into the same directory as your runner.py script. You can get cma and read more about it here: https://pypi.python.org/pypi/cma
5.1) Verify that cma is installed. At the top of runner.py do an "import cma"
5.2) CMA works by repeatedly running the fitness function while it figures out the weights of our network. Because it calls the fitness function as a callback, we need a way of keeping track of how far into an evolutionary run we are. We can do so by a clever use of numpy. Right after we initialized our weights variable initialize a variable "max_fevals" with a value of 100 and a then variable "results" as follows. results is a two dimensional array. Each row represents a fitness evaluation. The first position will be our fitness. The second position will be our -eventual- scaffolding parameter. The rest of the num_synapses positions will be the weights that were evolved for that fitness evaluation.
results = np.zeros((max_fevals, 2 + num_synapses), dtype=np.float64) results[:, 0] = -1 
5.3) Now update the fitness function to be able to take in our results. Add a parameter "results=None" that will by default be None when it is not specified.
5.4) At the top of the fitness function check to see if results is not None. If so, assume that we're evolving our weights and therefore want to save them to results. First, find our current position by finding the lowest fitness value. If that fitness value is negative, we have more evolving to do. If that fitness value is positive or zero, we're done so resturn a very bad fitness value.
if results is not None: feval = np.argmin(results[:, 0]) if results[feval, 0] >= 0: return 2.0 
5.5) Then at the bottom of the fitness function, right before the return, we'll want to store our guesses.
if results is not None: results[feval, 0] = np.mean(errors) results[feval, 2:] = weights return results[feval, 0] 
5.6) Test to see what happens when you run fitness with and without the results parameter on the random initial weights. Run this at the bottom of main().
print(results[0,:6]) print(fitness(weights, positions, sizes)) print(results[0,:6]) print(fitness(weights, positions, sizes, results)) print(results[0,:6]) for i in range(max_fevals): print(fitness(weights, positions, sizes, results)) return 0 
You should see something like the following. The first two arrays should be identical to mine's and the last array should have your repeated fitness value as its first argument and then differing-from-mine's weight values in the next four array positions. The last number printed should be a 2.0. This will ensure that our fitness function doesn't overwrite the lowest fitness values!!! If you do get something similar to the below, you can delete the above print() and for statements.
[-1. 0. 0. 0. 0. 0.] 0.49464025 [-1. 0. 0. 0. 0. 0.] 0.49464025 [ 0.49464025 0. 0.50167121 -0.97422892 -0.54256275 0.97009538] 0.49464025 0.49464025 ... 0.49464025 0.49464025 2.0 
5.7) Now we can add more parameters for CMA. First is a variable called "sigma" that we will initialize to 0.25 that the amount of standard deviation CMA-ES will initially vary our initial variable by. You can do this and the next step after you intiialized the results array.
5.8) Then we initialize a CMAOptions() variable that will help CMA-ES with our specific problem.
opts = cma.CMAOptions() opts['bounds'] = [-1, 1] # Limit our weights to the range [-1, 1] opts['ftarget'] = 0 # Set our goal as getting a fitness of 0 opts['maxfevals'] = max_fevals # Run until we evaluate the fitness function this many times opts['tolfun'] = 0.0 # Prevents CMA-ES from stopping too early if we don't have continuous improvement opts['tolfunhist'] = 0.0 # same as above opts['verb_log'] = 0 # Limit the amount of data printed to the screeen 
5.9) Then we can run the evolution!!!
es = cma.CMAEvolutionStrategy(weights, sigma, opts) es.optimize(fitness, args=[positions, sizes, results]) 
You should see something likethe following output:
(9_w,19)-aCMA-ES (mu_w=5.6,w_1=29%) in dimension 75 (seed=590627, Tue Sep 29 12:09:19 2015) Iterat #Fevals function value axis ratio sigma min&max std t[m:s] 1 19 4.893862500000000e-01 1.0e+00 2.42e-01 2e-01 2e-01 0:0.1 2 38 4.421325000000000e-01 1.0e+00 2.36e-01 2e-01 2e-01 0:0.2 3 57 4.496592500000000e-01 1.0e+00 2.31e-01 2e-01 2e-01 0:0.3 6 114 4.843342500000001e-01 1.0e+00 2.20e-01 2e-01 2e-01 0:0.5 
5.10) Unfortunately, our results at the moment should be quite bad. How do we know? A value of 0.5 or worse is basically random guessing. But let's plot it.
plt.plot(results[:, 0], '.') plt.axis([0, max_fevals + 1, 0, 1.0]) plt.xlabel('Evaluation') plt.ylabel('Fitness') plt.title('Best fitness: %.6f' % np.min(results[:, 0])) plt.savefig('results.png', bbox_inches='tight', dpi=100) plt.close() 
5.11) While we can run CMA-ES for longer, it'll take lots of time. So let's first concern ourselves by writing a test for the best evolved brain. First we'll need to generate a list of test positions right after where we first declared our positions.
test_positions = [(x, z) for x in np.linspace(-12, 12, 13) for z in np.linspace(8, 18, 6)] 
5.12) Then we'll need to test those positions on the best brain from the final 10% of evolution. It should be pretty close to 0.5 but take a lot of time to run (it's testing 168 positions!)
best_pos = np.argmin(results[max_fevals * 0.9:, 0]) + max_fevals * 0.9 test_fit = fitness(results[best_pos, 2:], test_positions, sizes) print(test_fit) print("./AppRagdollDemo 1 2.4 6 13", " ".join(list(map(lambda x: '%.6f' % x, results[best_pos, 2:])))) 
5.13) Now the moment of truth. Change the "max_fevals" variable to 10000. Do you get good results? Run the last printed line with different positions and sizes (either 2.4 or 3.6). How close are it's guesses to -0.5 and 0.5?
(8_w,16)-aCMA-ES (mu_w=4.8,w_1=32%) in dimension 75 (seed=806130, Thu Oct 1 18:08:06 2015)
Iterat #Fevals function value axis ratio sigma min&max std t[m:s]
1 16 4.980917572975159e-01 1.0e+00 2.39e-01 2e-01 2e-01 0:0.1 2 32 4.999890029430389e-01 1.0e+00 2.31e-01 2e-01 2e-01 0:0.2 3 48 4.999679923057556e-01 1.1e+00 2.26e-01 2e-01 2e-01 0:0.3 
100 1600 6.932575255632401e-02 1.6e+00 2.33e-01 2e-01 2e-01 0:10.0
200 3200 3.009220063686371e-01 1.8e+00 2.16e-01 2e-01 2e-01 0:20.3
300 4800 6.651824712753296e-02 1.9e+00 1.21e-01 1e-01 1e-01 0:31.0
400 6400 9.923399984836578e-02 2.0e+00 9.44e-02 8e-02 1e-01 0:42.0
500 8000 2.363199926912785e-02 2.3e+00 7.46e-02 7e-02 8e-02 0:52.9
600 9600 2.266325056552887e-02 2.7e+00 6.48e-02 6e-02 7e-02 1:3.9
626 10016 2.000000000000000e+00 2.8e+00 6.90e-02 6e-02 8e-02 1:6.6
./AppRagdollDemo 1 P 0.0 2.4 6 13 -0.111434 0.059474 0.380173 -0.987895 0.067203 0.738417 0.743121 -0.210064 0.880601 0.049765 -0.999996 -0.891706 -0.751089 -0.128191 0.954568 0.885107 0.297995 -0.274674 0.149991 -0.998936 -0.668069 -0.922515 -0.175289 -0.401415 -0.781272 0.553520 -0.982237 -0.811084 -0.038278 0.985526 -0.242963 0.262981 -0.995365 -0.527623 -0.104670 -0.815820 0.936519 0.991092 0.935621 0.366035 0.950837 -0.137820 -0.665993 -0.298201 0.975300 -0.361183 0.499567 -0.091435 -0.986124 -0.404100 -0.206669 0.962506 -0.016370 -0.979014 -0.932418 -0.984638 0.085348 -0.474785 -0.704432 0.004024 -0.016271 0.933038 -0.074986 -0.961675 0.916290 -0.050167 0.892390 0.873856 -0.601724 0.786095 -0.179949 -0.934628 0.477538 -0.859568 -0.155424
5.14) Congratulations! You have the proprioceptive robot all done!
6.1) Right now our parameters for AppRagdollDemo only accept graphics, size, position, and weights. Let's add two more parameters: sensor and scaffold. Add a "char sensor;" and a "double scaffold;" to RagdollDemo.h
6.2) Then navigage to RagdollDemo.cpp's initPhysics() and right after into pos, but before girth add the following:
sensor = argv[pos++][0]; scaffold = atof(argv[pos++]); 
6.3) Modify the ./AppRagdollDemo command I had from my results in 5.13 to add the two additional parameters. We'll use 'P' for proprioception and a scaffold value of 0. Make sure that the command has the same behavior as before.
6.4) Now we're ready to handle different sensor modalities. Navigate over to clientMoveAndDisplay and add if statements based on the sensor where you set neurons_input. If it's 'P' your robot should use the existing code to set neuron_inputs to joint angles. If it's 'V' then set neuron_inputs to '0.' Does your robot's behavior drastically change?
6.5) Now we're going to be creating a vision system so let's first create a way of drawing lines to tell us what the vision system might be doing. Test this function for different sources, destinations, and colors to get a feel of what it does.
void RagdollDemo::draw_line(btVector3 src, btVector3 dst, btVector3 color) { glLineWidth(5.0); glColor3f(color.x(), color.y(), color.z()); glBegin(GL_LINES); glVertex3f(src.x(), src.y(), src.z()); glVertex3f(dst.x(), dst.y(), dst.z()); glEnd(); } 
6.5) Now let's add a new function CalculateVision to RagdollDemo.cpp and RagdollDemo.h and for the 'V' sensor set
double CalculateVision(int rays, double radius, double spread, btVector3 src, double azi, double zen) { int count = 1; double distance = radius; btVector3 dst = btVector3(src.x() + radius * sin(zen) * sin(azi), src.y() + radius * cos(zen), src.z() - radius * sin(zen) * cos(azi)); #TO BE FILLED IN THE NEXT STEPS return distance / radius / count * 2. - 1.; } 
6.6) The first step of filling out CalculateVision will be to cast a ray from the source to destination and to check whether it's hit anyting. While your rays won't likely be in the same positions, make sure that when the object is in front of the ray that it changes color.
 btCollisionWorld::ClosestRayResultCallback RayCallback(src, dst); m_dynamicsWorld->rayTest(src, dst, RayCallback); if (RayCallback.hasHit()) { distance = 0.0; if (graphics) draw_line(src, dst, btVector3(0.0, 0.0, 0.0)); } else if (graphics) draw_line(src, dst, btVector3(0.75, 0.75, 0.75)); 
6.7) Update distance in the code above to use RayCallback.m_hitPointWorld to calculate the distance from src to collision. Then make sure that further objects lead to higher distance values.
6.8) Then create a function to cast additional rays away from the center ray.
for (int i = 1; i < rays; ++i) { ++count; dst.setX(src.x() + radius * sin(zen) * sin(azi + pow(-1, i) * ((int) ((i + 1) / 2)) * spread)); dst.setY(src.y() + radius * cos(zen)); dst.setZ(src.z() - radius * sin(zen) * cos(azi + pow(-1, i) * ((int) ((i + 1) / 2)) * spread)); # Cast a ray and check whether it hit and add to distance } 
6.9) Play around with the parameters of neuron_inputs until you a fan of rays that looks like mine's. Each ray is separated by 5 degrees, with
neurons_input[i] = CalculateSussex(9, 30., ?, btVector3(0, 3.5, 0), ?, ?); 
6.10) Once you've added your rays and it seems to work, put in the limitation of 512 steps and update your runner.py! First let's updat the fitness() function to accept a sensor parameter before results. Then after results add a "scaffold=1.0" parameter. In the "process" list add sensor and scaffold after the '0'. Make sure every item in "process" is of type 'str'!
6.11) Now update main() to take in the sensor from the command line.
sensor = sys.argv[1] 
6.12) Make sure your es.optimize() includes the sensor parameter, as does the test_fit calculation.
6.13) Now evolve your vision! In my case, the first run didn't do so well on the test. It memorized by flinging the objects over the far left eye! Notice that the plot should show a lower tendency to have fitness values cluster around 0.5. Why?
(8_w,16)-aCMA-ES (mu_w=4.8,w_1=32%) in dimension 75 (seed=807489, Thu Oct 1 17:35:03 2015)
./AppRagdollDemo 1 V 0.0 2.4 6 13 0.103191 0.033281 -0.538811 0.497204 0.069158 -0.324142 0.493537 -0.833189 -0.952445 -0.978045 -0.326805 -0.242170 0.501297 -0.989718 -0.096023 -0.985875 0.974242 0.840394 -0.953507 0.222232 -0.518375 0.115300 -0.626326 -0.697763 -0.860402 -0.993152 -0.169275 0.184869 0.164260 0.276915 0.925261 0.153285 0.970203 0.738156 0.496861 -0.173943 -0.072006 0.653874 0.998129 0.978686 0.482530 -0.999319 -0.078337 0.902155 0.972387 0.832175 -0.182369 -0.437196 -0.095860 0.648740 -0.998240 0.693114 -0.935537 0.210072 -0.974007 -0.341692 -0.983070 -0.372232 0.073482 -0.371810 -0.586527 0.268161 0.431054 -0.383495 0.987521 -0.999942 0.841365 -0.903312 -0.976890 0.907322 0.385058 -0.878975 -0.145806 0.503977 0.993508
7.1) One thing that we haven't done yet is save our results for comparisons. Import pickle at the top of runner.py and then throw this line at the end of results. This will save our results and test_fit in a serialized file that you will be able to load later for review. We use the sensor and seed as a way of uniquely identifying each results file.
with open("results_%s_%07i.pkl" % (sensor, es.opts['seed']), 'wb') as f: pickle.dump((results, test_fit), f) 
7.2) Also, you might want to do the same kind of file convention for the plt.savefig() png file name.
7.3) Inside runner.py we currently send 0.0 as the scaffold value. For this guide we'll create two scaffold functions that we used in the paper. First is the simple scaffold, which is 1 if we're halfway through our evolution.
def simple_scaffold(feval, num_eval): return feval >= num_eval // 2: 
7.4) The next is our more gradual scaffold. We'll normalize feval's fraction to the range of 0 to 1 from 0.3 to 0.9
def gradual_scaffold(feval, num_eval): if feval < num_eval * 0.3: return 0.0 if feval >= num_eval * 0.9: return 1.0 return (feval / num_eval - 0.3) * 3. / 2. 
7.5) In the fitness function's first "if results is not None:" check the value of sensor. If it's N, then scaffold variable should be the return of simple_scaffold. If the sensor is S, X, or C, then it should be the gradual scaffold.
7.6) Test the function by setting max_fevals to 10 and printing the scaffold value for every fitness evaluation when the sensor is N. The first half of the results should be 0 and the second half 1.
0 0 0 0 0 1 1 1 1 1
7.7) Test the same as 7.7 for a scaffold of S, X, and C. Comment out the print when you're done and verified these numbers.
0.0 0.0 0.0 0.0 0.15 0.3 0.45 0.6 0.75 1.0
7.8) Now that we know that everything on the python side of things is set up, let's try putting in the scaffolds into RagdollDemo. For each of the next three scaffolds create a new function in both RagdollDemo.h and RagdollDemo.cpp. They shall each accept proprioception as the first parameter and vision as the second parameter. In my case I have a class variable named "step" that keeps track of timesteps, starting with 0, and a variable called "max_steps" that is set to 512. Familiarize yourself with these pictures to get an intuitive grasp of the scaffolds.
7.9) Create a Swapping scaffold that swaps from P to V linearly earlier in the simulation as the scaffold increases. So at a scaffold of 0.0 it should return the value of "p" and at a scaffold of 0.5 it should return the value of "p" for the first 256 timesteps and the value of "v" for the last 256 timesteps. When scaffold is 1.0 then the value of "V" shoul;d be returned at every timestep.
double RagdollDemo::CalculateS(double p, double v) { return ???; } 
7.10) Create a X (averaging) scaffold that averages the P and V using the scaffold percent of V and (1 - scaffold) percnet of P.
double RagdollDemo::CalculateX(double p, double v) { return ???; } 
7.11) Create the C (crossmodal) scaffold. It's kind of hard to explain, but as the scaffolding value increases the intersection point moves earlier into the simulation until it's entirely vision. Here's the cod.
double RagdollDemo::CalculateC(double sensor1, double sensor2) { double frac = erf((1.0 * step / max_step + scaffold * 2 - 1) * 4 - 2) * 0.5 + 0.5; return frac * sensor2 + (1 - frac) * sensor1; } 
7.12) Let's test your functions to see how they work! Let's create a "double TestScaffold(char scaf);" that takes in the sensor (scaffold) and runs it on some values. Make sure to also include it in RagdollDemo.h
void RagdollDemo::TestScaffold(char scaf) { printf("Test%c", scaf); for (step = 0; step <= max_step; ++step) printf(" %2li", step); printf("\n"); for (scaffold = 0; scaffold <= 1.0; scaffold += 0.25) { printf("%.2f:", scaffold); for (step = 0; step <= max_step; ++step) { switch(scaf) { case 'S': printf(" %.3f", CalculateS(0., 1.)); break; case 'X': printf(" %.3f", CalculateX(0., 1.)); break; case 'C': printf(" %.3f", CalculateC(0., 1.)); break; } } printf("\n"); } } 
7.13) At the top of initPhysics add the following code:
max_step = 10; TestScaffold('S'); TestScaffold('X'); TestScaffold('C'); exit(0); 
Do you get these values? If so, you're done! Comment out the above code!
TestS 0 1 2 3 4 5 6 7 8 9 10 0.00: 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.25: 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.000 1.000 0.50: 0.000 0.000 0.000 0.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 0.75: 0.000 0.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.00: 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 TestX 0 1 2 3 4 5 6 7 8 9 10 0.00: 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.25: 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.250 0.50: 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.75: 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 1.00: 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 TestC 0 1 2 3 4 5 6 7 8 9 10 0.00: 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.25: 0.000 0.000 0.000 0.000 0.000 0.002 0.012 0.045 0.129 0.286 0.500 0.50: 0.002 0.012 0.045 0.129 0.286 0.500 0.714 0.871 0.955 0.988 0.998 0.75: 0.500 0.714 0.871 0.955 0.988 0.998 1.000 1.000 1.000 1.000 1.000 1.00: 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 
7.14) Make sure ClientMoveAndDisplay also handles the scaffolds, for example:
neurons_input[i] = CalculateX(CalculateProprioception(i), CalculateVision(rays, radius, spread, src, ((i * 2) - 3) * M_PI / 8, zen)); 
7.15) You're done! Run a bunch of runs using the P, V, N, S, X, C on various initial positions. You might want to move your runner.py file into different directories, one for each set of experiment positions or other parameters!
Let me know if there's any issues!
submitted by notkarol to ludobots

Veganism Info

This was originally a post made for a thread claiming B12 couldn't be found in plant sources, and that veganism was impossible 100 years ago(among other things). Feel free to re-format, edit, add, remove, or share as you see fit; copy-pasta it all across reddit until people understand this issue isn't based around opinions, but science and history.
Please try to only add reliably sourced links, and let the science do the talking. I've seen quite a few posts in many threads along the lines of, 'An omnivore diet is probably healthier, but I don't want to eat animals,' or 'It's true you can't get B12, but I'll just take a supplement.'

You don't need to give in to the incessant noise and peer-pressure of the meat-eaters; You are right, they are wrong.

This absolutely could be made simpler, more elegant, and more approachable. The protein link also has a fair amount on the dangers of red meat that should probably be more well known, though the original post was focused more on meat vs non-meat as a whole.

Also, not mentioned is that many great scientists and scholars were vegetarians(and possibly vegans) in a time before factory farming and a lot of the more inhumane ways of procuring animal products such as dairy, eggs, and honey. Though this is just currently speculation, as I'm not bothering to source anything.

"" A survey of naturally occurring and high Vitamin B12-containing plant-derived food sources showed that nori, which is formed into a sheet and dried, is the most suitable Vitamin B12 source for vegetarians presently available. Consumption of approximately 4 g of dried purple laver (Vitamin B12 content: 77.6 μg /100 g dry weight) supplies the RDA of 2.4 μg/day. In Japan, several sheets of nori (9 × 3 cm2; approximately 0.3 g each) are often served for breakfast. A large amount of nori can be consumed as certain forms of sushi (vinegared rice rolled in nori). In particular, hand-rolled sushi made by wrapping rice and fillings with nori is easy to prepare and facilitates the consumption of a large amount of nori. When dried purple laver was treated by toasting until the laver’s color changed from purple to green, the toasting treatment did not affect the Vitamin B12 contents [57]. Dried purple lavers could also be a suitable food item for integration in Italian, French, and other forms of western cuisine. Dried purple laver is also a rich source of iron and n-3 polyunsaturated fatty acids (Figure 4). Dried purple laver is a natural plant product; therefore, it is suitable for most vegetarian groups. Among edible mushrooms, relatively high levels of Vitamin B12 were detected in the commercially available shiitake mushroom fruiting bodies, but the Vitamin B12 content significantly varies (1.3–12.7 μg/100 g dry weight), which is significantly lower than that found in dried purple laver. However, the dried shiitake mushroom fruiting bodies (per 100 g) contain 18.9 mg of Vitamin D2 (ergocalciferol) and 2.0 mg of iron [63], which are also nutrients that vegetarian diets tend to lack. Thus, the use of these plant-based food sources can significantly improve the nutrient imbalance in vegetarian diets to reduce the incidence of Vitamin B12 deficiency in vegetarians. "
" To clarify the bioavailability of vitamin B12 in lyophylized purple laver (nori; Porphyra yezoensis), total vitamin B12 and vitamin B12 analogue contents in the laver were determined, and the effects of feeding the laver to vitamin B12-deficient rats were investigated. The amount of total vitamin B12 in the dried purple laver was estimated to be 54.5 and 58.6 (se 5.3 and 7.5 respectively) microg/100 g dry weight by Lactobacillus bioassay and chemiluminescent assay with hog intrinsic factor respectively. The purple laver contained five types of biologically active vitamin B12 compounds (cyano-, hydroxo-, sulfito-, adenosyl- and methylcobalamin), in which the vitamin B12 coezymes (adenosyl- and methylcobalamin) comprised about 60 % of the total vitamin B12. When 9-week-old vitamin B12-deficient rats, which excreted substantial amounts of methylmalonic acid (71.7(se 20.2) micromol/d) in urine, were fed the diet supplemented with dried purple laver (10 microg/kg diet) for 20 d, urinary methylmalonic acid excretion (as an index of vitamin B12 deficiency) became undetectable and hepatic vitamin B12 (especially adenosylcobalamin) levels were significantly increased. These results indicate that vitamin B12 in dried purple laver is bioavailable to rats. "
" Cultivation of laver as food is thought to be very ancient, though the first mention was in Camden's Britannia in the early 17th century.[7]#citenote-7) It is plucked from the rocks and given a preliminary rinse in clear water. The collected laver is repeatedly washed to remove sand and boiled for hours until it becomes a stiff, green mush.[[8]](https://en.wikipedia.org/wiki/Laver(seaweed)#cite_note-8) In this state, the laver can be preserved for about a week. Typically during the 18th century, the mush was packed into a crock) and sold as "potted laver"."
“...your body is capable of storing several years' worth of vitamin B-12, deficiency is rare. “
" Finally, and very surprisingly, certain plants were observed to be able to absorb B12 from their growth medium, accumulating the molecule in the leaf vacuole. This latter finding may be important as a way to address the global challenge of providing a nutrient-complete vegetarian diet, a valuable development as the world becomes increasingly meat-free due to population expansion. "
" Good sources of iron
Food Portion Iron content (milligrams)
Fortified cold breakfast cereal 3 ounces 30 to 60
Spirulina seaweed 3 ounces 28
Oysters 3 ounces 9
Soybeans, cooked 1 cup 9
Cream of Wheat 1 serving 9
Pumpkin seeds 3 ounces 8
Spinach, boiled and drained 1 cup 7
Lentils, cooked 1 cup 7
Soybeans, cooked 1 cup 5
Kidney beans, cooked 1 cup 4
Beef, ground 4 ounces 3
Turkey, ground 4 ounces 3 Source: USDA National Nutrient Database for Standard Reference"
" While calcium and dairy can lower the risk of osteoporosis and colon cancer, high intake can increase the risk of prostate cancer and possibly ovarian cancer.
Plus, dairy products can be high in saturated fat as well as retinol (vitamin A), which at high levels can paradoxically weaken bones.
Good, non-dairy sources of calcium include collards, bok choy, fortified soy milk, baked beans, and supplements that contain both calcium and vitamin D (a better choice than taking calcium alone).
Read the full article on calcium and milk.

5 Quick Tips: Building Strong Bones

1. Look beyond the dairy aisle. Limit milk and dairy foods to no more than one to two servings per day. More won’t necessarily do your bones any good—and less is fine, as long as you get enough calcium from other sources. Calcium-rich non-dairy foods include leafy green vegetables and broccoli, both of which are also great sources of vitamin K, another key nutrient for bone health. Beans and tofu can also supply calcium.
2. Get your vitamin D. Vitamin D plays a key role along with calcium in boosting bone health. Look for a multivitamin that supplies 1,000 IU of vitamin D per day. If your multi only has 400 IU of vitamin D, consider taking an extra supplement to get you up to 1,000 IU or 2,000 IU per day. Some people may need 3,000 or 4,000 IU per day for adequate blood levels, particularly if they have darker skin, spend winters in the northern U.S., or have little exposure to direct sunlight. If you fall into these groups, ask your physician to order a blood test for vitamin D. Read more about vitamin D in the vitamins section of The Nutrition Source.
3. Get active. Regular exercise, especially weight-bearing exercise such as walking or jogging, is an essential part of building and maintaining strong bones.
4. Be careful about getting too much retinol (vitamin A). Don’t go overboard on fortified milk, energy bars, and breakfast cereals, all of which can be high in bone-weakening vitamin A. Many multivitamin makers have removed much or all retinol and replaced it with beta-carotene, which does not harm bones.
5. Help your kids build strong bones. Youth and young adulthood is the period when bones build up to their peak strength. Helping youth lead a bone-healthy lifestyle—with exercise, adequate calcium, and adequate vitamin D—can help them keep strong bones through all their adult years.
Read why the milk and dairy recommendation on Harvard’s Healthy Eating Plate differs from that of the U.S. Government’s MyPlate."
" In terms of the amount of protein consumed, there’s evidence that eating a high-protein diet may be beneficial for the heart, as long as the protein comes from a healthy source.

  • A 20-year prospective study of over 80,000 women found that those who ate low-carbohydrate diets that were high in vegetable sources of fat and protein had a 30 percent lower risk of heart disease compared with women who ate high-carbohydrate, low-fat diets. Diets were given low-carbohydrate scores based on their intake of fat, protein, and carbohydrates. (9) However, eating a low-carbohydrate diet high in animal fat or protein did not offer such protection...
  • More evidence that protein quality matters comes from a 20-year study that looked at the relationship between low-carbohydrate diets and type 2 diabetes in women. Low-carbohydrate diets that were high in vegetable sources of fat and protein modestly reduced the risk of type 2 diabetes. (13) But low-carbohydrate diets that were high in animal sources of protein or fat did not show this benefit...
  • People should aim to reduce overall consumption of red meat and processed meat, but when you do opt to have it, go easy on the grill. High-temperature grilling creates potentially cancer-causing compounds in meat, including polycyclic aromatic hydrocarbons and heterocyclic amines. You don’t have to stop grilling, but try these tips for healthy grilling from the American Institute of Cancer Research: Marinate meat before grilling it, partially pre-cook meat in the oven or microwave to reduce time on the grill, and grill over a low flame...
  • One study showed that eating approximately one daily serving of beans, chickpeas, lentils or peas can increase fullness, which may lead to better weight management and weight loss. (21)
There’s no need to go overboard on protein. Though some studies show benefits of high-protein, low-carbohydrate diets in the short term, avoiding fruits and whole grains means missing out on healthful fiber, vitamins, minerals, and other phytonutrients."
"The simplest way to think about why meat produces more emissions is in terms of the efficiency of converting grain to edible meat. Or even more, plant protein to animal protein.
Efficiencies of Animal Food Production Milk Chicken Pork Beef lbs. grain/ lb. live weight 1.0 2.5 4.0 8.0 Percent edible weight 95% 55% 55% 40% lbs. feed / lb. edible weight 1.1 4.5 7.3 20.0 plant-animal protein conversion - 20% 10% 4%
Let me shed some light on this nifty table. The top row is the amount of grain required to produce a pound of raw animal (or milk). Percent edible weight, well, that is self-explanatory. The third row shows how much grain is required to produce one pound of edible meat. The fourth row is the efficiency with which plant protein is converted to animal protein.
What does it all mean? Well, for one thing, this explains why the U.S. livestock population consumes more than seven times as much grain as is consumed directly by the entire American population (Pimentel 2003). All the fertilizer, tractor fumes, transportation, ground tilling, you name it, that go into producing a pound of grain are literally multiplied by 4.5 to produce a pound of chicken breasts, or by 20 to produce a pound of ground beef.
The greenhouse steak
I know I keep picking on beef, but there's another huge problem with our favorite bovines. When you look at the full lifecycle of cattle, the emissions from producing the feed are only a small portion of the total greenhouse gas output.
The matter is raised in the table below, and delicately labeled "biological activity." In more direct terms, cattle emit a lot more gas than chickens or pigs do, and all those cow farts really add up...
Water use and biodiversity
Even though climate change is the hot topic du jour, it not the only thing we should be thinking about. The world is moving towards increasing problems with freshwater scarcity, and we are in an era of unprecedented threats to biodiversity. I won't dwell on these topics, which are each worthy of their own column, but here are a few things to chew on:
Agriculture irrigation accounts for 85 percent of the fresh water use in the United States.
Producing one pound of animal protein requires 100 times more water than producing one pound of grain protein.
According to the Food and Agriculture Association (FAO), "livestock may well be the leading player in the reduction of biodiversity, since it is the major driver of deforestation, as well as one of the leading drivers of land degradation, pollution, climate change, overfishing, sedimentation of coastal areas and facilitation of invasions by alien species."
Going vegetarian is not enough to save the world
It may seem obvious, but eating tofu while still driving a Hummer is not going to help us solve any of our problems. While reducing our meat consumption would be a good idea for our planet and our bodies, I also want to offer a bit of perspective.
For one thing, those of us who happen to live in North America are not the only ones who want to eat more meat. Global meat production is expected to more than double between 2000 to 2050, due in large part to rising demand in China and other developing nations.
Another thing to keep in mind is that eating a typical lacto-ovo vegetarian diet (vegetarian with dairy and eggs), is not the be-all, end-all of fossil fuel reduction. Cheese and yogurt especially require significant amounts of energy, while chicken is not all that bad in terms of GHG emissions
Where that food come from matters a great deal, as well. Eating a grass-fed cow from down the road or a chicken raised in your backyard is significantly different from eating a McDonald's burger that originated in Brazil.
For all the complexities, the bottom line is clear: There are all sorts of things that we can and should think about when it comes to changing our behavior to reduce fossil fuel emissions. And our plates are great place to start."
Bonus Links
Chocolate Pudding Fruit
“...assumes a flavor, color and texture often likened to chocolate pudding when ripe.”
Banana Mango Custard Fruit
“Pawpaw fruits have a sweet, custardish flavor somewhat similar to banana, mango, and cantaloupe,[3][5][28] varying significantly by source or cultivar,[3] with more protein than most fruits.[3] Nineteenth-century American agronomist E. Lewis Sturtevant described pawpaws as
... a natural custard, too luscious for the relish of most people[21]
Ohio botanist William B. Werthner noted that
The fruit ... has a tangy wild-wood flavor peculiarly its own. It is sweet, yet rather cloying to the taste and a wee bit puckery – only a boy can eat more than one at a time.[5]
Fresh fruits of the pawpaw are commonly eaten raw, either chilled or at room temperature. However, they can be kept only 2–3 days at room temperature, or about a week if refrigerated.[14] The easily bruised pawpaw fruits do not ship well unless frozen.[3][26] Where pawpaws grow, the fruit pulp is also often used locally in baked dessert recipes, with pawpaw often substituted with volumetric equivalency in many banana-based recipes. Pawpaws may also be blended into ice cream[14] or included in pancakes.[14]
Due to its potential for allergic reaction causing contact dermatitis and possible presence of pesticides,[29] pawpaw consumption may be harmful to humans.[30][31]


According to a report from the Kentucky State University Pawpaw Program (right table), raw pawpaw (with skin) in a 100 gram amount provides 80 Calories and is a rich source (20% or more of the Daily Value, DV) of vitamin C (22% DV), magnesium (32% DV), iron (54% DV) and manganese (124% DV). The fruit also contains a moderate amount (10–19% DV) of vitamin A (11% DV).


Zebra swallowtail butterflies (Eurytides marcellus) with pawpaw foliage
Phytochemical extracts of the leaves and fruit contain acetogenins, including the neurotoxin annonacin.[31] The seeds and bark contain the chemical asimitrin[32] and other acetogenins, including asimin, asiminacin and asiminecin.[31][33]

Effect on insects

Due to the presence of acetogenins, the leaves, twigs, and bark of pawpaw trees can be used to make an organic insecticide.[19] The one notable exception is the zebra swallowtail butterfly (Eurytides marcellus), whose larvae feed on the leaves of various species of Asimina, conferring protection from predation throughout the butterfly's life, as trace amounts of acetogenins remain present, making them unpalatable to birds and other predators.[22]

Edit: "...referring to a diet fad called “protein combining” that became popular in the 1970s. It was based on the premise that vegetarian and vegan diets provide insufficient content of essential amino acids, making it necessary to combine plant-based proteins to get the same “complete” protein you’d get from an animal. Protein combining has since been discredited by the medical community, but there are still people out there who adhere to this practice, and even more people who still believe plant-based protein is incomplete."
submitted by RedditBetween_Lines to vegan

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