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function forkjulia()
	r = ccall((:fork, "libc"), Int32, ())
	if r != 0
		# parent
		returnstatus=0
		ccall((:waitpid, "libc"), Int32, (Int32, Ptr{Int32}, Int32), r, &returnstatus, 0)
		println("forkjulia: return status: $returnstatus")
	end
end

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# Lazy quicksort.
# Can be used to take only the first k elements of the sorted array,
# And will do this 'efficiently'
lazysort{T}(lst::Vector{T}) =
@task begin
	if     length(lst) == 0   # is sorting trivial?
	elseif length(lst) == 1
		produce(lst[1])
	else
		# take pivot element

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# T: type of underlying data
# N: dimensionality of filter
type ConvolveFilter{T,N}
	fk::Array{Complex{T},N}
end

# apply a filter to data.
# If the dimensionality of the data (M) is larger than the filter (N),
# then apply the filter to the first N dimensions of the data.
*{T,N,M}(f::ConvolveFilter{T,N},x::Array{T,M}) = real(ifft(conj(f.fk) .* fft(x,1:N), 1:N))

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import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):

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from scipy import misc
from scipy.ndimage import filters
import numpy as np
import tensorflow as tf

#from inception import image_processing
from inception import inception_model as inception

#from PIL import Image
#from PIL import ImageFile

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# This code implements the model outlined in:
# "Design of conditions for emergence of self-replicators"
# by Sarkar, Wang, and England (2018)

# Reduced, one-atom model,
# using 'mechanistic' transition-state modeling 

using ChemicalReactionNetworks
using PyPlot 
#using ODE

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import ollama
import feedparser
import time
from datetime import datetime


sys_prompt = """
Your task is to judge whether an article title and abstract appeals to a specific researcher. This researcher specializes in computational modeling and analysis of complex biological systems, with particular focus on:
....

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import torch
import torch.distributed as dist

def main(rank, world_size):
    # Initialize distributed environment
    dist.init_process_group(backend='gloo', init_method='env://', rank=rank, world_size=world_size)
    
    # Determine neighbor ranks
    prev_rank = (rank - 1) % world_size
    next_rank = (rank + 1) % world_size