Very neat lectures by Feynman on electricity: https://www.feynmanlectures.caltech.edu/II_01.html#Ch1-S1
This morning for some reason I started to think about making a manufacturing company in the US. I was thinking about how we import so much from other countries that wouldn’t it be neat if from raw materials we build something that people would buy. So, the thought process was then like oh boy, what resources do we have that we can actually produce things that are sustainable.
Trash! Obviously this is something we have in abundance. However, the stuff that gets sent to landfills is not being used well. It just sits there… Some companies are “up-cycling” (taking things like plastic bottles and producing a more expensive product) plastic bottles and producing shoes and hand bags (https://www.veja-store.com/en_uk/c/men).
But, what about all the trash in land fills. What can we do with that? So, I googled and found:
Very neat website that has a lot of useful info. It seems like we burn a lot of the stuff that ends up in landfills and this creates electricity by using the heat to turn a generator. This also reduces the mass and volume of the waste. However, you are still producing CO2. Which is not as bad as methane in terms of green house gas, but is still not ideal.
I also found that landfills also produce biogas: https://www.eia.gov/energyexplained/biomass/landfill-gas-and-biogas.php
Gasses such as methane and carbon dioxide (CO2) are considered “biogases”. These gases can be burned to produce electricity. Not only landfills are doing this, but also farms. The farms collect manure in “manure lagoons” and the methane captured from these ponds is used to produce electricty.
Biomass such as organic solid waste, crop residue, etc. can be converted to hydrogen and CO2: https://www.energy.gov/eere/fuelcells/hydrogen-production-biomass-gasification. The hydrogen could then be used for powering vehicles etc. This type of waste seems to be in abundance as well. This article (https://www.energy.gov/sites/prod/files/2015/01/f19/billion_ton_update_0.pdf) suggests that we will produce 1 billion tons of biomass year. This article was written in 2011!!!
The great part they claim is that there is low net green house gas produced since biomass uses CO2 during its growth process. This is very interesting.
So, we still don’t have a clear idea on the whole original question of building a manufacturing plant that uses renewables. But, maybe next time.
I’d like to make a world heat map of the riots going on. Doing a quick google search didn’t get me any results of such an app. I was thinking about riots because of somewhat of a long story. There is a podcast that I listen to called The White Vault, that has a voice actor that lives in Chile. Now recently the podcast had to postpone an episode because there was a riot going on in Chile and it was unsafe for the voice actor to make it into the studio to record. This got me curios about riots.
There are many questions I have about riots. How do they form, what is the root cause, what are the initial conditions, how do governments handle them? How useful are they for bringing about real change? What sort of metrics and data can we collect to answer these questions? Can we then use this collected data in order to predict when riots will form? Riots are interesting and seemingly most of the world experiences them.
But, for the heatmap, I think initially the heat should be based on the number of news articles. Make another map that is based on NLP extraction of the number of deaths/money/etc that shows how actually bad the thing is.
But, really i’d like to also link to the list of the news articles/outlets etc.
I know d3 is pretty complex but maybe something like:
where maybe the connectors would be the country that was reporting on the riot.
I’m now working at Janssen Pharmaceuticals (a Johnson and Johnson company) as a machine learning scientist for drug discovery. So, what that means is that I’m using state of the art techniques in machine learning and trying to use them to help discover new drugs with the right properties. I just finished my second week and am planning on working on a reinforcement learning project already!
I’m still learning what the bigger goals are and what the overarching plan is directing our research. And well I’m not really a chemistry or biology expert in any way so I’m getting up to speed on a lot of that. Thankfully my boss has been able to explain everything I need to know so far. So, I’ll try and keep the blog updated.
As far as Janssen itself, I’m liking that I get to work one day a week at home and that I get free access to a bunch of museums like MOMA in NYC. One downside is that the 16 hr of volunteer time they give you off you can only take it off in 8 hour increments and it doesn’t accrue. So, it is going to be a bit harder to take advantage of this benefit than I thought. Also, the health insurance options don’t seem to be as good as I was expecting, especially for a pharma company.
A quick edit. I noticed I haven’t posted here in more than a year! Well my last year was a crazy busy period due to my previous job at Raytheon BBN. While working there I got to do a variety of research on about five different proposals and worked on two different programs. So, hopefully this new job will be a less stressful and I’ll have more time to post here.
I think it might be helpful in the future to not just have json data be just the dataset but also contain pickled functions that can be used by the end user to easily access the data in a way that works for their application. Dill can be used to serialize a python function or class (though no security is assumed). Then stick that serialized function into the json and use that to read the dataset. Would be much nicer to just say that everyone needs to provide functions to their dataset that are easily obtained. Since these are arbitrary functions this is very very dangerous so I’d only recommend using for data that you wrote yourself… So, this sort of defeats the purpose…
Looks like a neat meet-up/website.
My original bounty hunting paper could actually be considered a market implementation of the Highest Response Ratio Next.
The bounty assigned to tasks is set to some base bounty and a bounty rate $latex r$ which in the first bounty hunting paper was set to 100 and 1 respectively. So, as each tasks was left undone the bounty on it would rise. Tasks belong to particular “task classes” which basically means that there location is drawn from the same gaussian distribution. In the paper we used 20 task classes and there were four agents. The four agents were located at each of the four corners of a 40×60 rectangular grid. The agents decide which task to go after based on which task has the highest bounty per time step which works out to be:
This is for the case when agents commit to tasks and are not allowed to abandon them. Essentially non-preemptive. When the agents are allowed to abandon tasks we then have:
Both of these equations are stating that the agents are going after the task in an HRRN order. Now, the key part that bounty hunting added was that it made it work in a multiagent setting. This is where they learned some probability of success of going after the particular task class . Also, the paper experimentally demonstrated some other nice properties of bounty hunting based task allocation in a dynamic setting.
Presently I’m taking this approach and moving it to dynamic vehicle routing setting where I use it to minimize the average waiting time of tasks where the agent doesn’t get teleported back to a home base after each task completion. Namely the dynamic multiagent traveling repairman problem. This is another setting where the Shortest Job Next (Nearest Neighbor in euclidean space) is a descent heuristic and because the agents are not reset causes interesting behavior with a non-zero bounty rate.
Found a pretty cool site The Morning Paper. The author goes over a CS paper every day during the week. It is a broad range of interesting topics.
So, consider a dynamic wireless sensor network. We wish to minimize the average wait time for each of the nodes in the network to be serviced by new information. We however do not want to increase the By using the bounty hunting algorithm we can do this. I might want to look into routing algorithms.
Consider poison point processes with holes. When we have a single neighborhood we have a poisson point process, when we have multiple neighborhoods we will have non-overlapping regions where no tasks are generated. This is where the “holes” are. Stochastic Geometry is the area of mathmatics which is interested in this.
But, I’ve not really been focused on wireless sensor networks, and it is a bit of a stretch to fit bounty hunting to it (at least as far as I can tell. My first papers might suggest otherwise). But with my current direction I have more interest with spatial queues, I have queues rather than wireless sensor networks. So, there is spatial queuing theory, but there is not a spatial queuing theory with holes! The paper “Risk and Reward in Spatial Queuing Theory” deals with spatial queuing theory for the dynamic traveling repairman problem. All of these systems assume a region without holes or space where no tasks will be generated. This is an important thing in the real world as there are generally spaces where there won’t actually be tasks. Therefore, I think I need to incorporate the concept of Poisson Point Processes with Holes. Then build from that what to expect based on the size of the holes and locations. The holes matter because the distance the servers must travel between the next task is dependent on the size of these holes!
So, I think this is important. Actually I think that holes might not be general enough. It would be better if I could generalize to any space.
Wow so it has been a long time. I’ve recently been looking at stocks again and just two days ago I found a stock and I was like I should buy that. Then I didn’t. But I really really should have because it then proceeded to go up by 20% in 2 days. So, this made me look again into algorithmic trading. I found a couple really good resources:
Quantopian will let you design your own algorithms for trading on old data and will also let you run it through robinhood.io or interactivebrokers. I think I like IB better but should start with robinhood.io as there are no fees. But this is awesome!