I see these articles every now and then over last 5-10 years, but as someone who has followed/worked around this industry in the past, I can tell you that there is enough demand, supply and capital for this industry and it is not going anywhere.
All of these companies are EBDITA profitable with decent margins and the net loss is them reinvesting in marketing, product and M&A (from their earnings reports). As the effects of covid wanes, their profits are going to go up very quickly which will be seen over the next year.
Most housing discussions seems to avoid one of the main reason driving the prices up, their treatment as an investment vehicle.
Residential housing should not be used as an investment vehicle. Period. Its primary purpose is to allow families to own and live in the house. Mandating that can eliminate the rich landlords/foreign investors buying houses just for investment, stabilize the demand and drive down the prices.
One way to mandate that could be progressive property tax based on how many properties you already own with a max cap on number of allowed residential properties one can own. This might decrease incentive to build new housing for developers, but can be solved by temporary tax breaks for building new housing.
Anytime you create a new law, you should, like good code, remove all of the hard-coded values within it. Ideally all laws should be pointfree[0]. Having a max value of owners can easily be routed around(I don't own this house, this company, that I happen to own, owns this house!), or setting some tax breaks for developers are both nasty hacks that wouldn't make it through code review. The Land Value Tax would.
>One way to mandate that could be progressive property tax based on how many properties you already own with a max cap on number of allowed residential properties one can own.
This would do nothing. Investment ownership of housing is tiny. It is the homeowners who push for policy to reduce construction of housing and boost prices.
Getting rid of zoning + Land value taxes are the answer.
The second order effect is that without real estate investors, the market for rental accommodations will eventually disappear. Having no rental market would by default accomplish the goal of having everyone own their own home, but it would cause plenty of other social and economic pain.
One problem with high tax based on capital gains at sale is that it needs the sale to happen and you start running into issues where people never sell and houses will get inherited. This still doesn’t tackle the problem head on.
City and state governments can offer temporary tax breaks for building new housing in certain zones to attract capital. This is already done in a way when government is trying to attract capital for new industries via SEZs.
Hi James, this is pretty neat. I think this website could be a big hit among all the graduate applicants since they all are looking for creating a personal academic website at the time of applications.
Is there an option to import a latex resume to create the website? That would be handy.
I just want to thank you for ShareLatex, I use it for all my academic documents, it's pretty awesome.
Yes, I used Freeboard (https://github.com/Freeboard/freeboard) couple of years ago for an IoT store. It's highly customizable and has fantastic UI. It can be used with minimal effort. Highly recommended.
Actually, it's more related to the original naming term that was adopted for cellular neural networks (CNN). Convolutional neural networks were originally referred to as ConvNets (CNN term is also used for them as well).
IMHO, I don't think this naming is misleading since CNN has been originally used to refer cellular neural networks.
>Yes, this is one of the applications of these networks.
Thanks, would you mind elaborating more on other advantages?
I did read the wikipedia link and other links you posted, however it's not entirely clear what other benefits exist. May I suggest writing a blog post about what you perceive are the benefits of CNN (cellular networks) vis-a-vis the other CNNs.
Hi, author here. Even though I was on HN, I didn't realize that this is posted over here :) I'm glad about all the feedback. Thank you.
> I'm reading up on Cellular Neural Networks and it seems like they are simple convolutions, of which you can specify the kernel.
Actually its more than that, simply put, cellular neural networks are a parallel computing paradigm similar to neural networks, with the difference that communication is allowed between neighboring units only [1].
> In fact, this library is just calling scipy.signal.convolve2d() with different kernels.
The part you're referring to performs the convolution between the kernel function and the feedback template to get the result of the feedback loop. Please note the kernel function is sigmoidal or its approximation and remains unchanged.
It will be easier to understand if you'll visualize it as a control system as shown in [2] with a feedback template and a control template. These templates (coefficients) are configurable and produce different results for different configurations.
One of the applications of these networks is image processing as stated in [3] "CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors needed for applications like particle detection in jet engine fluids and spark-plug detection.".
Hi there. I'm completely unfamiliar with CNN. Do they relate to Cellular Autonoma in any way other than sharing part of a name? And of course the fact that they communicate only with neighbors. Are there patterns of emergent behavior in the classical sense, or is it more closely related to a neural networks?
Nice catch! It is indeed closely related to cellular automata. CNN processors could be thought of as a hybrid between ANN (artificial neural networks) and CA (continuous/cellular automata) [1]. Like neural networks, they are large-scale nonlinear analog circuits that process signals in real time. Like cellular automata, they consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly only through their nearest neighbors [2].
The topology and dynamics of CNN processors closely resembles that of CA. Like most CNN processors, CA consists of a fixed-number of identical processors that are spatially discrete and topologically uniform. The difference is that most CNN processors are continuous-valued whereas CA have discrete-values [1].
Originally CNN was used for Cellular Neural Networks and ConvNet was used for Convolutional Neural Networks. However, lately, CNN and ConvNet are both used interchangeably for Convolutional Neural Networks.
I'm using mobile and the adding website flow is smooth and concise. I connected my blog hosted on GitHub. The only caveat is that this requires permission to access everything on your GitHub account, public and private repositories, and permission to do anything with them.
However, after adding the website repository, I'm not able to see any posts, is this only for pages? There are some UI issues with page editor on mobile that can be sorted out in beta. Nice product!
Update: I can access posts after changing the URL, but a direct link is missing in the UI.
Unfortunately GitHub doesn't give great control over permissions. The only thing we can choose from is access to all public repos, or access to all public and private repos. We can't specify that we want only the user repos and not the organization or vice versa.
I will certainly have a look at the issue you are having. Could you contact us via our support tool on the site? Or email us at support at forestry.io.
All of these companies are EBDITA profitable with decent margins and the net loss is them reinvesting in marketing, product and M&A (from their earnings reports). As the effects of covid wanes, their profits are going to go up very quickly which will be seen over the next year.