macos – Connecting to remote mac machine via ssh tunnelling over VPN

From my laptop, I am trying to open remote desktop in server2:

This is how it looks like:

my-mac-laptop -> vpn. ---> server1(redhat)  ->  server2(mac-server)
                           <----------same network---------------> 

Here, I can’t access server2 directly. I have to connect to server1 via VPN. And from server1 I can access server2(mac-server).

I have already enabled Remote Login and Remote Management in the server2(mac-server).

Followed to that, I am attempting to port forward the remote desktop ports with the following command:

ssh -L 5900:server2-ip-address:5900 user@server1

My understanding has been this will tunnel all the remote desktop traffic to the server2(mac-server) in my mac laptop.

In the next step, I open screen-sharing app from my mac laptop and type localhost which should forward 5900 traffic to mac-server2 over the ssh-tunnel.

However, this is not working and it is stuck with “connecting” status. Any idea?

I would like to know how I can triage this case, any ideas welcome.

Are there logs in the mac-server2 that might assist me to debug?

machine learning – Remove unnecessary Neurons from a Neural Network regarding a particular output

Suppose we have a Neural Network with a binary output (0 or 1). What I am trying to do is to remove neurons or layers from the NN while maintaining a correct classification for all the instances that were classified as 1 in the original NN, same thing for the output 0. Said differently, is there any way to spot neurons that are paramount to the correct classification of a particular output ? The aim is to remove all the unnecessary neurons for that output.

A research track could be compiling the NN to a Boolean Formula and reasoning on it to spot the neurons that does not contribute to the chosen output, but it is not always obvious to carry out this compilation.

Black screen on starting Ubuntu on dualboot machine with Mac

My dual boot with Ubuntu 18.04 and Mac was running perfectly until my Macbook Pro (mid-2014) ran out of battery and shut down. I do not use the ReFind boot manager but use the alt button to choose between boot options on starting the computer. Now when I try to start into Ubuntu it shortly shows the Ubuntu logo followed by a blackscreen. I tried to start grub by holding esc but then it just prints “grub” to the black screen and does not do anything else. Does anybody know how to fix this problem?

Turing machine for L = {a∗wwb∗ : w ∈{a,b}∗}

I know how to create a Turing machine for ww. However, this language is very tricky one because I have to distinguish between a* and w and b* and w, which seems ridiculous without any bruteforce. Any ideas?

permissions – Connecting to mysql db server from a remote server to a local machine

As per my understanding, there are two main things to check before connecting to a remote mysql db server.

1. Bind address.
2. Grant privileges.

I was able to connect to a remote mysql db server from my Mac, it was easy (mysql -uroot -h x.x.x.x -p)

Imagine a scenario for my own learning, I want to connect to a mysql db instance on my Mac from a remote server. Imagine I am logging into this remote linux machine from my Mac and trying to connect to my own Mac db server?

mysql -uroot -h x.x.x.x -p

is raising an error 2003 mysql cannot connect 10060 cannot connect to the x.x.x.x server. It is similar to the reverse proxying while connecting to the local machine from a remote one, can someone direct to right resource (if there is a similar question please comment) which can help me figure out this thing?

architecture – Unifying Python Preprocessing Pipelines for Machine Learning on Time Series Data (High Throughput Batch & Low Latency Streaming)

What frameworks, design patterns, systems, etc. exist for unifying the preprocessing of time series data in Python such that high throughput is achieved on the retrospective batch data for training machine learning models while also allowing for easy model deployment with low latency on real-time streaming data for inference?

The data is raw, high frequency (50-500Hz) ICU bedside monitor signals which are recorded into a bespoke time-series database containing over 3 trillion data points that come from a RabbitMQ stream producing on the order of tens of thousands of new points per second. The database is accessible via a rather slow REST API backend in PHP or SDKs based in various languages. Researchers are really only familiar with Python-based tooling (numpy, scipy, pandas, biosppy, PyTorch, Tensorflow, various GitHub repos of domain-specific code from research papers, etc) while also not being interested or skilled in engineering custom deployment code for each of their models. We are needing high throughput while preparing large subsets (up to a couple of terabytes raw) of the database for efficiently and easily developing models from retrospective data, while also having a very low latency (<500ms) on real-time streaming data.

  1. Traditional ML Research Approach

    • Relevant retrospective data is pulled to disk, processed sequentially (usually inefficiently), and eventually, a decent performing model is made.
    • This works but is completely incompatible with the real-time systems meaning deployment requires significant custom engineering. Or worse, this processing is simply inefficient and takes days to process the dataset and/or extra engineering work to parallelize. Therefore, this is not a great approach due to how the research phase is slowed down and the excessive technical work requirements for optimizing the processing as well as deploying to real-time data.
  2. Custom Python Framework

    • I had started on developing a Python-based framework that tried to minimize the amount of systems knowledge and code required from researchers while still allowing them the freedom to perform their work however they saw fit via familiar tools (ie, Python). This ended up being quite the undertaking and has been very difficult. Worries of bugs, dealing with edge cases, performance, maintenance, adaptability, … have me looking for a better approach. Many of the problems I discovered along the way seem to be addressed by the streaming frameworks.
  3. Streaming Frameworks

    • It appears that streaming frameworks are the current leading way of feeding real-time machine learning models. In particular, Spark Streaming, but also Storm, Flink, Samza, Trill, …
    • Apache Beam is currently the most interesting though I am uncertain this is the best option
      • Unified: batch and streaming can be processed in the same way
        • I recently learned Flink offers similar functionality
      • Extensible: allows for new SDKs, IO connectors, etc
        • This is important for interfacing with our bespoke database.
        • Issue: The Python SDK of Apache Beam does not have existing IO for RabbitMQ but Java SDK does
        • Allows for the execution of any Python code on the data, not just simplistic min/max/windowing and such of other streaming engines
      • Language: Allows for everything to be written in Python
        • Only other all Python offering I have found is Streamz

Thank you for your time. If there are better venues or ways to ask this question, please let me know! Especially if I have misused terminology, please let me know as that will allow me to research these issues better on my own.

user experience – how to use an espresso machine?

There are often questions on the net about how to use an espresso machine, with no clarification of what exactly is meant. There are a lot of coffee machines, they are automatic, semi-automatic, traditional (such as those installed in coffee houses), office, home. You will find most of the information on the excellent website

dnd 5e – Is the Infernal Machine Rebuild module allowed in Adventurers League?

Unfortunately, no, it’s not AL-legal.

The ALCC, or Adventurers League Content Catalogue (available for free as part of the D&D Adventurers League Player & DM Pack) has the following to say about Infernal Machine Rebuild on p. 13 of the document:

Infernal Machine Rebuild

While Infernal Machine Rebuild is not an AL-legal adventure, those who
complete it can apply the rewards from the included certificate to one
of their Adventurers League characters.

Is Infernal Machine Rebuild is a AL Legal? If so.. what the module code?

My Friend want to learn playing DnD with AL ways so I want to give him an T1 adventure, And I found this Infernal Machine Rebuild. I read the story.. and I like it. But, Is it AL Legal Adventure tho.. thats just I wanna ask..

Are password managers still effective on a cloud PC (virtual machine)?

I have recently started using a cloud gaming service. It provides me with control of a ‘remote PC’ (actually a virtual machine running in their data centre) accessed through an app or VNC console. The VM is switched on/off using a control webpage where a password must be entered.

This week the cloud gaming service was hacked (specifically, several people discovered that all users could access the control webpage back-end using their ordinary user password). An attacker had access to all of the virtual machines and is known to have accessed one of them (owned by someone we will call “Numpty”) in order to take control of other accounts which were open on that VM at the time.

Using a password manager (1Password, LastPass, etc.) is often the top recommendation for secure computing. But in this specific case, it seems that using a password manager is no more secure. It would not have protected Numpty, since their other accounts were already open on the desktop to which the attacker had full access. Is that correct?

I even wonder whether a password manager would have been less secure than memorized passwords. If Numpty had been using a password manager, it seems that the attacker could have opened the web pages of popular websites (Amazon, Google, PayPal, etc.) and accessed those accounts without needing to know the password. Is that correct? I have never used a password manager, so I am not certain.

I am wondering about whether to discourage the other users of cloud gaming services from using password managers, so please point out any flaws in this argument.