Royce  Reinger

Royce Reinger


Ruptures: Change Point Detection in Python

Welcome to ruptures

ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models. ruptures focuses on ease of use by providing a well-documented and consistent interface. In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.

Basic usage

(Please refer to the documentation for more advanced use.)

The following snippet creates a noisy piecewise constant signal, performs a penalized kernel change point detection and displays the results (alternating colors mark true regimes and dashed lines mark estimated change points).

import matplotlib.pyplot as plt
import ruptures as rpt

# generate signal
n_samples, dim, sigma = 1000, 3, 4
n_bkps = 4  # number of breakpoints
signal, bkps = rpt.pw_constant(n_samples, dim, n_bkps, noise_std=sigma)

# detection
algo = rpt.Pelt(model="rbf").fit(signal)
result = algo.predict(pen=10)

# display
rpt.display(signal, bkps, result)

General information


Concerning this package, its use and bugs, use the issue page of the ruptures repository. For other inquiries, you can contact me here.

Important links

Dependencies and install

Installation instructions can be found here.


See the changelog for a history of notable changes to ruptures.

How to cite. If you use ruptures in a scientific publication, we would appreciate citations to the following paper:

  • C. Truong, L. Oudre, N. Vayatis. Selective review of offline change point detection methods. Signal Processing, 167:107299, 2020. [journal] [pdf] 

Download Details:

Author: Deepcharles
Source Code: 
License: BSD-2-Clause license

#machinelearning #python #science #signal #processing 

Ruptures: Change Point Detection in Python

FLSA.jl: Computing The Fused LASSO Signal Approximator


Computing a graph induced Fused LASSO Signal Approximator. You can use it to denoise data. The package includes some utility methods to assess the algorithms and apply it to images as well es ion-mobilty spectrometry (IMS) data sets.

Mathematical Formulation

flsa formula

For the one dimensional version of the Johnson's dynamic programming algorithm, have a look into Lasso.jl


The fused LASSO signal approximator can be used to denoise e.g. images:

Noisy Input

demo noise

Cleaned by FLSA

demo flsa


Fast Gradient Projection (FGP)

Also known as Fast Iterative Shrinkage Algorithm (FISTA).

Alternating Direction Method of Multipliers (ADMM)

Maximum Gap Tree (MGT)

Own algorithm based on a iterative approximation by dynamic programming algorithm minimizing a sub-tree-graph.


Image Graph

using FLSA
graph = FLSA.img_graph(size(B)..., dn=2, lam=0.1)   # (1)
F = FLSA.fista(B, graph, verbose=true; max_iter=10) # (2)

First you have to define graph (line (1)). Then one of the algorithms above are called (see (2)).

HDF5 Input

In order to be easily called from other languages a HDF5 intermediate data structure is supported that looks as follows (see for a working python example):

             1 2 3 ... n

             1 2 3 ... m


Download Details:

Author: EQt
Source Code: 
License: View license

#julia #computing #signal 

FLSA.jl: Computing The Fused LASSO Signal Approximator
Lawrence  Lesch

Lawrence Lesch


Peerjs-server: Server for PeerJS

PeerServer: A server for PeerJS

PeerServer helps establishing connections between PeerJS clients. Data is not proxied through the server.

Run your own server on Gitpod!

Open in Gitpod


Run server


If you don't want to develop anything, just enter few commands below.

Install the package globally:

$ npm install peer -g

Run the server:

$ peerjs --port 9000 --key peerjs --path /myapp

  Started PeerServer on ::, port: 9000, path: /myapp (v. 0.3.2)

Check it: It should returns JSON with name, description and website fields.


Also, you can use Docker image to run a new container:

$ docker run -p 9000:9000 -d peerjs/peerjs-server


$ kubectl run peerjs-server --image=peerjs/peerjs-server --port 9000 --expose -- --port 9000 --path /myapp

Create a custom server:

If you have your own server, you can attach PeerServer.

Install the package:

# $ cd your-project-path

# with npm
$ npm install peer

# with yarn
$ yarn add peer

Use PeerServer object to create a new server:

const { PeerServer } = require('peer');

const peerServer = PeerServer({ port: 9000, path: '/myapp' });

Check it: It should returns JSON with name, description and website fields.

Connecting to the server from client PeerJS:

    const peer = new Peer('someid', {
      host: 'localhost',
      port: 9000,
      path: '/myapp'

Config / CLI options

You can provide config object to PeerServer function or specify options for peerjs CLI.

CLI optionJS optionDescriptionRequiredDefault
--port, -pportPort to listen (number)Yes 
--key, -kkeyConnection key (string). Client must provide it to call API methodsNo"peerjs"
--pathpathPath (string). The server responds for requests to the root URL + path. E.g. Set the path to /myapp and run server on 9000 port via peerjs --port 9000 --path /myapp Then open - you should see a JSON reponse.No"/"
--proxiedproxiedSet true if PeerServer stays behind a reverse proxy (boolean)Nofalse
--expire_timeout, -texpire_timeoutThe amount of time after which a message sent will expire, the sender will then receive a EXPIRE message (milliseconds).No5000
--alive_timeoutalive_timeoutTimeout for broken connection (milliseconds). If the server doesn't receive any data from client (includes pong messages), the client's connection will be destroyed.No60000
--concurrent_limit, -cconcurrent_limitMaximum number of clients' connections to WebSocket server (number)No5000
--sslkeysslkeyPath to SSL key (string)No 
--sslcertsslcertPath to SSL certificate (string)No 
--allow_discoveryallow_discoveryAllow to use GET /peers http API method to get an array of ids of all connected clients (boolean)No 
 generateClientIdA function which generate random client IDs when calling /id API method (() => string)Nouuid/v4


Simply pass in PEM-encoded certificate and key.

const fs = require('fs');
const { PeerServer } = require('peer');

const peerServer = PeerServer({
  port: 9000,
  ssl: {
    key: fs.readFileSync('/path/to/your/ssl/key/here.key'),
    cert: fs.readFileSync('/path/to/your/ssl/certificate/here.crt')

You can also pass any other SSL options accepted by https.createServer, such as `SNICallback:

const fs = require('fs');
const { PeerServer } = require('peer');

const peerServer = PeerServer({
  port: 9000,
  ssl: {
    SNICallback: (servername, cb) => {
        // your code here ....

Running PeerServer behind a reverse proxy

Make sure to set the proxied option, otherwise IP based limiting will fail. The option is passed verbatim to the expressjs trust proxy setting if it is truthy.

const { PeerServer } = require('peer');

const peerServer = PeerServer({
  port: 9000,
  path: '/myapp',
  proxied: true

Custom client ID generation

By default, PeerServer uses uuid/v4 npm package to generate random client IDs.

You can set generateClientId option in config to specify a custom function to generate client IDs.

const { PeerServer } = require('peer');

const customGenerationFunction = () => (Math.random().toString(36) + '0000000000000000000').substr(2, 16);

const peerServer = PeerServer({
  port: 9000,
  path: '/myapp',
  generateClientId: customGenerationFunction

Open to see a new random id.

Combining with existing express app

const express = require('express');
const { ExpressPeerServer } = require('peer');

const app = express();

app.get('/', (req, res, next) => res.send('Hello world!'));

// =======

const server = app.listen(9000);

const peerServer = ExpressPeerServer(server, {
  path: '/myapp'

app.use('/peerjs', peerServer);

// == OR ==

const http = require('http');

const server = http.createServer(app);
const peerServer = ExpressPeerServer(server, {
  debug: true,
  path: '/myapp'

app.use('/peerjs', peerServer);


// ========

Open the browser and check


The 'connection' event is emitted when a peer connects to the server.

peerServer.on('connection', (client) => { ... });

The 'disconnect' event is emitted when a peer disconnects from the server or when the peer can no longer be reached.

peerServer.on('disconnect', (client) => { ... });


Read /src/api/

Running tests

$ npm test


We have 'ready to use' images on docker hub:

To run the latest image:

$ docker run -p 9000:9000 -d peerjs/peerjs-server

You can build a new image simply by calling:

$ docker build -t myimage

To run the image execute this:

$ docker run -p 9000:9000 -d myimage

This will start a peerjs server on port 9000 exposed on port 9000 with key peerjs on path /myapp.

Open your browser with http://localhost:9000/myapp It should returns JSON with name, description and website fields. http://localhost:9000/myapp/peerjs/id - should returns a random string (random client id)

Running in Google App Engine

Google App Engine will create an HTTPS certificate for you automatically, making this by far the easiest way to deploy PeerJS in the Google Cloud Platform.

Create a package.json file for GAE to read:

echo "{}" > package.json
npm install express@latest peer@latest

Create an app.yaml file to configure the GAE application.

runtime: nodejs

# Flex environment required for WebSocket support, which is required for PeerJS.
env: flex

# Limit resources to one instance, one CPU, very little memory or disk.
  instances: 1
  cpu: 1
  memory_gb: 0.5
  disk_size_gb: 0.5

Create server.js (which node will run by default for the start script):

const express = require('express');
const { ExpressPeerServer } = require('peer');
const app = express();

app.enable('trust proxy');

const PORT = process.env.PORT || 9000;
const server = app.listen(PORT, () => {
  console.log(`App listening on port ${PORT}`);
  console.log('Press Ctrl+C to quit.');

const peerServer = ExpressPeerServer(server, {
  path: '/'

app.use('/', peerServer);

module.exports = app;

Deploy to an existing GAE project (assuming you are already logged in via gcloud), replacing YOUR-PROJECT-ID-HERE with your particular project ID:

gcloud app deploy --project=YOUR-PROJECT-ID-HERE --promote --quiet app.yaml




Discuss PeerJS on our Telegram chat:

Please post any bugs as a Github issue.

Download Details:

Author: Peers
Source Code: 
License: MIT license

#typescript #webrtc #signal 

Peerjs-server: Server for PeerJS

Synchrony.jl: Analysis Of Synchronous Signals

Analysis of synchronous signals

This package implements efficient multitaper and continuous wavelet transforms, along with the following transform statistics most of which operate on pairs of signals:

  • Power spectral density (PowerSpectrum)
  • Power spectral density variance (PowerSpectrumVariance)
  • Cross spectrum (CrossSpectrum)
  • Coherence (Coherence for the absolute value, Coherency for the complex value)
  • Phase locking value, a.k.a. the mean resultant vector length or R̄ (PLV)
  • Pairwise phase consistency, a.k.a. the unbiased estimator of R̄^2 (PPC)
  • Phase lag index (PLI)
  • Unbiased squared phase lang index (PLI2Unbiased)
  • Weighted phase lag index (WPLI)
  • Debiased squared weighted phase lag index (WPLI2Debiased)
  • Jammalamadaka circular correlation coefficient (JCircularCorrelation)
  • Jupp-Mardia squared circular correlation coefficient (JMCircularCorrelation)
  • Hurtado et al. modulation index (phase-amplitude coupling) (HurtadoModulationIndex)

Additionally, the following point-field measures are implemented:

  • Point-field coherence (pfcoherence)
  • Point-field PLV (pfplv)
  • Point-field PPC, variants 0, 1, and 2 (pfppc0, pfppc1, pfppc2)

And the following point-point measures:

  • Point-point cross correlation (pfxcorr)

All measures except for the point-field measures have corresponding unit tests. Documentation is forthcoming.

Download Details:

Author: Simonster
Source Code: 
License: View license

#julia #signal #analysis 

Synchrony.jl: Analysis Of Synchronous Signals
Brook  Hudson

Brook Hudson


Clamp: A Ruby Command-line Application Framework


"Clamp" is a minimal framework for command-line utilities.

It handles boring stuff like parsing the command-line, and generating help, so you can get on with making your command actually do stuff.

Not another one!

Yeah, sorry. There are a bunch of existing command-line parsing libraries out there, and Clamp draws inspiration from a variety of sources, including Thor, optparse, and Clip. In the end, though, I wanted a slightly rounder wheel. (Although, Clamp has a lot in common with Ara T. Howard's main.rb. Had I been aware of that project at the time, I might not have written Clamp.)

Quick Start

A typical Clamp script looks like this:

require 'clamp'

Clamp do

  option "--loud", :flag, "say it loud"
  option ["-n", "--iterations"], "N", "say it N times", default: 1 do |s|

  parameter "WORDS ...", "the thing to say", attribute_name: :words

  def execute
    the_truth = words.join(" ")
    the_truth.upcase! if loud?
    iterations.times do
      puts the_truth


Internally, Clamp models a command as a Ruby class (a subclass of Clamp::Command), and a command execution as an instance of that class. The example above is really just syntax-sugar for:

require 'clamp'

class SpeakCommand < Clamp::Command

  option "--loud", :flag, "say it loud"
  option ["-n", "--iterations"], "N", "say it N times", default: 1 do |s|

  parameter "WORDS ...", "the thing to say", attribute_name: :words

  def execute
    the_truth = words.join(" ")
    the_truth.upcase! if loud?
    iterations.times do
      puts the_truth


Class-level methods like option and parameter declare attributes, in a similar way to attr_accessor, and arrange for them to be populated automatically based on command-line arguments. They are also used to generate help documentation.

There are more examples demonstrating various features of Clamp on Github.

Declaring options

Options are declared using the option method. The three required arguments are:

  1. the option switch (or switches),
  2. an option argument name
  3. a short description

For example:

option "--flavour", "FLAVOUR", "ice-cream flavour"

It works a little like attr_accessor, defining reader and writer methods on the command class. The attribute name is inferred from the switch (in this case, "flavour"). When you pass options to your command, Clamp will populate the attributes, which are then available for use in your #execute method.

def execute
  puts "You chose #{flavour}.  Excellent choice!"

If you don't like the inferred attribute name, you can override it:

option "--type", "TYPE", "type of widget", attribute_name: :widget_type
                                           # to avoid clobbering Object#type

Short/long option switches

The first argument to option can be an array, rather than a single string, in which case all the switches are treated as aliases:

option ["-s", "--subject"], "SUBJECT", "email subject line"

Flag options

Some options are just boolean flags. Pass ":flag" as the second parameter to tell Clamp not to expect an option argument:

option "--verbose", :flag, "be chatty"

For flag options, Clamp appends "?" to the generated reader method; ie. you get a method called "#verbose?", rather than just "#verbose".

Negatable flags are easy to generate, too:

option "--[no-]force", :flag, "be forceful (or not)"

Clamp will handle both "--force" and "--no-force" options, setting the value of "#force?" appropriately.

Required options

Although "required option" is an oxymoron, Clamp lets you mark an option as required, and will verify that a value is provided:

option "--password", "PASSWORD", "the secret password", required: true

Note that it makes no sense to mark a :flag option, or one with a :default, as :required.

Multivalued options

Declaring an option ":multivalued" allows it to be specified multiple times on the command line.

option "--format", "FORMAT", "output format", multivalued: true

The underlying attribute becomes an Array, and the suffix "_list" is appended to the default attribute name. In this case, an attribute called "format_list" would be generated (unless you override the default by specifying an :attribute_name).

Hidden options

Declaring an option ":hidden" will cause it to be hidden from --help output.

option "--some-option", "VALUE", "Just a little option", hidden: true

Version option

A common idiom is to have an option --version that outputs the command version and doesn't run any subcommands. This can be achieved by:

option "--version", :flag, "Show version" do
  puts MyGem::VERSION

Declaring parameters

Positional parameters can be declared using parameter, specifying

  1. the parameter name, and
  2. a short description

For example:

parameter "SRC", "source file"

Like options, parameters are implemented as attributes of the command, with the default attribute name derived from the parameter name (in this case, "src"). By convention, parameter names are specified in uppercase, to make them obvious in usage help.

Optional parameters

Wrapping a parameter name in square brackets indicates that it's optional, e.g.

parameter "[TARGET_DIR]", "target directory"

Multivalued (aka "greedy") parameters

Three dots at the end of a parameter name makes it "greedy" - it will consume all remaining command-line arguments. For example:

parameter "FILE ...", "input files", attribute_name: :files

Like multivalued options, greedy parameters are backed by an Array attribute (named with a "_list" suffix, by default).

Parsing and validation of options and parameters

When you #run a command, it will first attempt to #parse command-line arguments, and map them onto the declared options and parameters, before invoking your #execute method.

Clamp will verify that all required (ie. non-optional) parameters are present, and signal a error if they aren't.


Both option and parameter accept an optional block. If present, the block will be called with the raw string argument, and is expected to validate it. The value returned by the block will be assigned to the underlying attribute, so it's also a good place to coerce the String to a different type, if appropriate.

For example:

option "--port", "PORT", "port to listen on" do |s|

If the block raises an ArgumentError, Clamp will catch it, and report that the value was bad:

ERROR: option '--port': invalid value for Integer: "blah"

For multivalued options and parameters, the validation block will be called for each value specified.

More complex validation, e.g. those involving multiple options/parameters, should be performed within the #execute method. Use #signal_usage_error to tell the user what they did wrong, e.g.

def execute
  if port < 1024 && user != 'root'
    signal_usage_error "port restricted for non-root users"
  # ... carry on ...

Advanced option/parameter handling

While Clamp provides an attribute-writer method for each declared option or parameter, you always have the option of overriding it to provide custom argument-handling logic, e.g.

parameter "SERVER", "location of server"

def server=(server)
  @server_address, @server_port = server.split(":")

Default values

Default values can be specified for options, and optional parameters:

option "--flavour", "FLAVOUR", "ice-cream flavour", default: "chocolate"

parameter "[HOST]", "server host", default: "localhost"

For more advanced cases, you can also specify default values by defining a method called "default_#{attribute_name}":

option "--http-port", "PORT", "web-server port", default:  9000

option "--admin-port", "PORT", "admin port"

def default_admin_port
   http_port + 1

Environment variable support

Options (and optional parameters) can also be associated with environment variables:

option "--port", "PORT", "the port to listen on", environment_variable: "MYAPP_PORT" do |val|

parameter "[HOST]", "server address", environment_variable: "MYAPP_HOST"

Clamp will check the specified envariables in the absence of values supplied on the command line, before looking for a default value.

Allowing options after parameters

By default, Clamp only recognises options before positional parameters.

Some other option-parsing libraries - notably GNU getopt(3) - allow option and parameter arguments to appear in any order on the command-line, e.g.

foobar --foo=bar something --fnord=snuffle another-thing

If you want Clamp to allow options and parameters to be "interspersed" in this way, set:

Clamp.allow_options_after_parameters = true

Declaring Subcommands

Subcommand support helps you wrap a number of related commands into a single script (ala tools like "git"). Clamp will inspect the first command-line argument (after options are parsed), and delegate to the named subcommand.

Unsuprisingly, subcommands are declared using the subcommand method. e.g.

Clamp do

  subcommand "init", "Initialize the repository" do

    def execute
      # ...



Clamp generates an anonymous subclass of the current class, to represent the subcommand. Alternatively, you can provide an explicit subcommand class:

class MainCommand < Clamp::Command

  subcommand "init", "Initialize the repository", InitCommand


class InitCommand < Clamp::Command

  def execute
    # ...


Like options, subcommands may have aliases:

Clamp do

  subcommand ["initialize", "init"], "Initialize the repository" do
    # ...


Default subcommand

You can set a default subcommand, at the class level, as follows:

Clamp do

  self.default_subcommand = "status"

  subcommand "status", "Display current status" do

    def execute
      # ...



Then, if when no SUBCOMMAND argument is provided, the default will be selected.

Subcommand options and parameters

Options are inheritable, so any options declared for a command are supported by it's sub-classes (e.g. those created using the block form of subcommand). Parameters, on the other hand, are not inherited - each subcommand must declare it's own parameter list.

Note that, if a subcommand accepts options, they must be specified on the command-line after the subcommand name.

You can define a subcommand_missing method that is called when user tries to run an unknown subcommand:

Clamp do
  def subcommand_missing(name)
    if name == "foo"
      return Object.const_get(:FooPlugin) if Object.const_defined?(:FooPlugin)
      abort "Subcommand 'foo' requires plugin X"

Getting help

All Clamp commands support a "--help" option, which outputs brief usage documentation, based on those seemingly useless extra parameters that you had to pass to option and parameter.

$ speak --help
    speak [OPTIONS] WORDS ...

    WORDS ...                     the thing to say

    --loud                        say it loud
    -n, --iterations N            say it N times (default: 1)
    -h, --help                    print help


Clamp comes with support for overriding strings with custom translations. You can use localization library of your choice and override the strings at startup.

Example usage:

require 'gettext'

Clamp.messages = {
  too_many_arguments:        _("too many arguments"),
  option_required:           _("option '%<option>s' is required"),
  option_or_env_required:    _("option '%<option>s' (or env %<env>s) is required"),
  option_argument_error:     _("option '%<switch>s': %<message>s")
  # ...

See messages.rb for full list of available messages.


Copyright (C) 2011 Mike Williams

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.


Contributing to Clamp

Source-code for Clamp is on Github.

Author: mdub
Source code:
License: MIT license

#ruby  #ruby-on-rails 

Clamp: A Ruby Command-line Application Framework
David mr

David mr



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Mark Henry

Mark Henry


How to Use Signal on Windows & Mac -

In this article, we have discussed the ways with which you can use Signal on Mac & Windows. The popularity of the encrypted messaging app, Signal, has grown recently due to endorsement by several celebrities and WhatsApp’s ill-conceived privacy policy update. In fact, the iOS version of the Signal messaging app recently topped the download charts of the App Store in several countries, including France, Austria, Germany, Finland, Switzerland, India, and Hong Kong. This was about the mobile version of Signal, but do you know, you can also use Signal on a PC just like Telegram and WhatsApp? This article discusses the steps with the help of which you can use Signal Messenger on a Mac or Windows 10 PC or laptops.

Use Signal Messenger on Your Desktop Computer

Signal doesn’t offer its services over the web like WhatsApp but does have a desktop app for Linux, Mac, and Windows. However, similar to WhatsApp, you need to have the Signal mobile app functional on your handset so as to let it work on your PC. So, without any further delay, let’s proceed further to the steps to use Signal Messenger on a Mac or Windows 10 PC or laptops.

Link and Use Signal on Your PC

You can use Signal on your PC using the following steps:

First, you need to download Signal on your desktop and further install it.
Once you have installed the app, you need to launch it. A QR code will appear on your screen, and further, you need to sync the chats by scanning the mobile app of Signal.
Further, open the Signal Messenger on your smart device and click on the menu button appearing on the upper-right side.
Further, from the pop-up menu, you need to select “Settings.”
Tap on the “Linked Device” under the Settings page. Now, hit “+” appearing at the bottom-right corner on your Android device. If you run Signal on iOS, you need to go to Settings and click on “Linked Devices” and further tap on the option “Link New Devices.”
Now, scan the QR code onto the screen of your PC, and both the apps will get synced in a while. Once they’re in-sync, click on “Link Device.”
Further on your Signal desktop app, you need to select a name for the linked devices and click on “Finish Linking Phone.”
The Signal desktop app will sync all your contacts with your device, and now you can send and receive messages using the Signal Desktop. Your messages and contacts may take a few minutes to get synced between the two devices.

Un-Link Signal Desktop with Your Phone

To un-link the Signal desktop app with your Device, follow the steps mentioned below:

To un-link the devices, you need to click on Settings and tap on the “Linked Devices” on your smartphone.
Now, click on the device’s name that needs to be un-linked and further confirm by pressing “OK.”
That’s it. You have successfully un-linked Signal Desktop with the mobile app of Signal. Later on, when you need to use the app on your PC, you need to re-ink the two.

So, these were the steps with which you can use the best encrypted messaging app on your PC. Being one of the best alternatives to Telegram and WhatsApp, Signal has not only been endorsed by brands and celebrities, but also individuals have widely accepted it as the safest to date messaging service in terms of privacy.

Now that you know how to use Signal on a PC or laptop, you would still use it more often on your handset than on PC. So, have you installed Signal on your PC yet? Do let us know in the comment section if you face any issues.

Read Official Blog Post - How to Use Signal on Windows & Mac

#signal #application

How to Use Signal on Windows & Mac -

Tech Behind WhatsApp Rival Signal App

Signal Foundation and Signal Messenger LLC, a non-profit company, rolled out its flagship app in 2014. Ironically, Signal Foundation was set up by WhatsApp co-founder Brian Acton with Signal Messenger CEO Moxie Marlinspike.

Read more:

#signal #whatsapp #algorithm #dataprivacy #encryption

Tech Behind WhatsApp Rival Signal App
Elton  Bogan

Elton Bogan


Data Mining to Identify Drug Safety Signals

Marketing authorization of a drug is granted on the basis that, in the proposed indication(s) and at the time of authorization, its therapeutic effect outweigh the safety risk(s) for the target population (a.k.a “positive benefit-risk balance”). All drugs differ from each other in terms of their safety risk profile, even within the same therapeutic class, but no drug is without safety risk.

What’s a safety risk?…It’s any untoward occurrence associated with the use of a drug. Safety risks are named “identified” when there is adequate evidence of a causal relationship with the concerned drug, or “potential”, when there is some basis for suspicion of an association with the drug, but this has not been confirmed. An adverse drug reaction (ADR), contraindication or even the incorrect use of the drug might be considered as safety risks. When safety risks are important, these could have an impact in the benefit-risk balance of the drug and/or have implications for public health.

Every year, more drugs are launched and their utilization raise consequently. Moreover, polypharmacy (i.e., concurrent use of 5 or more drugs) increases ADR occurrence risk due to a multitude of factors, including drug-drug and drug-disease interactions. ADRs are an important cause of morbidity and mortality, even though many ADRs are potentially avoidable if drugs are used by the right patient at the right dose [1]. This reflects the importance to ensure the drug safety vigilance throughout the entire life cycle.

Photo by Laurynas Mereckas on Unsplash

Polypharmacy is common in the older population and the risk of ADRs and injury increases with increasing numbers of drugs

Pharmacovigilance (PV) is a multidisciplinary science aimed to the collection, assessment, monitoring and prevention of ADRs and other safety-related data, e.g., off-label use, pregnancy exposure, overdose. As a science, the knowledge on drug safety is built through the analysis of data received from various type of reporters (e.g., patients, health professionals) and by means of active (e.g., studies) and passive (e.g., spontaneous notification) surveillance strategies. All of us are part of a global PV network and contribute to the safe use of drugs when reporting suspected ADRs to the national health authority, drug manufacturer or PV/Poison center, given that underreporting of ADRs may result in the late identification of new safety risks.

#data-mining #pharmacovigilance #signal #data-science

Data Mining to Identify Drug Safety Signals

Target Classification with Doppler-Pulse Radar and Neural Networks

In the world of data science the industry, academic, and government sectors often collide when enthusiasts and experts alike, work together to tackle the challenges we face day-to-day. A prime example of this collaboration is the Israeli Ministry of Defense Directorate of Defense Research & Development (DDR&D)’s MAFAT challenges. A series of data science related challenges with real-world application and lucrative prize pools. In the program’s own words:

Given the recent inception of the program, there haven’t been many challenges yet, however, there are expected to be a variety of challenges ranging from complicated Natural Language Processing puzzles to computer-vision related endeavors.

One such challenge, their second one made available thus far, caught my eye. It involves creating a model for classifying living, non-rigid objects that have been detected by doppler-pulse radar systems. The challenge, “MAFAT Radar Challenge — Can you distinguish between humans and animals in radar tracks?” implores competitors to develop a model that can accurately distinguish humans from animals based on a spectrum of radio signals recorded from various doppler-pulse radar sites on various days. If you are interested in participating I recommend visiting thechallenge site before reading on.

So what kind of data are we working with and what do we need to know about it?

The key to developing an accurate and competitive model is to first understand the data, how it was sourced, and what it is missing. Included with the competition is 5 CSV files containing the metadata, and 5 pickle files (serializing Python object structure format) containing doppler readings that track the object’s center of mass and slow/fast time readings in the form of a standardized I/Q matrix.

Before we go any further it is worth breaking down a few key concepts relating to signals and the specific types of data collected. The signal readings that make up the dataset fall into two levels of quality, High Signal to Noise Ratio, and Low Signal to Noise Ratio. This reading, High SNR and Low SNR divides the set into two levels of quality, one with high clarity that hasn’t been heavily tainted by a noise generating process, and one with low clarity that has had aspects such as weather impact the quality of the reading.

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#machine-learning #radar #signal #challenge #neural-networks

Target Classification with Doppler-Pulse Radar and Neural Networks