fabiochiu/medium-articles huggingface dataset:
The Role of Politics in Leadership
By: Joe Bologna
Tags: Society

It’s not commonly known, but the actual reason Rome fell was because baseless rhetoric caused irrational behavior in leaders. The ability to agree (and win) was considered the ultimate virtue. (To some degree this remains true in cultures in the region). Politicians used rhetoric to gain control over the population. This lack of integrity with respect to what was good for the Roman populous resulted in the collapse of their society.

Politics are a necessary evil and necessary to gain kinship in order to obtain a position of authority. Authority is given as a vehicle to lead the populous. However, after the leadership position is obtained, politics no longer has a place. This transition from running for office and running the office does not occur in the U.S. anymore. In fact, all elected officials spend many hours per week trying to get reelected. A few have expressed the irony of trying to find time to do the job they were “hired” to do, yet moonlighting to satisfy the machine that got them into office. The stupidity of a system like this is obvious, but it is also unlikely to change.

The fundamental characteristic of a good leader is integrity and the ability to influence. A bad leader uses manipulation. Navigating the political process to obtain a leadership position through influence is difficult, but not impossible. Attack ads are used in nearly all modern political campaigns as a means to manipulate voters. Some ads don’t even identify the person running for the office, instead focus on the opponent. This is incredibly stupid and often ineffective.

Why? Because, subliminally, the human brain does not understand “not”. The subconscious is an emotion engine that only understands nouns. Therefore, when an attack ad mentions an opponent, they are actually advertising for the opponent. Often, the end of the ad will show the candidate in a pleasing, trustworthy setting to provide an immediate alternative. This doesn’t always work. It would be far better to repeat the candidates name without referring to the opponent. Voters’ subconscious would only focus on the candidate.

Candidates that resort to these forms of manipulation are unlikely good leaders. They will use manipulation to get what they want and be proxies for what their supporters want. They are OK with the means justifying the ends. So long as the do things they were “hired” to do, they’ll have support. This is not leadership. But it has become normal, it’s not even new.

In closing, I urge support for people who work through influence to overcome manipulative forces in leadership. There will always be a battle between “good” and “evil”, but we’re here to do good, let’s not give up on it. Maybe fewer people will be out of their minds as a result.
That’s how you were born girl!
Tags: Feminism, Love, Poetry, Society, Self Improvement

If she cooks, she is docile

If she doesn’t, she is a rebel

She parties with her friends, she is loose The one who comes home after work always, she is a bore

You do this, you do that

You do it all or you don’t give a damn

You will always be labelled and judged You’ll be forced to feel guilty

Step back and shrug off all these labels

Look inside and find yourself

Peel away each frame one by one

The one given by your parents

The one gifted by friends

The one that is holding you tight…

Who are you?

What do you want?

What are your likes? What are your dislikes?
Generative Line Art With Blender
By: Octavio Gonzalez-Lugo
Tags: Python, Blender, STEM, Mathematics

Simulation of natural phenomena, geometrical properties of different shapes, and travel schedule optimization are some applications of advanced mathematics. Nonetheless, art generation is far from being perceived as a possible application for mathematics. Due to its pattern generation capabilities, math can offer a series of tools to create several pieces of generative art. The following describes how to create geometrical patterns with trigonometric functions in blender.

Generative art

Generative art refers to artwork created by the use of an autonomous system. Generally, the autonomous system is non-human and can independently determine the features in the artwork that otherwise require the decisions made by the artist. In this case, the artwork will consist of a series of patterns generated with trigonometric functions. To add the patterns into the scene we need two things, one: a function to create and manipulate a curve in the scene, and a function to calculate the coordinates in the curve.

To create a curve, first, a curve object is created and linked to the scene, and from the curve object, the kind of curve is defined. Then from all the coordinates in the generated pattern a new point in the curve line is added and located at the appropriate coordinates. Finally, a bevel is added to the curve to create a solid geometry.

The pattern calculating function will take as argument two wrapper functions and a limit for a mesh grid. The wrapper function arguments will be the x-axis and y-axis coordinates. That design will be helpful to create new patterns. Then those functions will be evaluated through a mesh grid object. Finally, the resulting patter values will be scaled so it can fit in the camera.

With all in place, a simple material can be added to the curve object to generate a pattern.
Tensorflow 2 YOLOv3-Tiny object detection implementation
By: Rokas Balsys
Tags: Machine Learning, Data Science, Computer Vision, Computer Science, Deep Learning

In this tutorial, you will learn how to utilize YOLOv3-Tiny the same as we did for YOLOv3 for near real-time object detection.

The YOLO object detector is often cited as being one of the fastest deep learning-based object detectors, achieving a higher FPS rate than computationally expensive two-stage detectors (ex. Faster R-CNN) and some single-stage detectors (ex. RetinaNet and some, but not all, variations of SSDs).

However, even with all that speed, YOLOv3 is still not fast enough to run on some specific tasks or embedded devices such as the Raspberry Pi.

To help make YOLOv3 even faster, Redmon et al. (the creators of YOLO), defined a variation of the YOLO architecture called YOLOv3-Tiny.

Looking at the results from pjreddie.com (image below) the YOLOv3-Tiny architecture is approximately 6 times faster than it’s larger big brothers, achieving upwards of 220 FPS on a single GPU.

The small model size and fast inference speed make the YOLOv3-Tiny object detector naturally suited for embedded computer vision/deep learning devices such as the Raspberry Pi, Google Coral, NVIDIA Jetson Nano, or desktop CPU computer where your task requires higher FPS rate than you can get with YOLOv3 model.

In this post, you’ll learn how to use and train YOLOv3-Tiny the same way as we used in my previous tutorials.

The downside, of course, is that YOLOv3-Tiny tends to be less accurate because it is a smaller version of its big brother.

For reference, Redmon et al. report ~51–58% mAP for YOLOv3 on the COCO benchmark dataset while YOLOv3-Tiny is only 33.1% mAP — almost less than half of the accuracy of its bigger brothers.

That said, 33% mAP is still reasonable enough for some applications.

As I said, on a standard computer with a Graphics Processing Unit (GPU), it is easy for YOLOv3 to achieve real‐time performance. However, in the miniaturized embedded devices, such as Raspberry PI, the conventional YOLOv3 algorithm runs slowly. The YOLOv3‐Tiny network can basically satisfy real‐time requirements based on limited hardware resources. Therefore, in this tutorial, I will show you, how to run the YOLOv3‐Tiny algorithm. YOLOv3‐Tiny instead of Darknet53 has a backbone of the Darknet19, the structure of it is shown in the following image:

You Only Look Once v3‐Tiny (YOLOv3‐Tiny) network structure

This above structure enables the YOLOv3‐Tiny network to achieve the desired effect in miniaturized devices.

Same as in the YOLOv3 tutorial, seeing Darknet-19 and above YOLOv3-Tiny structure, we can’t fully understand all layers and how to implement it. This is why I have one more figure with the overall architecture of the YOLOv3-Tiny network. In the picture below, we can see that the input picture of size 416x416 gets 2 branches after entering the Darknet-19 network. These branches undergo a series of convolutions, upsampling, merging, and other operations. Two feature maps with different sizes are finally obtained, with shapes of [13, 13, 255] and [26, 26, 255]:
Native Tongue
Tags: Language, African, Creativity, Culture, Colonization

Native Tongue

It leaves you, once again

Image by Bashar Alaeddin on Unsplash

They visit you sometimes,

running across your colonized tongue

as it clumsily stumbles over their sharp edges.

When they start to retreat,

you savour the taste and let them go,

as they will come back

when you are overflowing with desperation

and leave you all over again.
Your Rocket Is Now Boarding
By: Craig Brett
Tags: Travel, Space, Life, Future, Technology

Spaceports

This sounds like something you read in science fiction but there are currently 22 spaceports in the world today and dozens more are on the way.

Many of these aren’t going to be your stereotypical launch sites where miles of barbed wire surround cement pads in the middle of nowhere. No, many will look like this spaceport design that was developed for the Spaceport Japan Association.

designboom.com

There are so many spaceports coming that it’s hard to keep track. In the US, the planned and active sites can be found all over the country from Kodiak island in Alaska down to the new Spaceport America in New Mexico.

The UK will have at least three and Japan just approved a spaceport for Virgin Galactic. India is building a second in the country’s south and Indonesia is building one off the coast of New Guinea. New Zealand, Kenya, the Azores islands and Brazil all have sites planned or in use already.

SpaceX (who else) is even planning floating spaceports that will be connected to shore by hyperloops.

All of these spaceports have one thing in common — serving the explosive space economy that is expected to be worth $1 trillion this year.

Whether it’s tourism in orbit, carrying cargo to the several new space stations now being built or sending up thousands of more satellites, our future in space promises to be incredibly lucrative.

At some point we will even have factories in space, where micro-gravity environments can be extremely beneficial for manufacturing. The company Made in Space, for example, will be sending the first ceramics factory to the ISS station soon.

This site regularly updates a long list of companies planning to use space for building new products. These include the very high-tech things you expect to make in space like complex proteins and fiber optics, but also stuff like new types of beer and roasted coffee.
Why Too Many Hints are Bad for Improvement
By: Jean-Marc Buchert
Tags: Growth, Creativity, Self Improvement, Life Lessons, Learning

Making Sense Through Systemic Analogies

When Kepler noticed in his astronomical observations that the stars gravitated irregularly, he began an investigation to understand the forces that animated their movement.

However, as he was the first to follow this trail, he had very few conceptual resources to explain this phenomenon. So to fill the gap he decided to rely on more distant concepts such as from chemistry and elementary physics. For example, he assumed that the star was moving like waves on water or sound vibration.

What Kepler had then used was the power of analogies to study and clarify new and complex problems. These similarities found themselves especially useful for him to discover structural similarities between apparently different objects.

And this conceptual use of analogy was studied by management researchers at the Oxford Business School. Analyzing the way consultants and investors evaluated the risks of their projects compared to those of others, they noticed that they systematically underestimated them. Too focused on the specifics of their project, they were locked into an “inside view” that prevented them from seeing the results of other projects similar to them.

For example, consultants looking to invent a new tram system for a city in Scotland overspent their budget estimates by a factor of two by focusing on rigorous financial analysis of every aspect of their project, rather than comparing it systemically with other similar projects in Europe.

On the contrary, what the researchers have called the ‘outside view’ is to rely not on our experience or familiar analogies but distant and truly relevant analogies. When you base your thinking on various perspectives, you are better able to focus on real metrics and implications that you found otherwise.

If you want to raise your broad mind, you may better judge the risks and probabilities of your project by comparing it with examples from other horizons. Your improvising thinking will then shine and get you through unexpected consequences.
Chapter 3 : Numpy, Scipy and Matplotlib
By: Simone Azeglio
Tags: Machine Learning, Python, Data Science, Programming, Data Visualization

Hello Learners and Welcome Back!

There’s a new chapter in town! We’re glad to introduce you to our new friends: NumPy, SciPy and Matplotlib.

Populating the environment

Together with Pandas (go back to Chapter 2 for a brief introduction) they constitute the very foundations of scientific programming in Python.

As we told you before, scientific programming is considered as an appetizer for Machine Learning, so take your time to complete these notebooks.

You can find them on our Github, remember to follow us there for updates.

Have fun!

Simone, MLJC

P.S. If you find any bug / error or you have some problems with the code feel free to contact us at mljcunito@gmail.com
DoWhy — Python Library for Causal Inference by Microsoft
By: Mikhail Raevskiy
Tags: Machine Learning, Python, Microsoft, Programming, Software Development

DoWhy is a recently published python library that aims to make Casual Inference easy

Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed

Difficulties in finding cause and effect relationships

Current methods for finding relationships in data represent a simplified version of causal explanation. Most of the models that try to find causes in the data rely on empirical analysis. Pure causal inference relies on counterfactual analysis, which is more closely related to how people make decisions.

The causal inference depends on predicting objects that the model has not previously observed. This provokes a fundamental problem, which is that it is impossible to objectively evaluate causal inference algorithms using a test sample. The problem imposes restrictions on the data generation process. Traditional machine learning methods turn a blind eye to the fundamental problem, which leads to the limited generalizability of such models.

More about DoWHy library

DoWhy. Separating identification and estimation of a causal effect. Source: Microsoft Blog

The objective is modeled as a causal graph so that all assumptions are explicitly specified. DoWhy provides a unified interface for popular causal inference techniques. The library combines two large frameworks: graph models and potential outcomes. DoWhy allows you to test the validity of assumptions, if possible and evaluates the robustness of predictions.

In DoWhy, the recognition of the causal effect and its assessment are divided into two separate parts. DoWhy models the problem of causal inference in a workflow, which consists of four steps: modeling, recognition, evaluation, and refutation.

Workflow visualization in DoWhy. Source: Microsoft Blog

Code Example

Causal inference in four lines. A sample run of DoWhy. Source: Microsoft Blog

Read More

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10 Skills to Help You Become a Product Manager
By: Dhananjay Trivedi
Tags: Careers, Programming, Business, Startup, Product Management

The Hard Skills

1. Problem-solving attitude

When we are given a problem can we find a unique, different, and feasible solution to it or we just feel like giving up on it? That problem-solving attitude is the thing we need to become a great product manager.

Programming definitely helps us improve the way we think and approach a problem when we are trying to solve it, hence that skill will pay-off so much when we move into product management.

Be someone who exercises the mind in an effort to reach a decision.

2. Passion and understanding of technology

The best part is that we don’t really have to be a kick-ass coder or a gadget geek but we should be able to appreciate and understand how different apps, technologies, and products work.

This skill would also come in handy if we don’t want to be laughed at by the engineers you would be working with.

3. Deep understanding of the users

Many times, we might fail enough to really understand your end-users who actually are gonna use your product.

There might be people from different cultures, backgrounds, very different from you and your perspective. Research and invest your time in understanding them.

You should be able to build an application that is inclusive.

4. An eye for a good user experience

When there is a particular problem you might be trying to solve through a feature, there can be multiple ways to implement that feature where your user will go through a journey of steps to actually complete it.

You should be able to think and compare these different user-experiences and evaluate them to make the decision on which path to chose for your users.

Connecting the use of technology with a great user experience is a must-have skill for a product manager.

5. Strong business sense

This is where I have seen a lot of developers lacking, including me. The sooner we realize and start appreciating the business side of the problems, the better for us and our companies.

It really doesn’t matter how technologically good our system/application is if it doesn’t fit the business timeline and feasibility.

As a Product Manager, we should be able to help make the trade-off wherever required to build the right application for the users with the right technology meeting the business goals.

Keeping an eye out for the companies business goals while delivering the product is another important skill to have.

6. Understanding analytics

Data is what drives businesses today. The ability to understand and extract out insights from the data that your application is generating can help you make the right decisions for your product as well.

Again you might have data-analysts or data engineers in your team, you should be able to communicate and understand them, you don’t have to be the expert at it.
What Is Reddit Scared of?
By: William Chon
Tags: Society, Life, Culture, Social Media, Data Science

What Is Reddit Scared of?

Ranking the Most Popular Phobia Subreddits

What fears do we share with the others around us? A particular type of fear that I find especially interesting are phobias, or an irrational fear of something that’s unlikely to cause harm.

If you were to try to learn more about phobias on Google, all that comes up are a bunch of generic “Top 10” lists based on therapist interviews. I wanted to take a more data-driven approach to rank phobias, and Reddit is the perfect source to give us insight into the most engaging fears that we share.
AWS Sentiment Analysis of Web-Scraped Employee Reviews
By: Helmeczy Brúnó
Tags: R, AWS, Web Scraping, Amazon Comprehend, Glassdoor

Introduction

Hi! This post born as a means to fulfill a homework assignment on putting various AWS products to use in a practical setting. Alternatively, a simple step-by-step guide would have sufficed, but that probably would be more dreadful both to do & to read. Anyhow, when thinking of use-cases for speech recognition, translation, or comprehension, you would be forgiven to have your imagination go wild.

Indeed, using AWS translation & comprehension/sentiment analysis services on various news websites’ articles to compare sentiments came to my mind. Being more practical myself, & generally feeling the need to come up with something of more return on invested effort, I looked for something more immediately relevant to my professional circumstances:

I’m a 26 years old MSc Business Analytics Student at CEU, with an undergrad in Business Administration in Hotel Management & 5 years of experience in that field. As a relatively newbie to Data Science, Analytics & all that, 2 things constantly on my mind are a. Finding employment in the field & b. Trying to gain a level of domain/market understanding similar to what I came to know with Hotels.

Obviously, 1 project is not going to achieve either, but I think I might just be able to put my web-scraping skills to the test, and incorporate AWS Comprehend into the workflow, to build something helping me get a sense of how employers are in this field, in the sense that what employees are posting on a major employment website, Glassdoor. For purposes of this post I’ll focus on the IT sector, though obviously condensing down Data Science & Analytics to 1 sector is rather limited.

The generalized use-case is figuring out how the company is perceived by its employees. You’d be wise to say the approach below does not answer this question in itself, after all, Amazon Comprehend can ‘only’ categorize reviews by its detected sentiments, as well as to detect key phrases within each review. I will illustrate this with 1000 reviews on the 10 best-ranked Tech companies, per Glassdoor’s company ranking, having scraped their Companies’ & Reviews’ sections.
The Dark Backstory Behind Some Popular Christmas Carols
By: Kyrie Gray
Tags: Satire, Humor, Culture, Christmas, Music

Every year we sing the same carols without really thinking about the words. The tunes are just so catchy! Plus no one wants to believe that those tunes they associate with their favorite holiday are actually harboring dark stories behind their bright façades.

We Wish You a Merry Christmas

The song starts off innocently enough, with the carolers wishing the residents of a home a, “Merry Christmas.” However, in the second verse, the crowd’s demand for figgy pudding is unmet. They then ominously sing, “We won’t leave until we get some.” And they are true to their word.

After breaking into the household, they hold the family hostage for hours. Finally, the police negotiate 3 figgy puddings per caroler. Though they go to jail, the sadistic singers know that the family will forever think of them when they hear that familiar song. That feeling of power is sweeter than any pudding.

Jingle Bells

Yes, it is very fun to go, “Dashing through the snow in a one-horse open sleigh.” With bells and warm gloves what could go wrong? Well, one thing a sleigh lacks is a seatbelt.

Tom found this out the hard way when he took his sweetheart on what he thought would be a romantic date. He even recorded the words to the song as they sped through the park. All was well until the sled hit a root, and Tom was thrown into a tree. The “OH,” in the song is often mistranslated for a fun breath of air to take rather than the very real surprise of being launched from a sleigh. No one was laughing all the way to the emergency room, because of the horrific injuries and stuff.

12 Days of Christmas

Many see this as a sweet song about someone’s significant other sending them wonderful gifts for 12 days straight. Truthfully, this song is about a person being harassed by a stalker who keeps sending bigger, and more annoying, gifts to get a reaction. The ladies, lords, maids, and musicians beg the person to respond to the stalker since they’ve been threatened with death if they stop their designated action or try to contact the authorities.

Oh Christmas Tree

Who doesn’t love a picturesque Christmas tree? Those who have never seen one, that’s who. Few know this song is actually a transmission from the future. The “leaves so unchanging” refer to the fake leaves that are made of a synthetic material that doesn’t burn. Which was the fate of most of the trees in the world.

In the time the song sent from, no one would even dream of cutting down a rare tree just so they could put it in their living room for a week. To do so would be immoral, and a crime punishable by 20 years in prison.
The Medical History of Sex Toys
By: Sexedplus Dan
Tags: Sex, Sexuality, History, Health, Comics

And here’s a wonderful article about that story of vibrators being used to induce orgasms as a medical treatment.
Fellow Marketers: Please, Stop With the Buzzwords
By: Liana Buenaventura
Tags: Marketing Tips, Communication, Marketing, Business, Communication Skills

Fellow Marketers: Please, Stop With the Buzzwords

A go-forward actionable plan to optimize your vocabulary

There are no points for poor communication. Photo by Joshua Miranda from Pexels

I didn’t need coffee to fuel me that morning. The high of giving my first presentation to our biggest client at my new job provided all the adrenaline I needed.

The strategy was sound, the creativity was stunning, and the market insights were fresh. This was my time to shine and prove to my colleagues and clients that they made the right choice in headhunting and relocating me across the country for the role.

I envisioned looks of astonishment because I felt the presentation was that good. Heck, maybe even a high-five or proclamation of “this is exactly why we chose you!” from the client when we landed on the token “Thank You” slide.

But instead, the vice president of my client’s business turned to me at the end and said, “This is great. Now can you say it again in English?”

The problem was I did say it in English.
Darjan Hil, video collection
By: Darjan Hil
Tags: Design, Information Design, Talks, Data Visualization, Infographics

I have tried to collect all the videos which are somewhere in the internet in one article. This helps me to share the videos in one place, but also to keep an overview on all the talks me or a YAAY team member had. The internet never forgets, so let’s keep at least an overview.

Meetup Talk: Labs for the European Union and public health

Date: March 2019 / Language: English

Conference Keynote: Visualisierung auf den Punkt bzw. Bubble gebracht

Date: November 2018 / Sprache: Deutsch

Meetup Talk: The power of information design and so portfolio examples

Date: August 2018 / Language: English

Conference Talk: EDCH Munich — every dot on the paper must have a meaning

Date: März 2018 / Sprache: Deutsch

Nicole Lachenmeier, Danilo Wanner: Die 7 Prinzipien von YAAY, was wir gelernt haben

Date: November 2017 / Sprache: Deutsch

LectureTalk: Visualize your research

Date: Dezember 2016 / Sprache Deutsch

Imagefilm: YAAY @ Swiss Cultural Challenge

Date: November 2016 / Sprache: Deutsch

Hackaton Concept: Visual Exploration of Vesalius Fabrica

Date: Juli 2016 / Language: English

Indre Grumbinaite: The way we work at YAAY

Date: November 2015 / Language: English

YAAY Manifesto: Team answers questions visually

Date: Juni 2015/ Language: Visual

HGK Experteninterview: Die Geschichte hinter YAAY

Date: Dezember 2014 / Sprache: Deutsch

Campus Talk: Visuell kommuniziert es sich leichter

Date: Mai 2014 / Sprache: Deutsch

Research Project: Development of a Content Independent Game Framework — Tourney

Date: August 2013 / Sprache: Deutsch

Research Project: Scientific Communication at the University — Blog Design

Date: Juni 2013 / Sprache: Deutsch
React 2020 — P4: Functional Component Props
By: Dino Cajic
Tags: React, Reactjs, JavaScript, Programming, Web Development

In the previous article, we created two functional components: Nissan and Toyota. We imported those components into the App component and rendered them. If we wanted to render other cars, we would have to generate a specific component for each of them.

If you look at the two components that we created, you will see that the only difference between them is the string that’s rendered and the component name. Wouldn’t it be better to have a generic Car component that accepts the year, make, and model of a car as arguments and displays whatever vehicle we pass to it? Well that’s what this topic is about.

Let’s create a Car functional component and add Year/Make/Model as the string placeholder.

If we import the Car component into App and render it, we can see it displayed in our browsers.

How do we pass arguments to components? Arguments are passed in the form of props. Props is just short for properties. Props allow you to pass custom data to the React component. Props will be passed to the component when we’re rendering the component. So, in the App component, we’re rendering the component <Car />. We’ll create custom prop names that will be attached to React’s props object, which React will make accessible to each React component.

Let’s create a couple of custom props for our Car component. Props resemble HTML attributes to me. For example, the <img> tag has an attribute called src: <img src=””>. We’ll create three “attributes” for our <Car /> tag (located in src/App.js) and we’ll name them year, make, and model.

<Car year="1990" make="Nissan" model="240sx" />

Each one of these props, year, make, and model, will be attached to the props object by React automatically. If you look at your browser, you’ll notice that nothing has changed since these properties are not being utilized anywhere. How can we display these arguments instead of the string “Year Make Model?” We’ll have to modify the Car component.

In order to use the props object inside of a functional component, you’ll have to declare props as a parameter inside of your functional component declaration.

function Car(props) { ... }

Now, with React magic, you have access to the props object with all of your custom attributes. To access the props object inside of the JSX that’s returned, you’ll have to use curly braces. You use curly braces to access objects, variables, etc, inside of JSX. We’ll cover JSX in more detail in a later article.

<div>{ props }</div>

To access the property that was just created, you use the dot operator on the props object. For example, to access the year property, type in props.year. We can repeat the process for make and model.

If you look at your browser, you’ll see that the arguments have made their way into the Car component.

We can now create as many Car components as we want.

If we look at our browser, we can verify that the 2 new vehicles are displayed.

That’s all their is to it. We’ll look at creating class components and passing arguments to them in the next couple of articles.
Top 5 Funny Swiss Expressions That Can’t Be Translated Into English
By: Nicolas Carteron
Tags: Travel, Society, Friendship, Culture, Language

Top 5 Funny Swiss Expressions That Can’t Be Translated Into English

Read it and I promise you’ll be “déçu en bien”

The limits of my language mean the limits of my world

We humans speak over 7,000 different languages. Each of them has its own peculiarities, its own quirks that provide its speakers with a unique understanding of the world. Each of them is a tool to create art and meaning in a separate, singular way. When one has the opportunity to learn a new language, one suddenly enters a parallel universe. Responding to Wittgenstein's quote above, one suddenly broaden the limits of one’s world.

Every language has its idioms, grammatic and syntactic constructs, and pronunciation oddities that provide it with a character like no other. Every one of them has ways to express feelings, situations, or ideas that can’t be translated.

I come from Switzerland, and my mother tongue is French, Swiss-French to be specific. It is the same language our neighbours to the West speak. A Swiss can understand and be understood 100% by a French. There are tiny differences here and there, however. We have an accent French people tend to make fun of. We also use older, out-of-fashion words for which they tend to make fun of us too.

Overall, French people tend to make fun of us for pretty much anything. But we Swiss don’t hold it against them. We know they’re just jealous. Look at the picture above. We live in Rivendell, who wouldn’t want that!

Our version of French is full of expressions that are not only untranslatable in English, but also incomprehensible for French speakers. Here are 5 of my favourites.
An Instant Classic: “My Octopus Teacher”
By: Fahri Karakas
Tags: Nature, Creativity, Life Lessons, Culture, Life

An Instant Classic: “My Octopus Teacher”

This documentary will change your life

I have just watched a fascinating documentary about an octopus, but it has been so much more than I expected. “My Octopus Teacher” is a film about the ocean, underwater creatures, ecology, curiosity, creativity, wildlife, sea forests, spirituality, nature, and interconnectedness.

Craig Foster is a diver and wildlife filmmaker who was in a dark and stressful period in his life. He begins his therapy by diving the dark waters of the ocean in Western Cape and learns to develop a deeper appreciation of his natural surroundings. He explores how he is connected to the world around him.

In particular, Craig becomes obsessed with this octopus. One day, Craig sees a strange shape on the ocean floor: An octopus is covered with an armor of rocks and shells. He decides there is a lot to learn here and he dives and visits the den of the octopus every day.

Craig decides to follow the life of this octopus for over 300 days. Eventually, the octopus realizes that Craig is not a threat, but a friend. The octopus allows Craig into her life. Both Craig and the octopus are very curious and it is a symbiotic dance of mutual learning and exploration. Craig spends a lot of time with the octopus, develops this amazing empathy and gentleness, and learns a great deal about life, nature, and interconnectedness. Watch the trailer here:

Craig’s underwater journey is a breathtaking one. He finds a new passion to explore life beneath the water and connects with an octopus in a pretty amazing way. It is a love story and it shows you what happens when you care about, learn from, and interact with an octopus. It is such an emotional story that you find yourself mysteriously attracted to this strange underwater creature and this sea forest.

There are all these heartwarming and heartbreaking moments about the life of this octopus. You learn how she becomes a hunter at night, how she saves herself from the shark attacks through the most incredible methods, how she interacts with her habitat, how she reproduces, and how she dies. You learn about the enormous creativity and intelligence of this animal that was sharpened through millions of years of evolution. The connection you feel is so deep and creative that you might find yourself crying.

This film is truly a gift for your humanity, your curiosity, and your spirit. The cinematography is fascinating — it is a feast for your eyes and soul. It reminds you of how we are all in this wonderful world together and sharing the wonders of this life and nature. It is so easy to miss and forget the magic of all. We should boldly immerse ourselves within the natural world so that we become a harmonious part of it.

Each life is very fragile, precious, beautiful, and interconnected. Therefore, we must cherish and protect the life of each living being on this planet.
SqR00t Offensive Security Tech Talks
Tags: Security, Infosec, Engineering, Privacy, Data Security

Heads up, we’ve moved! If you’d like to continue keeping up with the latest technical content from Square please visit us at our new home https://developer.squareup.com/blog

Square’s Information Security team runs a quarterly security meetup, Square R00t, which features several security- and data privacy-related lightning talks.

Recently, Jason Haddix, Chris Rohlf, Jake Heath, and Michael Roberts gave infosec talks at our “Square SqR00t — Offensive Security” event in Square’s San Francisco office. (Thanks to everyone who attended!) Two of these talks were recorded and are now available on our Square Engineering YouTube channel. Enjoy!

Tracing User Input Through JS is for Tools — Jake Heath and Michael Roberts

Jake and Michael present a tool they wrote to automate the process of finding cross site scripting on a penetration test.

Emergent Recon — Jason Haddix

Jason presents his methodology for finding targets against a company that he needs to hack.

If you’re interested in seeing more talks like these (or giving a talk!), be sure to attend our next Square R00t events! We have two events planned for Q4 2018:

Our first Square R00t in NYC on November 14, 2018.

Our next San Francisco event occurs on December.

We’ll share more details on the SquareEng Twitter account once we get closer.

Don’t forget to subscribe to the YouTube channel for more great content in the future!
Dynamically Adding Lines to a Plotly Plot in R
By: Raphael Fix
Tags: Plotly, R, Data Visualization

Since Plotly made their interactive graphing platform available for R, I’ve been trying to incorporate it into more of my projects. Recently I was working on a graph that included the price of a particular stock as well as multiple moving averages and thought it could be useful to use vertical lines to help better define buy and sell points for the viewer. While ggplot2 allows users to pass a single vector of values into the geom_vline() argument to do this, Plotly appears to require a separate list of arguments for each line (possibly as a result of the complete control they give you over the positioning of shapes). Below is a snippet of the data I used to build the graph. Full data available here.

A snapshot of the full GE price dataset

Below is an example of the arguments necessary to build a vertical line from the x axis to the adjusted price for GE on 2–11–1997.

p <- layout(p, shapes = list(type = “line”, fillcolor = “blue”,

line = list(color = “blue”),

opacity = 0.3,

x0 = “1997–02–11”,

x1 = “1997–02–11”,

xref = “x”,

y0 = 0,

y1 = 10.22,

yref = “y”))

Obviously drawing more than a couple lines in this manner would be very clumsy. My goal was to point out crossover points between the 2 EMAs, which means a non-programmatic approach would be very impractical. To make this task easier, I created a simple “for” loop to identify crossovers indicating a “buy” (in green) or a “sell” (in red).

First I used dplyr to filter out all the days where the EMAs did not cross:

line_dat <-

dat %>%

filter(dat$signal != '--')

Then I created a list of lists, with each of the nested lists containing the arguments for one line. To make it easier for the end user to distinguish between “buy” and “sell” days, I used an if-else statement to alter the line color for each iteration depending on the signal.

line_list <- list()

for(i in 1:nrow(line_dat)){

line_color <- ifelse(line_dat$signal[i] == 'buy','green','red')

line_list[[i]] <-

list(type = “line”,

fillcolor = line_color,

line = list(color = line_color),

opacity = 0.3,

x0 = line_dat[[1]][i],

x1 = line_dat[[1]][i],

xref = “x”,

y0 = 0,

y1 = line_dat[[2]][i],

yref = “y”)

}

Below is the resulting graph

I’ve already found other uses for Plotly at work, and I’m excited to see how I can work it into my R workflow.
What’s The Secret To Achieving Your Must-Have Outcomes?
By: Randy Wolken
Tags: Leadership, Business, Self Improvement, Self-awareness, Life Lessons

Answer for Me

Early in my career, I was amazed by how some individuals and organizations were successful while others were not. Some just stood out in the crowd. So, I begin to ask them why they were so successful. Were they smarter, or did they work harder? Were they luckier, or did they have it given to them? Nope. Nearly every one of them had learned the system for achieving outstanding outcomes day-in and day-out.

They create OKRs (Outcomes and Key Results) for their lives and businesses. I discovered that being smarter, working harder, being luckier, and have others give it to you does help. But, it was not the cause of their success. Being clear about what they wanted — their outcomes — matter much more.

Once they were clear about their outcomes, they determined how to measure them with crucial results. And finally, they kept score and measured these essential areas. Now, all that they did could be focused on their key outcomes and results. Amazing things happened then.

I then began to apply this approach to my own life and career.

E ach day I remind myself what my key goals or outcomes are. I then determine how I know when I have achieved them.

I measure my activists and results on a daily and weekly basis. Over time, with work and focus, I perform them.

I have had a fair amount of success in being a husband, father, friend, son, business leader, and community leader. I am not smarter, and I don’t work harder than most others. I am just more focused on my OKRs.
Trading your coffee cup
By: Thuận Sarzynski
Tags: Supply Chain, Sustainability, Food, Coffee, Business

Trading coffee is not an easy job. In a recent trip to Vietnam, I had the chance to meet a coffee trader and visit his wet mill.

First of all, let’s remember that there are two coffee species traded around the world; coffee arabica which is 60% of the traded coffee, and coffee robusta for the other 40%. Coffee arabica is mostly used for drinks while robusta even though it can be drunk as a low-quality coffee, it is more often transformed into medicines and food products.

Stocks of coffee arabica are traded at the New York stock exchange while for robusta it is traded at the London stock exchange. These platforms are used to exchange coffee futures, which are stocks used by traders to limit their exposure to coffee supply changes and price volatility. Every time, my trader friend buys real coffee from farmers, he sells a coffee future; and every time he sells coffee, he buys a coffee future. Traders make money from the price difference between the value of coffee futures and the price of real coffee.

Notice all the samples of coffee beans stored in plastic boxes?

The biggest difficulty of my friend is that he must always check coffee prices on his phone until 1:30 in the morning in Vietnam, time at which London Stock Exchange closes. Vietnam being the first exporter of coffee Robusta, most of the transaction of my friend happens in London. Once the London Stock Exchange closes, my friend checks the closing price of coffee, writes it down and sends it to all coffee merchandisers in the country who buy coffee directly from farmers.
Python Dictionary Methods — Explained
By: Mikhail Raevskiy
Tags: Programming, Data Science, Software Development, Python, Technology

❓ What is a dictionary ❓

Dictionary is an unordered data structure that allows you to store key-value pairs. Here’s an example of a simple Python dictionary:

This dictionary uses strings as keys, but the key can be, in principle, any immutable data type. The value of a particular key can be anything. Here’s another example of a dictionary where keys are numbers and values are strings:

An important clarification: if you try to use a mutable data type as a key (e.x. list), you will get an error:

In fact, the problem is not with mutable data types, but with non-hashable data types, but usually, they are the same thing.

Retrieving data from a dictionary 📖

Square brackets are used to get the value of a particular key [] . Suppose we have a pair in our dictionary 'marathon': 26 :

Again, you will get an error if you try to retrieve a value for a non-existent key. There are methods to avoid such mistakes, which we will talk about now.

Adding and updating keys 🔑

Adding new pairs to the dictionary is quite simple:

Updating existing values is exactly the same:

Deleting keys ❌
It’s Possible You Don’t Have Time for a ’90s A Capella Dance Medley
By: Nicole Cliffe
Tags: The Nineties, Danes Ftw, Music

…by actual Danish people, but, you know, maybe you should go read The New York Review of Books, or something.

Yes, there’s Ace of Base.
MerzFiles #04: I am an AI-driven troll (probably).
By: Vlad Alex, Merzmensch
Tags: Artificial Intelligence, Art, Culture, Videogames, Transmedia

MerzFiles #04: I am an AI-driven troll (probably).

I greet you, my dear 45!

So you are probably aware of my biggest sin — I begin series. And then I begin a new one, and so on. Sequential thinking? Anyway, I continue my series sometimes, oh yes I do.

So here’s a deal: when we will be 100 subscribers, I’ll tell you more about my series.

Today I just tell about “Our Research Facility”.

I started this series on Instagram (are we following us?) years ago. It was a small portal into another dimension, full of mad scientists, mysteries, and other weird stuff.

And now I will invade Medium with this stuff from an alternate reality. For example this:

Meaning of Life

Reports from our Research Facility

No more, no less.

Apropos, meaning of life. Here is my favorite, written by Artificial Intelligence (still GPT-2, back to 2019):
Best Libraries and Platforms for Data Visualization
Tags: Big Data, Technology, Data Visualization, Data Science, Programming

In one of our previous posts we discussed data visualization and the techniques used both in regular projects and in Big Data analysis.

However, knowing the plot does not let you go beyond theoretical understanding of what tool to apply for certain data. With the abundance of techniques, the data visualization world can overwhelm the newcomer. Here we have collected some best data visualization libraries and platforms.

Data visualization libraries

Though all of the most popular languages in Data Science have built-in functions to create standard plots, building a custom plot usually requires more efforts. To address the necessity to plot versatile formats and types of data. Some of the most effective libraries for popular Data Science languages include the following:

Some of the most effective libraries for popular Data Science languages

R

The R language provides numerous opportunities for data visualization — and around 12,500 packages in the CRAN repository of R packages. This means there are packages for practically any data visualization task regardless the discipline. However, if we choose several that suit most of the task, we’d select the following:

ggplot2

ggplot2 is based on The Grammar of Graphics, a system for understanding graphics as composed of various layers that together create a complete plot. Its powerful model of graphics simplifies building complex multi-layered graphics. Besides, the flexibility it offers allows you, for example, to start building your plot with axes, then add points, then a line, a confidence interval, and so on.

ggplot2 is slower than base R and rather difficult to master, it pays huge dividends for any data scientist working in R.

Lattice

Lattice is a system of plotting inspired by Trellis graphics. It helps visualize multi-variate data, creating tiled panels of plots to compare different values or subgroups of a given variable. Lattice is built using the grid package for its underlying implementation and it inherits many grid’s features. Therefore, the logic of Lattice should feel familiar to many R users making it easier to work with.

RGL

rgl package is used to create interactive 3D plots. Like Lattice, it’s inspired by the grid package, though it’s not compatible with it. RGL features a variety of 3D shapes to choose from, lighting effects, various “materials” for the objects, as well as the ability to make an animation.
But The Ghosts Came Anyway
By: Matthew Donnellon
Tags: Creative Writing, Poetry, Creativity, This Happened To Me, Poetry On Medium

Written by

Matthew Donnellon is a writer, artist, and sit down comedian. He is the author of The Curious Case of Emma Lee and Other Stories.