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Deemples is a golf app that helps golfers find a game whenever, wherever. Join available games, or create your own and let others join you. There are 60 million golfers and 30,000 golf courses around the world. Why stick to your own groups, when you can expand your network and play with new people every game. Organise your upcoming golf games, hassle-free! #nevergolfalone #noweveryonecangolf #deemplesgolf

Deemples

deemples

Deemples - a mix between matching + ecommerce. No one will pay for matching, unless volume is insane. But volume is not insane in golf, there are only 60mil golfers.

Why is it so hard to build a "tinder for golf".

HackerNoon Reporter: Please tell us briefly about your  background. My name is David Wong, born and raised in public schools in Malaysia, got a finance degree from Indiana University, spent a few years working across the US, Beijing, Shanghai, and Singapore, mainly in digital media advertising and technology, and now back in Malaysia chasing dreams and living cheaply. What's your startup called? And in a sentence or two, what does it do? The startup is called Deemples. It’s an app that golfers use to find each other and book tee times. What is the origin story? I started golfing 12 years ago, with a few friends that I “forced” into the game with me. Very quickly they decided that it wasn’t the game for them, but I loved it. Golf courses in Malaysia didn’t allow me to play alone, or match me up with other golfers at the golf course. I got frustrated, and was wondering, if there were platforms that could allow me to get a ride with a stranger, get food delivered by a stranger, stay at a strangers house, heck go on a date with a stranger, why wasn’t there anything for golfers? So we build Deemples, and today it has become the largest golf app in Malaysia :) What do you love about your team, and why are you the ones to solve this problem? The Deemples idea is not new. A lot of people have tried and failed in other parts of the world. Main reason why we think they’ve failed is the lack of skills in 1) product design/dev 2) user acquisition/retention 3) monetisation. I’m very fortunate the team we have does a good job in these 3 area, which has not only kept us alive, but thrived in an area where most have failed. If you weren’t building your startup, what would you be doing? I’d probably be based somewhere else outside of Malaysia, climbing the corporate ladder in the advertising or consumer product space, and golfing weekly. At the moment, how do you measure success? What are your core metrics? As any app product - core metrics are: downloads, monthly users, transactions, GMV. What’s most exciting about your traction to date? We were curious and found out that instead of a drop off that we expected since golfers had to make payment now, golfers came in more because golfers now have an additional reason to use Deemples aside from just finding golf buddies. Golfers were also egging each other on to make payment, so that they know their playing partner was confirmed to participate! As soon as monetisation features were available on Deemples, number of monthly users tripled. What technologies are you currently most excited about, and most worried about? And why? From a non-tech perspective, the technology that Deemples uses are not rocket science. Only when we get to a level of scale, would we use AI and recommendation algorithms to show the most relevant games to golfers. What drew you to get published on HackerNoon? What do you like most about our platform? Straight up, no bull, tech publication! Editor’s Note: YES #NOBULL 💚 What advice would you give to the 21-year-old version of yourself? Care less, and do more. I think at that time, I was falling into the mindset of how to do something that paid the most, especially when you see your peers’ successes. The same mindset that shapes you during formative years during your teens, came to play in the 21-30s, where you either made it early or you didn’t. Only later on do you realise, that focusing solely on the money wasn’t the best option, but using that 21-30s time to explore a ton of different things, to find out what truly you enjoyed doing was probably the better bet. So, care less, do more. What is something surprising you've learned this year that your contemporaries would benefit from knowing? Don’t regret all the mistakes you’ve made. Learn from it, and move forward with it. You can’t change the past, so don’t beat yourself up about it. But you can change the future, so let’s focus on that instead! Deemples was nominated as in Startups of the Year hosted by HackerNoon. one of the best startups in Kuala Lumpur

How Deemples CEO David Wong built the largest golf app in Malaysia 

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Let's learn about via these 77 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Laravel /Learn Repo 1. The Simple & Efficient Way to Enable JWT auth in Laravel By Using PHP-JWT Module I encounter this issue while working on an application for a client recently. Instead of using a JWT library build for Laravel, I used firebase/php-jwt to generate and authenticate users. Don’t ask why as there were many reasons behind this decision. If you are using any Laravel library for JWT like tymondesigns/jwt-auth then you don’t need this anyway, use the library if you want. 2. A Quick Guide to Upgrading Laravel to Version 9 🚀 How you can upgrade your laravel application to version 9 3. PHP Laravel VS Symfony: A Detailed Comparison of Web Development Frameworks When PHP developers need to prepare any enterprise-grade web application, they often prefer to use a framework. 4. How To Install Bash On Windows 10 Microsoft recently joined hands with Canonical (the parent company of Ubuntu) to bring the entire userland into Windows, minus the Linux Kernel. So as a result, you, as a developer, get access to the whole range of Ubuntu command-line tools and utilities with bash shell integrated with Windows. Let’s look at how we can install Bash on Windows 10 and take full advantage of the new feature. 5. My Laravel Repository Pattern Solution (in only 30 lines of code) A 30 line piece of script can finally provide a good solution for a repository pattern in Laravel apps. 6. 7 Laravel Tips You Should Know Hello, my name is Adrian. After writing an article about 6 Eloquent Tricks Every Laravel Developer Should Know and receiving a better than expected feedback from the readers, I decided to show some more Laravel tips that are used at Weap. 7. Error Messages Vs Validation Messages  in Laravel Blade I like to show error messages on top of the page and it’s pretty easy to do that with Laravel and Blade: 8. Laravel Nova Dashboard with Customizable Chart Most of the time in the part of development in software application, users need to have an admin page to show multiple data and information such as sales performance, operation performance, etc. As one of PHP framework, laravel built an administrative panel called laravel nova. Nova is a beautifully designed administration panel for Laravel. By using this tool, you can easily create and manage your admin panel of your website without having create it from scratch. The cool thing about Nova is it developed using PHP or Vue.js because of Laravel compatibility. Hence it makes your admin panel can be flawless and writing custom components is a cinch. 9. Laravel & Vuejs: A Miraculous Combination for Perfect Web Applications With 72% of the market share, PHP is one of the most popular and widely used programming languages. Laravel is a powerful Model-View-Controller based PHP framework for all of the web artisans. This open-source PHP framework was created for creating full-featured websites. It is the best choice for web app developers who seeks expressive, simple and elegant toolkit in order to create elegant applications with a good speed. The founders of Laravel value elegance, simplicity, and readability more than anything and designed this PHP framework upholding these values. It is PHP’s fastest-growing framework owing to its scalability, ease of use, and
flexibility. 10. Running Multiple Docker Containers With Custom Config Files Many of us using Docker to make our lives easier have ran into the issue of conflicting ports due to running more than 1 docker container. It’s easy to get bogged down into changing config files and squander an entire afternoon. I intend to write this article as a reminder to my future self whenever I need to set up multiple instances of docker container on a single machine. 11. Implement Live Video Streaming in a Laravel PHP Web Application Using LiveKit The Auction Events Platform for Creators is a Laravel PHP web application streaming live video from an auction event, using the LiveKit. 12. An SPA GUI Session as a Non-HttpOnly Cookie Are you writing a single page application or SPA? If so, you'd probably want to store some session data in the user's browser in order to keep track of the GUI's state. 13. How to set up your local projects to contribute to Laravel (or any PHP library) : a practical guide Let’s imagine this scenario: You are working on a Laravel application, and you found a bug either in the framework itself or in one of the third party libraries you are using. You edit the code inside your project, or maybe you installed a fresh copy of Laravel and library that has the issue, and you edited the code there. You saved the changes, but when you went to your terminal to commit the changes, GIT just ignored what you did. You start wondering “What’s happening here?”. 14. How to Setup a Powerful PhpStorm Configuration to Improve Laravel Development By just having the right IDE setup — you can learn and grow, meanwhile producing quality code and software. 15. How to Secure Your Laravel Application Nowadays, the tools we use to browse the internet require us to follow security standards. Otherwise, they tend to flag our web applications/sites as insecure. 16. Writing CASL React Abilities in a JSON File With a Laravel Artisan Command When writing a single page application or SPA you might inadvertently end up duplicating code because at the early stage of any project it is quite common to ask yourself so many questions. 17. Choosing Between Ruby on Rails and Laravel for App Development [A How To Guide] When you are looking for a software development partner for your app idea you obviously want to make sure, that your product will be developed in the best technology possible. 18. Laravel Real-Time Monitoring Using Inspector Hi, I'm Valerio, software engineer from Italy. 19. 14 Reasons Why Laravel Is The Best PHP Framework! The choice of which framework to use while developing an application depends on many factors, but just like any other application framework, Laravel does have its pros and cons. Leaving inside the hate, I would like to focus on the advantages of using the Laravel framework. 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How to Write Raw SQL Query Migration in Laravel We have created a laravel package to keep the track of raw SQL migration using package Laravel raw sql query migration 25. How to Build Resumable File Uploads into Your Web Apps With Tus, Laravel and Uppy A guide on how to implement resumable uploads in PHP with TUS, TUS-PHP and Uppy 26. A Better Way to Monitor Your Laravel Services Kubernetes or auto scaling can create a bit of mess in your monitoring data. Learn how to monitor your Laravel application by services instead of hostnames. 27. How to Add React.js to Laravel How to add ReactJS to your Laravel project 28. "Specified key was too long; max key length is 767 bytes" Error in Laravel One of these issues you might stumble across using the PHP framework Laravel is this one: 29. Laravel Homestead: Why, How, and When to Use It In Developement How to replicate your production environment in your local machine with a few configuration. 30. Building an Inventory Management App Using Vue.js and Laravel This article originally appeared on the Cosmic JS Blog. 31. How to Setup a Local Environment for Laravel Development With PHP, Node.Js, MySQL And Redis In this part of my series, we will install a minimum viable local environment for Laravel development. 32. Turn Your IDE Into a Powerhouse for Laravel Development With These Plugins for PhpStorm I will cover plugins that I use and recommend for your PhpStorm installation. 33. Explore the Value of Custom Rules and Data Validation (With Laravel) in Inspector The value a software provides to users is often a function of the quality of data it is able to manage. 34. Dockerize Your Laravel Project in a Jiffy The easiest way to dockerize your Laravel project. you can set up Laravel in just 10 minutes... 35. 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Why You Should Use Laravel PHP Framework for Web Development Comprehensive guide to web development using laravel framework. 41. What You Need to Know About Ruby on Rails to Start Developing Your First Application Ruby on Rails is a framework based on Ruby programming language, it is mainly used for web development. what do you need to know about it? 42. How Deemples CEO David Wong built the largest golf app in Malaysia Deemples was nominated as one of the best startups in Kuala Lumpur in Startups of the Year hosted by HackerNoon. Here's an interview with its CEO. 43. The 10+ Best Tools For Laravel Developers🛠 Check out the best tools for Laravel developers to boost the workflow. 44. Django Vs Laravel: The Ultimate Comparison Guide (2022) Are you looking for a comparison and an ultimate guide for Django vs laravel? You got the final destinations with features and benchmarks. 45. 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How to Handle All PHP Errors Sometimes your PHP application might produce many different types of earnings. 52. How to Spot N+1 SQL Query Problems Early for Laravel Projects My top favorite tools for spotting N+1 problems in database queries for Laravel projects. 53. Replace Comments With Better Code Hi, I’m Valerio, software engineer from Italy. 54. Adapter Design Pattern: Adapt Everything With Your Codes When we start building software, we can see many code's incompatibilities. This is because of many codes have been written based on different contracts. 55. Dirty Jobs: Debugging Till the Last Minute Debugging in practice means getting lost on tangents, trying to look good in front of your subordinates and doing just a little bit of debugging on the side. 56. Using ReactPHP to Stream Media A design for a streaming media server using ReactPHP and Laravel. 57. Using Components in Laravel: A Gentle Introduction In recent Laravel projects that I have been working on, I noticed that I reuse a lot of UI components on the same project but also on other coding projects. 58. Make Your Application Scalable Optimizing the ORM Performance In this article I would share a set of ORMs optimization strategies that I'm using in almost every backend service I'm working on. 59. Extending Laravel Artisan Commands 60. Laravel Real-Time Controllers Monitoring with Inspector-Laravel Package Hi, I'm Valerio, software engineer from Italy and CTO at Inspector. 61. Composer Packages that Every Laravel Developer Must Have I believe developers of middle and senior levels should always use these must-haves processes with at least one external data store. 62. Laravel Background Processes Analytics with Inspector In this article I would show you how to turn on analytics in the dark side of your application: "background Jobs and scheduled Artisan commands execution". 63. Development-Friendly Laravel Tools & Resources You can improve your productivity in Laravel by using these developer-friendly tools and resources to cut down on time spent on tedious, avoidable tasks. 64. Laravel Application Development: Introduction, Capabilities, and Websites If there is a framework that offers an exceptional platform for building robust web applications in PHP, it’s Laravel. Launched in 2011, the app development framework quickly started gaining ardent followers and became the most starred PHP framework on GitHub in 2015. 65. Step by Step Guide to Build the Laravel Web App Step by Step Guide to Build the Laravel Web App 66. Setting Up Laravel Unit and Security Tests This article provides a detailed explanation of Laravel Unit and Security Tests for web applications built using the Laravel framework. It explains the process 67. 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Set Up Laravel 6 on Google Cloud Run  with Continuous Integration [Step-by-Step Guide] Laravel has soared in popularity in the last few years. The Laravel community even says that Laravel has made writing PHP enjoyable than a pain. Laravel 6 has some interesting new features. Getting a super scaleable working URL for your application take hours if not days. Setting up something like Kubernetes is a huge task. This is where Google Cloud Run shines, you can get a working HTTPs URL for any of your containerized app in minutes. 73. Paypal Integration Package Built With PHP I have built a component PayPal integration package using PHP Laravel framework. 74. How Laravel Web Development Can Grow Your Business in 2020 and 2021 PHP web frameworks are the most popular sources for web application development. Laravel web development is one of the most important technologies for web application because of the MVC architecture. 75. Explaining Laravel Package Ecosystem Packages play a big role in the Laravel scene. How big and which packages?
To find it out we took the data from the Packagist.org API and filtered out the
Laravel packages. 76. 40 Must-Known Laravel Tools and Resources Laravel learning resources 77. [A How To Guide] Planning the Technology Stack for an Event Sourcing Project The most common question I encounter when training or consulting with developers, Engineers and Software development laboratories about a new Event Sourcing project is how and where do we start. Thank you for checking out the 77 most read stories about Laravel on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

77 Stories To Learn About Laravel

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! 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Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

$SHIB: My Journey Through the Deepest Recesses & High Command of a $10 Billion Decentralized Army

Along with my team, I’ve been involved in a project called , a novel approach that uses Deep Learning to identify marine-plastic and I wanted to share the findings: DeepPlastic 85% mean-average precision! Using our model, we can now detect on average 85% (mAP) of all epipelagic plastic in the ocean. We achieve this level of precision based on a Neural Network architecture called . Below we’ve attached a video containing examples of marine plastic that we’ve run our model on: YOLOv5-S Where’s the Paper and The Code? This article would mainly serve as a no-code summary of our research paper. If you’d like to skip straight to the nitty-gritty details of the neural network or the code, you can access them here: arxiv pre-print: https://arxiv.org/abs/2105.01882 Codebase and Dataset: https://zenodo.org/record/5562940#.YY1uML3MI-R If you’d like me to write another blog post explaining the code, data augmentation, etc., let me know in the comments below! How Did I Get Involved In This? I’ve always been passionate about the oceans. After graduating from school at California State University, Monterey Bay, I knew that I wanted to build tools/software that can help protect the ocean. After I collected, annotated, and curated the data along with the model, I realized that the precision metrics were significant, and there could be a manuscript that could be published here. So, I asked for help from , an expert in Marine plastics, and and for help with the writing and publishing of the manuscript. Dr. Sara-Jeanne Royer Jay Lowe Olivier Poirion Abstract The quantification of positively buoyant marine plastic debris is critical to understanding how concentrations of trash gather across the world’s oceans and identifying high concentration garbage hotspots in dire need of trash removal. Currently, the most common monitoring method to quantify floating plastic requires the use of a manta trawl. Before analysis, the need for physical removal incurs high costs and requires intensive labor–preventing scalable deployment of a real-time marine plastic monitoring service across the oceans. Without better monitoring and sampling methods, the total impact of plastic pollution on the environment as a whole, and details of impact within specific oceanic regions, will remain unknown. This study presents an automated workflow that utilizes videos and images captured within the epipelagic layer of the ocean as input and produces real-time quantification of marine plastic for accurate quantification and removal. YOLOv5-S was the best performing model, which operates at a Mean Average Precision (mAP) of 0.851 and an F1-Score of 0.89 while maintaining near real-time speed. In addition, our method can utilize” off the shelves” camera equipment and standard low-cost GPUs to monitor/quantify epipelagic plastic in near real-time. Goal For The Project We wanted to build a generalized object detector capable of identifying and quantifying sub-surface plastic around the world. Now that we understand our goal and how to achieve it, let us jump into the workflow. Curating The Dataset Finding a dataset that contained annotated pictures of marine debris was incredibly hard. There was no dataset in existence that had images of marine plastic in the epipelagic layer of the ocean. So, I decided to create one. I bought a GoPro Hero 9, A wetsuit, and snorkeling equipment and headed out to various locations in California along with 2 Plastic bags and 2 Plastic Bottles. The locations I visited were: Lake Tahoe, Bodega Bay, San Francisco Bay. Here, I shot videos of the plastic in 4K and later broke them into images frame-by-frame. [All plastics used were sanitized and were removed from the environment after I finished capturing the videost] The initial dataset was more than 100,000 images which I then painstakingly went through one by one and chose the best images and annotated them using supervise.ly. The final dataset, along with images scraped from the internet, was a whopping 4000 images. I tried my best to replicate real-world scenarios such as occlusion and brightness by burying objects in sand or placing them across from the sun. ( ) Photo by on ; ( ) Photo by on ; ( ) Image by Author Bottom Right Naja Bertolt Jensen Unsplash Top Right Nariman Mesharrafa Unsplash Top Left, Bottom Right Data Formatting Images were resized into 416x416 and converted into the format the Darknet and YOLOv5 PyTorch require. Data Augmentation Since the final dataset only contained 4000 images, I thought the best way to increase the size of the dataset was to augment it. So, I used Flip, Rotate, Brightness to replicate oceanic environments. I also used B&W to make the model not generalize towards color and cutouts to simulate occlusion. Building the Neural Network Building a Neural Network was a straightforward task. I had two goals for model selection: The model had to be somewhat accurate, and the model had to be fast. Fast enough to be used on Buoys and UAVs. I tried many models, such as Faster R-CNN, EfficientDet, SSD, etc., but stuck with two models: YOLOv4-Tiny and YOLOv5-S. Interested in a code run down for YOLOv5? Let me know in the comments below or reach out to me. Things to know/Tuning Hyperparameters: I used an called ADAM to set a decaying learning rate.Used a package called (Weights and Biases) and continuously monitored the loss.I used a as the final layer and only used called trash_plastic.I used paperspace.com and Google Colab pro with to train the model.Used transfer learning from weights trained on and Adaptive Learning Rate W&B softmax a single class NVIDIA v100 GPUs Underwater Scenes Deep Sea Debris — JAMSTEC JEDI Dataset. All the code used for the models including architectures can be found at , YOLOv4 YOLOv5 Interested in our code? Find it here Results After a lot of experimenting with training methods, data augmentations, and fine-tuning hyperparameters, we finally reached a point where the results were good enough to be used in real-world deployments. Best model : Precision: 96%, Mean-Average-Precision: 85%, F1-Score: 0.89, Inference Speed: 2.1 milliseconds/img. YOLOv5-S Photos by , ( ) Photo by on ; All Other Images by Author (Bottom Row) JAMSTEC JEDI (Second Row First Image) Bottom Right Naja Bertolt Jensen Unsplash What’s Next? At the moment, we are in the process of getting our paper published. We are trying to get the model in the hands of other researchers for testing and developing innovative ways to synthesize more data. If you’re interested, want to contribute, or want to chat, You can reach me here: . gautamtata.blog@gmail.com This article was first published here

How Deep Learning Can Help Quantify, Monitor, and Remove Marine Plastic: The DeepPlastic Way

How Not to ‘Overfit’ Your AI Learning by Taking Both fast.ai and deeplearning.ai courses Data science and artificial intelligence might be the hottest topic in tech right now, and rightfully so. There are tremendous breakthroughs both on application level and research fields. This is a blessing, and a curse, at least for students and enthusiasts that want to break into this area. There are too many algorithms to learn, too many coding/engineering skills to hone, and way too many new papers to keep up with even if you felt you’ve mastered the art. The journey is long, the learning curve is steep, the strife is real, yet the potential is so great people still flock into it. The good thing is we also have great educators and instructors working on mitigating the pain and make the process a little less harsh and a bit more fun. We’ll explore two of the greatest among them and share a potentially effective approach to help you swim through the sea of Data Science a bit happier. AI Learning ‘Burn-out’ If you list what one needs to learn to become an ‘OK’ data scientist or machine learning engineer, it could be scarily long: Linear Algebra, Calculus, Statistics, Algorithms, … Math: Python, R, SQL/NoSQL, Hadoop, Spark, Tensorflow/PyTorch, Keras, Numpy, Pandas, OpenCV, Data Visualization… Coding: Linear Regression, Logistic Regression, Support Vector Machine, PCA, Anomaly Detection, Collaborative Filtering, Neural Network, CNN, RNN, K-Means, NLP, Deep Learning, Reinforcement Learning, AutoML, … Algorithms: Command Line, Cloud platform(AWS, GCP, Asure), DevOps, Deployment, NGINX/Apache, Docker… Engineering: The list can easily make the head spin for a person just entering the filed. Yet, it is still just scratching the surface. Some people make and dive right into it. Some lost momentum and felt totally under the water and the exit is nowhere to be seen. What went wrong? an ambitious plan You ‘Overfit’ Yourself Overfitting is a very familiar idea for anyone that knows a bit of Machine Learning. It basically means your algorithm learned ‘too much’ of the data and buried itself into the little details of the data-set and missing the big picture. Come to think of it, sometimes when we learn something, we dive so deep we forgot why we were learning it and how it will fit into the big picture. It’s something I’d like to call ‘overfitting’ your own learning. This happens especially often for people coming from academia background. A math Ph. D. tends to make sure all the theorems are fully understood before proceeding to the next one. This is great for learning math. Have a profound understanding of theories will give you great intuition and confidence. It will enable you to see patterns and issues people without the training cannot see easily, yet Data Science demands more. Theory aside, there is also a practical part to it. A properly applied algorithm coupled with efficient codes, carefully tuned hyper-parameters, and well-designed pipeline will usually achieve decent results, but not algorithm alone. Delve down too deep into theory, and you risk missing the practical side of the learning. It’s equally important to accumulate experiences on how to implement what you learned and handle real-life complexities. How to address this? Entering deeplearning.ai and fast.ai courses. Deeplearning.ai and Fast.ai A lot of courses have been developed to help navigate people through the learning process. Among them, Deeplearning.ai and fast.ai are two unique ones that have their own approaches and can give us some insights into a potentially effective way of learning Data Science. deeplearning.ai is a paid course developed by . , it is known for its well-designed learning curve, calm and smooth teaching style, and challenging while fun assignments. It is well accepted as the Deep Learning course one cannot go wrong with. It starts from the fundamental theories and works its way up on how to put all the pieces together to solve real-life problems. It’s also called a ‘Bottom-up’ approach. deeplearning.ai Andrew Ng Like his other courses fast.ai fast.ai is introduced by and as a free course to teach people with basic coding experience state-of-the-art deep learning techniques. Without much explanation of the underlying theories, with very few lines of code, student of fast.ai is capable of achieving astoundingly great results on its own domain quickly into the lessons. (I built a rate and after finishing lesson 1 of fast.ai course.) It teaches you how to tackle the real-world problem first, then digs deeper and deeper into how and why things work. It’s also called a ‘Top-down’ approach. Jeremy Howard Rachel Thomas Chinese Calligraphy Style Classifier that reaches 96% accuracy deployed it on the cloud Which One is the Best Approach? Both! So‘Bottom-up’ and ‘Top-down’, which one is better? Which one should we take? The answer is ! Both See, these two courses complement each other. Say you start from ’s deeplearning.ai course, you buried yourself into endless formulas and theories, you gained a lot of intuition, but after weeks of learning, you still have nothing to show to your friends and not quite sure when you can apply your newly gained knowledge. Andrew Ng Your study on the machine learning fundamentals is getting diminishing returns. Your brain slows down and you start to feel boring. Now is the perfect time to start taking a lesson or two of the fast.ai course. With the help of the powerful fast.ai library and few lines of code, you’ll be able to build impressive models that solve real-life problems and even beat some state-of-the-art papers and competitions. Kaggle This will give your brain a totally different kind of stimulation and your heart more confidence and passion to delve deeper into why everything works. Once you built a couple of projects and ‘wow’ed your friends, you will be more motivated to learn more about the fundamentals, then you can go back to deeplearning.ai course and keep your study there. These two courses push each other forward, you can just rinse and repeat till you finished both. This forms a perfect learning circle. The best thing about taking both courses this way is once you finished both, you’ll be fully prepared. You have tons of projects built along the way from fast.ai course to showcase to potential employers and you also have the deep knowledge of how everything works or even published one paper or two to show your findings. You are now a well-rounded Data Scientist. How cool is that? Any feedback or constructive criticism are welcomed. You can either find me on Twitter or my blog site . @lymenlee wayofnumbers.com

Jeremy Howard’s fast.ai vs Andrew Ng’s deeplearning.ai - Are They That Different From Each Other?

An In-depth Review of Andrew Ng's deeplearning.ai Speciliazation

Deep has taken the world by storm and the juggernaut has kept rolling since early 2017. Learning As far as the core methodology goes, neural have been around since decades and convolutional neural networks and recurrent neural networks have been around since 15 odd years. networks What has changed suddenly you ask? GPUs and breakthroughs in automated systems like self driving cars. As Andrew Ng himself says, “I think the other reasons the term deep learning has taken off is just branding. These things are just neural networks with more hidden layers, but the phrase deep learning is just a great brand, it’s just so deep.” Developers all around the globe are heavily motivated seeing the innovation DL is driving in each and every sector. Every company is either pitching themselves as providing Artificial Intelligent solutions or a blockchain company. Quite frankly, these 2 things have become the Guitars and DSLRs which college going students use to impress people but very few take it to the next level. So how do we get our skin in the game? I will try to analyse some of the most popular and widely accepted online courses for deep learning as I have almost completed them (I am yet to finish Udacity’s NanoDegree’s RL part, which itself is a discourse on its own) and break them down in terms of the value they add versus the cost it takes out of you — time and money. all We will discuss about 4 courses in totality: Deeplearning.ai Specialisation — Coursera Deep Learning A-Z — Hands On Experimentation — (SuperDataScience) Udemy Applied AI with Deep Learning — IBM ( ) Coursera Deep Learning NanoDegree — Udacity Lets begin with a short intro to all the courses: Deeplearning.ai, Andrew Ng’s boat which leads us towards autonomous salvation is split over 5 courses covering NN, CNN, RNN and dedicated courses to hyperparameter tuning and ml project structuring. The courses contains 3–4 weeks respectively(except course 2 and 3) which contain as many quizzes and programming assignments in python hosted on coursera’s jupyter hub. DeepLearning A-Z on Udemy provided by folks at superdatascience.com is a 23 hour rigorous course which splits the course into 3 parts : Supervised learning(ANN,CNN,RNN), Unsupervised Learning( SOM, Boltzmann Machines, Autoencoders) and some basic ML referesher and data preprocssing techniques and templates. Applied AI with Deep Learning by IBM is focused more towards the IoT/ production level design of deep learning systems with introduction to various frameworks like deeplearning4j, systemml and tools like Apache Spark and IBM Bluemix platform which makes exploring deep learning on real time data. Its a 4 week course which covers a lot in lost less time. Udacity’s Deep Learning Nanodegree covers everything from NN to GANS and previously was marketed as a foundations course but they added Reinforcement Learning and dropped the former name. Contains rich video content and hands on mini segments which are presented in the form of self paced jupyter notebooks. Assignments are reviewed and mentors are also assigned for a personalised touch. So, after a brief introduction to all the nominees for the Best Course on Deep Learning, the Oscar goes too… Hold on, lets take this to the jury room. From a learner’s perspective, I came up with an evaluate-tionary framework of auto M.A.T.I.C.S ( oney ssignments ime ntuition ontent upport) M A T I C S - Registered in the state of Beliefotopia. All hail our saviour Geoff Hinton and his scribe Yann Lecun. Good things come to those who wait and the metric goes in the increasing order of relevance as well, so stick around! You are not the only one with fancy acronyms, Marvel! Lets discuss all these points with the perspective of students(S) and working professionals(P) oney 1) M S: — Coursera is known for the it has in place to help students not only get its content but also get certified for the course for full or partial payments. Udemy’s courses are comparatively very cheap (Less than $20) and the content might be available on the internet if you look . Udacity is probably the only one which provides free alternative to its nanodegree course content but then it charges $300–$500 dollars for its nanodegrees for the personalized code reviews and mentors. financial aid system closely P: — If your company is paying for your courses as part of L&D policy, I’d say do them all! If its your own pocket through which you are looking to pay then I’d suggest having Coursera’s $49 per month full access to their catalogue. Udemy is just a day’s salary for you and Udacity probably worth 2–3 weeks. Here’s a free certificate for your resume courtesy John Oliver Verdict:- If you don’t want but only knowledge then audit coursera courses, skip udacity nanodegree and be a pirate, harry! Else, shell out some bucks for the course creators’s hardwork and be a responsible student. certificates ssignments 2) A Udacity beats everyone in terms of assignment quality and code reviews. Andrew Ng has provided some cool Ipython notebooks but I must say, the assignments are not at all intuitive. Udemy course provides some very hands on intuitive level assignments/explanation where the tutor explains each and every line of the code which does help if your theory is on point. IBM’s course provides weird yet interesting assignments as they try to bring IBM Watson platform and big data aspect into real time production level assignments but are poorly explained/structured with weird UX around IBM platform. Verdict:- If you like someone explaining all the code, Udemy assignments are very good. You want your code to be reviewed/mentorship — Udacity is bang on. For everything else, there is Coursera. ime 3) T S — Students are at an advantage for they can easily pace out the courses over time. Coursera 4 week courses if done properly hardly take a week if you are clear with your math fundamentals. Udacity has much more to offer in terms of mini assignments and longer syllabus which justifies its ask of 8–10 hours per week. IBM course is very swift but it covers very less things. Udemy course is around 23 hours runtime which clearly takes atleast 2 weeks to finish. P — All the courses are also kept keeping professionals in mind. Udacity has some deadline based projects so you kinda have to keep up. But burning the midnight oil everyday is better than chugging through the whole content over the weekend for you will forget it the next week. Verdict:- Coursera is value for time, Udemy is self paced but might be frightening at times. Udacity is not for the faint-hearted if you are not well versed with its prerequisites of math and python ntuition 4) I Andrew Ng covers the underlying math in a much much better way than all of the tutors but at times it proves to be counter intuitive if you like coding and hacking your way through it. Udemy course covers both code logic and tech intuition in a nice and precise way but visually is unappealing.They also provide links to research papers to read which can be used as supplementary material to strengthen your knowledge on a particular topic. Udacity content is very intuitive though they provide some write ups and math below their videos, some math intensive sessions (RNN) surely are snoozeworthy.( Important, yet snoozyy) IBM course gives you an idea about IoT and production level deep learning with its very basic and intuitive videos which are good if you are looking to quickly try and build something without going into the grittiness of deep learning. That’s not what she said… Verdict:- I personally found Udacity content to have cleared a lot of mydoubts, but then Udemy clears your coding intuition and Andrew Ng’s lectures though less intuitive but are necessary if you re looking to understand how everything works. You can choose to go for Udemy if your aim is to prototype rapidly and learn intuitively only. ontent 5) C Andrew Ng’s content is very standard and it feels as if you are sitting in a classroom and your professor is breaking down each and every difficult concept into simpler terms. Udacity’s content is graphically attractive and doesn’t make you sleep( RL and RNN will) coupled with special content by Andrew Trask, Ian Goodfellow and Sebastian Thrun keeps things interesting from time to time. SuperDataScience team has come up with their hit A-Z series in many verticals of AI/ML/DL and though their slides are not a visual treat, their content is precise and inclined more towards the practical aspect. They provide some very cool templates which are heavily reusable if you plan to experiment. IBM content is a bit hasty I would say, the varied pace at which different instructors go and the accent of the main tutor is sometimes very difficult to comprehend without subtitles. Verdict:- Udacity — Yayyyy, Udemy — Yayyy, DeepLearning.ai — Yayy, IBM — Yay upport 6) S I’d say Udacity’s 60% fees might be justified for the support they provide. Slack channels, weekly office hours, live webinars with top scholars, mentors to help you out with code and logical errors and portfolio building skills make taking a mooc a very wholesome experience. Coursera and Udemy have support forums where you can discuss with your fellow classmates or reach out to TAs but then it depends on how much grasping power you have. Better the support, Better the understanding.I personally got frustrated with the IBM course as the assignments are so rigid I almost gave it up at one point just because I was copy pasting wrong json file. Verdict:- Udacity takes the cake and justifies its price in this segment FINAL VERDICT Its one thing to download tutorial videos and other thing to take an actual course. I have had like 30–40 gb worth of tutorials ranging from Game Theory to How To Make Delicious Pastas and I still eat instant pasta packets. The point being if you are commiting yourself to a course and are serious about entering this field or are just wanting to have an overview of the fuss in the market, I have some yoda wisdom to pass on. Chronological Order : Deeplearning.ai, Udemy, IBM, Udacity Machete Order: Udemy, IBM and then watch the world burn The Chosen One: Udacity DLND Original Trilogy — Andrew Ng’s Machine Learning-> Khan Academy Math ->DeepLearning.ai

DeepLearning 101: Coursera Vs Udemy Vs Udacity

Chain Theory: A Proposed User-Friendly and Customizable Cryptographic Model

চেইন তত্ত্ব: একটি প্রস্তাবিত ব্যবহারকারী-বান্ধব এবং কাস্টমাইজযোগ্য ক্রিপ্টোগ্রাফিক মডেল

Kettentheorie: Ein Vorschlag für ein benutzerfreundliches und anpassbares kryptografisches Modell

Teoría de la cadena: una propuesta de modelo criptográfico personalizable y fácil de usar

Théorie des chaînes : une proposition de modèle cryptographique convivial et personnalisable

चेन थ्योरी: एक प्रस्तावित उपयोगकर्ता-अनुकूल और अनुकूलन योग्य क्रिप्टोग्राफ़िक मॉडल

チェーン理論: ユーザーフレンドリーでカスタマイズ可能な暗号モデルの提案

체인 이론: 제안된 사용자 친화적이고 사용자 정의 가능한 암호화 모델

Teoria da Cadeia: Uma Proposta de Modelo Criptográfico Fácil de Usar e Personalizável

Теория цепей: предлагаемая удобная и настраиваемая криптографическая модель

Zincir Teorisi: Önerilen Kullanıcı Dostu ve Özelleştirilebilir Bir Şifreleme Modeli

Lý thuyết chuỗi: Một mô hình mật mã thân thiện với người dùng và có thể tùy chỉnh được đề xuất

链式理论：一种用户友好且可定制的加密模型

Ping Ping Ping That is the sound of my phone alerting me to overnight snowfall at the various Hokkaido ski resorts I monitor. While this sound brings me immense joy in January and February, in March, it brings only FOMO. I decamped from Hokkaido in early March for the past few ski seasons. My recent experience taught me that Mother Nature turns on the heat circa March 1st. I’m a primadonna skier who only likes the driest and deepest pow. However, this season, Gaia changed up big time. There was a brutal warm spell in February that rekt the snow back. The cold weather only returned towards the end of the month. But frigid temperatures came back in March, accompanied by 10 to 30cm dumps of freshie pow pow every night. Hence, the reason why my phone was blowing up. Sitting in various South East Asian hot and humid countries throughout March, I kept foolishly checking the app and ruing my decision to leave the slopes. The April thaw finally occurred in earnest, and with it, the end to my FOMO. As readers know, my ski experiences act as metaphors for my macro and crypto trading book. I previously wrote that the cessation of the US Bank Term Funding Program (BTFP) on March 12th would cause a global market rout. The BTFP was cancelled, but a vicious sell-off in crypto specifically did not occur. Bitcoin broke $70,000 decisively and topped out around $74,000. Solana kept pumping alongside various doggie and kitty meme coins. My timing was wrong, but like the ski season, the unexpected favorable March conditions will not be repeated in April. While I love winter, summer brings joy as well. The onset of northern hemispheric summer brings sporting bliss as I reorient time towards playing tennis, surfing, and kiteboarding. Summer will usher in a renewed gusher of fiat liquidity due to the policies of the US Federal Reserve (Fed) and Treasury. I will briefly outline my mental map on how and why April will experience extreme weakness in risky asset markets. For those intrepid enough to short crypto, the macro setup is favorable. While I will not outright short the market, I have closed in profit several shitcoin and memecoin trading positions. From now until May 1st, I will be in a no-trade zone. I hope to return in May with dry powder ready to deploy to position myself for the bull market to begin in earnest. Tricksters The Bank Term Funding Program (BTFP) ended a few weeks ago, but no US, non-Too-Big-to-Fail (TBTF) banks have subsequently faced any real stress. That is because high priests of fugazi finance have a bag of tricks they will use to surreptitiously print money in order to bail out the financial system. I will peek behind the curtain and explain how they are expanding the USD fiat money supply, which will underpin the general rise of crypto and stonks until the end of the year. While the end result is always money printing, the journey is not without periods where liquidity growth slows, providing a negative catalyst for risk markets. By going through the bag of tricks and estimating when the rabbit will be pulled from the hat, we can guestimate when there will be periods where the free market is allowed to operate. The Discount Window The Fed and most other central banks operate a facility known as the discount window. Banks and other covered financial institutions who are in need of funds can pledge eligible securities to the Fed in exchange for cash. By and large, the discount window, for now, only accepts US Treasuries (UST) and mortgage-backed securities (MBS). Assume that a bank is fucked because a bunch of Pierce & Pierce boomer muppets runs it. The bank holds a UST that was worth $100 when purchased but is currently worth $80. The bank is in need of cash to satisfy deposit outflows. Rather than declaring bankruptcy, the dogshit insolvent bank can tap the discount window. The bank swaps the $80 UST for $80 of dolla dolla bills because under the current rules the bank receives the market value of pledged securities. To deprecate the BTFP and remove the associated negative stigma without increasing the risk of bank failures, the Fed and US Treasury now encourage troubled banks to tap the discount window instead. However, under the current collateral terms, the discount window is not as attractive as the recently expired BTFP. Let’s go back to our example above to understand why. Remember that the UST’s value declined from $100 to $80, which means the bank has an unrealized loss of $20. Initially the $100 of UST was funded with $100 of deposits. But now the UST is worth $80; therefore, the bank would be short $20 if all the depositors fled. Under the BTFP rules, the bank receives par for underwater USTs. That means a UST worth $80, when delivered to the Fed, is swapped for $100 of cash. This restores the solvency of the bank. But the discount window only gives $80 for a UST worth $80. The $20 loss is still there, and the bank remains insolvent. Given that the Fed can unilaterally change the collateral rules to equalize the treatment of assets by the BTFP and discount window, by greenlighting the use of the discount window by the insolvent banking system, the Fed continued the stealth banking bailout. Therefore, the Fed has essentially solved the BTFP problem; the entire UST and MBS balance sheet of the insolvent US banking system (I estimate at $4 trillion) will be supported when needed by money printed to finance loans through the discount window. This is why I believe the markets haven’t forced any non-TBTF banks into bankruptcy post-March 12th, when the BTFP ended. Bank Capital Requirements Banks are often called upon to finance governments who offer bonds at yields less than nominal GDP. But why would private for-profit entities buy something that features a negative real yield? They do it because the banking regulators allow banks to purchase government bonds with little to no money down. When the banks, who hold an insufficient capital cushion against their government bond portfolio, inevitably blow up because inflation appears and bond prices fall as yields rise, the Fed allows them to use the discount window in the manner described above. As a result, banks would rather buy and hold government bonds than lend to businesses and individuals in need of capital. When you or I buy anything using borrowed money, we must pledge collateral or equity against potential losses. That is prudent risk management. But the rules are different if you are a vampire squid zombie bank. After the 2008 Global Financial Crisis (GFC), the world’s banking regulators attempted to force banks globally to hold more capital, thus creating a more robust and resilient global banking system. The system of rules that codified these changes are called Basel III. The problem with Basel III is that government bonds are not treated as riskless. Banks must put a small amount of capital behind their gargantuan portfolio of sovereign bonds. These capital requirements have proven problematic during times of stress. During the COVID March 2020 market crash, the Fed decreed that banks could hold USTs with no collateral backing. This allowed banks to step in and warehouse trillions of dollars worth of USTs in a riskless fashion … at least in terms of their accounting treatment. When the crisis abated, the supplemental leverage ratio (SLR) exemption for USTs was reinstated. Predictably, as UST prices fell due to inflation, banks went bust due to an insufficient capital cushion. The Fed rode to the rescue with the BTFP and now the discount window, but that only covers losses from the last crisis. How can banks step up and slurp down more bonds at the current unattractively high prices? The US banking system loudly proclaimed in November 2023 that due to Basel III forcing them to hold more capital against their government bond portfolios, Bad Gurl Yellen could not stuff them with more bonds. Therefore, something had to give because the US government has no other natural buyers of its debt at these negative real yields. Below is the polite way the banks phrased their precarious situation. Demand for US Treasuries may have softened among several traditional buyers. Bank security portfolio assets have been declining since last year, with bank holdings of Treasuries down $154 billion compared to one year ago. Source: Report to the Secretary of the Treasury from the Treasury Borrowing Advisory Committee Again, the Fed – led by Powell – saved the day. Out of nowhere, at a recent , Powell proclaimed that banks would not be subject to higher capital requirements. Remember that many politicians called for banks to hold more capital so that the regional banking crisis of 2023 would not repeat itself. Obviously, the banks lobbied hard to nullify these higher capital requirements. They had a good argument –  if you, Bad Gurl Yellen, want us to buy dogshit government bonds, then we can only do it profitably with infinite leverage. Banks around the world run every flavour of government; the US is no different. US Senate Banking hearing The icing on the cake was a recent letter from the International Swaps Dealer Association (ISDA) advocating for an exemption of USTs from the SLR I spoke about earlier. In essence, banks can only hold USTs in the trillions of dollars needed to finance the US government deficit on a go-forward basis if they are required to put no money down. I expect as the US Treasury ramps up debt issuance, the ISDA proposal will be accepted. This excellent chart from clearly shows how wasteful the US Government is, as evidenced by the record-high deficit. The two recent periods where deficit spending was higher were due to the 2008 GFC and the boomer-led COVID lockdowns. The US economy is growing, but the government is spending money like it's an economic depression. Bianco Research Taken together, the relaxation of capital requirements and a likely future exemption of USTs from the SLR are a covert way of money printing. The Fed doesn’t print the money, rather the banking system creates the credit money out of thin air and purchases the bonds which then appear on their balance sheet. As always, the goal is to ensure that government bond yields do not rise to a level above the nominal GDP growth rate. As long as real rates remain negative, stonks, crypto, gold, etc., will continue advancing in fiat currency terms. Bad Gurl Yellen My essay “ ” spoke in depth about how the US Treasury, led by Bad Gurl Yellen, increased the issuance of short-term Treasury bills (T-bills) in an effort to drain the trillions of dollars locked in the Fed’s Reverse Repo Program (RRP). As expected, the RRP decline coincided with a rip higher in stonks, bonds, and crypto. But now that the RRP is down to $400 billion, the market is wondering what the next source of fiat liquidity will be that bolsters asset prices. Don’t you worry, Yellen ain’t done yet, shout ‘bout to booty drop. Bad Gurl RRP Balance (white) vs. Bitcoin (yellow) The flow of fiat funds I will discuss focuses on US tax payments, the Fed’s Quantitative Tightening (QT) program, and the Treasury General Account (TGA). The timeline in question is from April 15th (the day tax payments are due for the 2023 tax year) to May 1st. Let me provide a quick guide on their positive or negative impact on liquidity to help you understand the implications of these three things. remove liquidity from the system. That is because payers must remove cash from the financial system by selling securities for example, in order to pay their taxes. Analysts expect tax payments for the 2023 tax year to be on the high side because of the large amounts of interest income received and a solid performance in the stock market. Tax Payments removes liquidity from the system. As of March 2022, the Fed allows roughly $95 billion worth of UST and MBS to mature without reinvesting the proceeds. This causes the Fed’s balance sheet to fall, which we all know reduces dollar liquidity. However, we are not concerned about the absolute level of the Fed’s balance sheet but the pace at which it declines. Analysts, like Joe Kalish of Ned Davis Research, expect the Fed to reduce the pace of QT by $30 billion per month at its May 1st meeting. The reduction in the pace of QT is positive for dollar liquidity as the Fed’s balance sheet rate of decline slows. QT When the balance rises, it removes liquidity from the system, but when it falls, it adds liquidity to the system. When the Treasury receives tax payments, the TGA balance rises. I expect the TGA balance to swell well above the current ~$750 billion level as tax payments are processed on April 15th. This is dollar liquidity negative. Don’t forget it's an election year. Yellen’s job is to get her boss, US President , reelected. That means she must do all she can to juice the stock market to make voters feel rich and ascribe this great outcome to the slow “genius” of Bidenomics. When the RRP balances finally go to zero, Yellen will spend down the TGA, releasing, most likely, an additional $1 trillion of liquidity into the system, which will pump markets. TGA Jeo Boden The precarious period for risky assets is April 15th to May 1st. This is when tax payments remove liquidity from the system, QT rumbles on at the current elevated pace, and Yellen has yet to start running down the TGA. After May 1st, the pace of QT declines, and Yellen gets busy cashing checks to jack up asset prices. If you are a trader looking for an opportune time to put on a cheeky short position, the month of April is the perfect time to do so. After May 1st, it’s back to regular programming … asset inflation sponsored by Fed and US Treasury financial shenanigans. Bitcoin Halving The Bitcoin block reward is forecast to halve on April 20th. This is seen as a bullish catalyst for crypto markets. I agree that it will pump prices in the medium term; however, the price action directly before and after could be negative. The narrative of the halving being positive for crypto prices is well entrenched. When most market participants agree on a certain outcome, the opposite usually occurs. That is why I believe Bitcoin and crypto prices in general will slump around the halving. Given that the halving occurs at a time when dollar liquidity is tighter than usual, it will add propellant to a raging firesale of crypto assets. The timing of the halving adds further weight to my decision to abstain from trading until May. To date I took full profit on these positions, MEW, SOL, and NMT. The proceeds were placed into and staked to earn that phat yield. Before Ethena, I would have held USDT or USDC and earned nothing while Tether and Circle captured the entire T-bill yield. Ethena’s USDe Could the market defy my bearish inclinations and continue higher? Fuck yeah. I’m perennially long as fuck crypto, so I welcome being wrong. Do I really want to babysit my most speculative shitcoin positions while I’m two-stepping at Token2049 Dubai? Hell no. Therefore, I dump. There is no need to be upset. If the dollar liquidity scenarios I discussed above come true, I will have that much more confidence to ape into all manner of dogshit. If I miss a few percentage points of gains but definitely avoid losses for my portfolio and lifestyle, that is an acceptable outcome. With that, I bid you adieu. Please remember to put your dancing shoes on, and I will see you in Dubai for a celebration of the crypto bull market.

Heatwave

তাপপ্রবাহ

Hitzewelle

Ola de calor

Vague De Chaleur

हीटवेव

熱波

폭염

Onda de calor

Волна тепла

Sıcak hava dalgası

Đợt nắng nóng

热浪

This paper is available on arxiv under CC 4.0 license. Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT 6 Conclusion In this work, we addressed the challenge of training multimodal meme sentiment classifiers on a limited number of labelled memes by incorporating unimodal sentiment analysis data. We did so by proposing the first instance of STILT that applies unimodal intermediate tasks to a multimodal target task. Specifically, we tested image-only and text-only sentiment classification as intermediate tasks in training a meme sentiment classifier. We showed that this approach worked – unimodal text improved meme classification performance to a statistically significant degree. This novel approach allowed us to train a meme classifier that outperforms meme-only finetuning with only 60% as many labelled meme samples. As possible explanations for our observations, we discuss apparent similarities and differences in the roles of image and text modalities between unimodal and multimodal sentiment analysis tasks. Acknowledgments This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224; and the provision of computational facilities and support from the Irish Centre for High-End Computing (ICHEC). References Christian Bauckhage. 2011. Insights into internet memes. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):42–49. Efrat Blaier, Itzik Malkiel, and Lior Wolf. 2021. Caption enriched samples for improving hateful memes detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9350–9358, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Ana-Maria Bucur, Adrian Cosma, and Ioana Iordache. 2022. BLUE at memotion 2.0 2022: You have my image, my text and my transformer. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2924–2936, Minneapolis, Minnesota. Association for Computational Linguistics. CrowdFlower. 2016. Image sentiment polarity - dataset. Available at https://data.world/ crowdflower/image-sentiment-polarity. Accessed: 2023-01-15. Baishan Duan and Yuesheng Zhu. 2022. Browallia at memotion 2.0 2022 : Multimodal memotion analysis with modified ogb strategies (short paper). In DeFactify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Noam Gal, Limor Shifman, and Zohar Kampf. 2016. “it gets better”: Internet memes and the construction of collective identity. New Media & Society, 18(8):1698–1714. Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018. Hybrid Attention based Multimodal Network for Spoken Language Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2379–2390, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Muzhaffar Hazman, Susan McKeever, and Josephine Griffith. 2023. 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The European Journal of Humour Research, 4(4):26–49. Gwang Gook Lee and Mingwei Shen. 2022. Amazon pars at memotion 2.0 2022: Multi-modal multi-task learning for memotion 2.0 challenge. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Quinn McNemar. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2):153–157. Shreyash Mishra, S Suryavardan, Parth Patwa, Megha Chakraborty, Anku Rani, Aishwarya Reganti, Aman Chadha, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, and Srijan Kumar. 2023. Memotion 3: Dataset on sentiment and emotion analysis of codemixed hindi-english memes. arXiv preprint, arXiv:2303.09892. Niklas Muennighoff. 2020. Vilio: state-of-the-art visiolinguistic models applied to hateful memes. arXiv preprint arXiv:2012.07788. Thanh Van Nguyen, Nhat Truong Pham, Ngoc Duy Nguyen, Hai Nguyen, Long H. 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In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Jason Phang, Thibault Fevry, and Samuel R. Bowman. ´ 2019. Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks. arXiv preprint, arXiv:1811.01088. Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019. WiC: the word-in-context dataset for evaluating context-sensitive meaning representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1267–1273, Minneapolis, Minnesota. Association for Computational Linguistics. Clifton Poth, Jonas Pfeiffer, Andreas Ruckl ¨ e, and Iryna ´ Gurevych. 2021. What to pre-train on? Efficient intermediate task selection. 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MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4439–4455, Punta Cana, Dominican Republic. Association for Computational Linguistics. Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020. Intermediate-task transfer learning with pretrained language models: When and why does it work? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5231–5247, Online. Association for Computational Linguistics. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, et al. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR. Sathyanarayanan Ramamoorthy, Nethra Gunti, Shreyash Mishra1, S Suryavardan, Aishwarya Reganti, Parth Patwa, Amitava Das, Tanmoy Chakraborty, Amit Sheth, Asif Ekbal, and Chaitanya Ahuja. 2022. Memotion 2: Dataset on sentiment and emotion analysis of memes. In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Elad Segev, Asaf Nissenbaum, Nathan Stolero, and Limor Shifman. 2015. Families and Networks of Internet Memes: The Relationship Between Cohesiveness, Uniqueness, and Quiddity Concreteness. Journal of Computer-Mediated Communication, 20(4):417–433. Lanyu Shang, Yang Zhang, Yuheng Zha, Yingxi Chen, Christina Youn, and Dong Wang. 2021. AOMD: An Analogy-Aware Approach to Offensive Meme Detection on Social Media. Inf. Process. Manage., 58(5). Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas PYKL, Amitava Das, Tanmoy Chakraborty, et al. 2020. 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Innovative Strategies in Meme Sentiment Analysis: The Impact of Unimodal Training

Computer-aided design (CAD) software revolutionized design, replacing drafting tables with sophisticated technology that streamlines design processes for engineers and designers. But the story doesn't end there. Can new advancements in AI and blockchain technology push the boundaries of CAD even further? In this interview, CEO and Co-Founder of CADAICO explored the intersection between CAD and such frontline technologies as AI and blockchain. Pedram Shahid, Mr. Shahid, can you briefly tell us about yourself and your route to web3? Absolutely, I'm a 43-year-old mechanical engineer with a rich 16-year experience in engineering firms across Germany. In my recent role as the Head of Industrial Engineering at Festo SE's sizable production facility, I observed firsthand the considerable amount of time my design engineers dedicated to manual tasks in CAD software. It was three years ago that this observation sparked the concept of integrating AI within CAD systems to significantly enhance engineering productivity. As I couldn't find a solution on the market, I departed from my position at Festo SE last year to establish CADAICO. In the initial months, I aligned with my first collaborator, the blockchain enterprise ONINO, gaining substantial insights into blockchain technology and its potential applications. This period was crucial for crystallizing the comprehensive vision for the CADAI platform, inclusive of its proprietary blockchain. What is CAD, and what makes it a sophisticated tool? Computer-aided design (CAD) is a pivotal tool across numerous industries, facilitating the creation of 3D models for objects. Widely utilized in fields such as mechanical engineering, architecture, and automotive manufacturing, CAD software enables precision and innovation in design. Each CAD software developer offers unique features specific to its product, leading to a lack of compatibility across different CAD systems. This disparity necessitates that designers invest considerable time and effort in learning and mastering these complex software programs, each distinct in its operation and capabilities. What advantages does using CAD software provide over traditional practices? Before the advent of CAD, engineers and designers relied heavily on manual drafting to sketch everything by hand, a process that was both time-intensive and prone to errors. The emergence of CAD revolutionized the design process, significantly enhancing productivity by automating and streamlining the creation of digital 3D models. Today, CAD has become the foundational tool across the engineering spectrum, indispensable in sectors including automotive, mechanical engineering, architecture, medical engineering, and electronics, setting a new standard for precision and efficiency in design. What are the challenges that designers often face with CAD software? Despite advancements in CAD technology, the design process remains predominantly manual and time-consuming. Remarkably, only about 25% of an engineer's design work leverages their creativity, with the bulk of their tasks being repetitive and routine. Mechanical engineering companies typically possess vast repositories of existing CAD data that could be repurposed for new designs. However, the challenge lies in efficiently locating the appropriate files within extensive archives containing thousands of entries. This situation underscores a significant opportunity for innovation in streamlining access to and utilization of existing CAD resources. How does CAD intersect with AI and blockchain? Leveraging the extensive CAD data for AI training, in conjunction with the high standards of security and transparency provided by blockchain technology, offers an ideal solution to the productivity challenges in the CAD design process. This combination not only promises to streamline workflow but also ensures data integrity and enhances collaborative possibilities, marking a significant leap forward in engineering design efficiency. This innovative approach is about augmenting the capabilities of engineers, not replacing them. By automating routine tasks, AI enables engineers to dedicate more time to creative and complex problem-solving tasks. Thus, this fusion of AI and blockchain doesn't diminish the role of engineers; rather, it elevates their work, freeing up valuable time for innovation and design excellence. Can you share the story behind the founding of CADAICO? Do you know what the most important thing is when you start a new business from scratch? Everything at the same time. The cornerstone of such an endeavor encompasses a clear vision, a dedicated team, sufficient funding, and strategic partnerships. My journey with CADAICO commenced solely with a vision. Progressively, I embarked on forging partnerships, securing funding, and assembling a team, one step at a time. My highlight was meeting Daniel by chance at a startup event in Karlsruhe. I overheard him talking to someone about his background in machine learning and approached him. We talked for hours and completely forgot about the event. He is now co-founder and CTO of CADAICO. From my findings, you recently received a backing from the German state of Baden Wurttemberg. How does this translate to progress? For me personally, it's not just about the financial support. What really matters is the nod we got from the tough judges. Being chosen as one of the few startups is a big deal and tells me we're doing the right thing. Plus, this kind of support is going to help us get noticed more and find good partners to work with. CADAICO claims to be the first to combine AI and blockchain for a collaborative CAD development platform. What does this entail? I have been looking around the market for the past three years to see who our competitors might be. I found a few startups trying to use AI to make CAD better in ways that are somewhat similar to ours. However, none of them are working on creating an AI platform that lets developers build many AI-driven apps and plugins specifically for CAD. Also, none of them are using blockchain to make sure these platform features are secure and well-managed. Right now, CADAICO stands out as the only company that's merging CAD with the latest in AI technology and blockchain, all on a platform made for developers. On the other hand, I genuinely hope to see more activity in this field. Collaboratively, we could significantly extend the limits of what's achievable, pushing the boundaries of innovation and efficiency in CAD development and application even further. Any Parting Words? Absolutely, I'm excited to share our vision with you. CADAICO is set on ushering in the next leap in the design process, a significant evolution since the inception of CAD. Looking ahead, we envision a future where engineers won't be tied down to weeks of meticulous drawing in CAD. Instead, they'll be interacting with AI assistants, swiftly bringing their 3D designs to life in mere minutes. Furthermore, I want to extend my deepest gratitude to our supportive community, which has been our backbone from the start. A special thank you goes out for the overwhelming support we received during our public sale.

AI And Blockchain In Computer Aided Design: Exclusive Interview With CADAICO CEO Pedram Shahid

Life happily carries on and we don’t even have to look at our own darkness. Perhaps the real problem with modern life is the benign micro-systems that foster the covert conspiracy- we agree to blame the world for things that are, in fact, within us all. Implicitly we agree to live in a superficial dystopia of masks and make-up, where the wounds of psyche and identity stay hidden below the surface. The very comforts that our system affords us, also engage us in a conspiracy of denial, where we avoid the discomfiting process of taking a deep look within, and consequently, we allow the demons free space in our psyche. That’s why the unacknowledged, negative parts of us don’t simply lay dormant; they affect our thinking and our behavior, both conscious and unconscious, in ways that can be insidious and even destructive. Recognizing our shadows is a positive expression of our autonomy, an alternative to the easy way of least resistance, it is a refusal to ignore parts of ourselves, and a positive act of contact with that essential but compromised figure we all carry around inside our heads in the form of ‘I’. Recognition, then, is neither masochism nor resigned submission to this feature of our psychology. It is a positive move towards genuine self-awareness, which – by integrating in this way the previously separate shadow, by bringing that shadow into the full light of consciousness – is then powerless to continue the task it set itself, i.e, to deceive us into projecting onto other people the pain or injustice it embodies. No longer can we be unsuspecting hosts for the behaviors that are put to use by the shadow with such vicious selectivity. It takes courage and defiance to engage in this process of recognition – both externally and inwardly – to challenge how confinement insulates us from the truth of our ambivalence, how confinement requires people to live in a suspended way, aware of only part of themselves. If we allow ourselves to acknowledge the full range of our humanity, we open ourselves to a greater capacity for change. There is power in the acknowledgment of our demons, which changes their ability to function in the dark; it will not make us immune to them, but it should let us manage them better. Knowing that we have a dark side expands our compassion towards ourselves and others. We all are fighting a battle within, and this makes us vulnerable to each other. But awareness can transform vulnerability into a source of solidarity. In fact, this recognition opens the door for a more equitable and empathetic society. In owning our inner conflict, we reduce the risk of projecting it onto others. This creates space for a culture of accountability and a compassionate conversation with our shadow as an integral part of the work of being in this world. It transforms the myth of ‘what was done’ to ‘what I’ve done’, and perhaps becomes a lifelong conversation with our courageous and compassionate inner voice: ‘This is what was done to me. Now, what can be done with it?’ To opt out of a system that not only permits but welcomes this denial of our demons is an act of profound individual and societal autonomy. It serves as an affirmation that we’re no longer willing to be accomplices to lying to ourselves and others; and, perhaps more vitally, that we decline to be complicit in the injustice of the status quo. Then, armed with this existential awareness of who we really are, we can begin to chart a course for a different kind of beings. We can let our actions become less reactive and more reflective, our behaviors less governed by dark assumptions, and conscious biases – and more the results of a holistic sense of self. In this emerging reality, autonomy and identity are no longer ideals we strive to achieve but realities we actively embody. They are a commitment to being human with grace, intentionality, and an intense dedication to becoming more fully human as individuals and a larger society. If we and create systems that nurture our deepest values, we unshackle ourselves from the painful inescapability of separate selves, and liberate ourselves to co-create a world in which human beings live their differences together in safety and dignity. pay attention to our demons,

Can Humanity's Systems Serve as Material Enablers for Evasion?

Sitting down to digest the videos in Andrew Ng’s Generative AI course it is hard to ignore the prevalent use of the term data and how important large data sets are when building these large language models such as and Bard. Andrew Ng is Founder of Deeplearning.ai, he was a co-founder and head of Google Brain and Chief Scientist at Baidu. It is fair to say he knows a thing or two about the power of data. Chat GPT So, where do we get this data? Who controls the flow of this data? And how do companies find the inputs that will provide the greatest outputs when dealing with real-world use cases? These are questions that many practitioners in the world of blockchain today are asking and answering by providing access to streams of reliable, transparent data from verified sources. A seminal shift is underway when it comes to data-driven decision-making as AI and Blockchain technology emerge to put the power in the hands of the public. Breaking the barriers to entry and the age-old data monopoly held by larger corporations, blockchain technology, and decentralized data are democratizing information and transforming who and how companies of all sizes can use this information. The Historical Data Monopoly The phrase "knowledge is power" echoes through all discussions regarding access to data. As with the printing press empowering capitalist press owners, control over information governs us and shapes our destiny. However, the lessons from history underscore the need to ensure that power remains decentralized, preventing the concentration of control among a select few. For years, the manipulation and analysis of vast datasets were privileges held by the corporate giants with deep pockets. Smaller businesses, startups, and entrepreneurs found themselves on the sidelines, unable to compete and essentially working with data that had already been manipulated. This data monopoly created barriers to entry, stifling innovation and limiting opportunities for those without substantial resources. The rise of blockchain technology disrupts this narrative, offering decentralized access to data that redefines the point at which start-ups can get their hands on valuable data sets. No longer confined to the elite, data is becoming a democratized resource accessible to businesses of all sizes. This decentralization is not just about breaking down data silos; it's about dismantling the barriers that have kept smaller players from harnessing the full potential of information. Using decentralized data means that everyone has access but also that no single party is in control of the process. Nick Campion, Head of Marketing for Flare Network tells us that by avoiding single points of failure this type of data can help to prevent censorship, fraud, and downtime, which increases the security and integrity of the information being supplied. Campion outlines Flare’s ambitious goals, “Flare’s optimization is through the network’s architecture and incentivization structure, with the validators securing Flare’s proof-of-stake consensus mechanism also responsible for supporting the provision of decentralized data as data providers for the network’s native oracles. The goal is to grow the industry’s total addressable market, increasing what’s possible with blockchain by giving developers decentralized access to the broadest range of data and data proofs, provided at scale, low latency, and minimal cost.” Empowering Start-ups and SMEs One of the most significant impacts of decentralized data access is seen in how it empowers small and medium-sized enterprises (SMEs). Traditionally overshadowed by larger corporations, SMEs can now and decentralized data to make informed decisions. leverage blockchain Whether it's understanding market trends, consumer behavior, or optimizing operations, smaller businesses now have a seat at the data-driven table using Blockchain and Artificial Intelligence. Mat Yarger, Founder of Demia, a blockchain project securing data for carbon markets, notes that “Subject matter experts can leverage decentralized data for informed decisions by accessing high-integrity, real-time data. This empowers them to make data-driven choices, enhance research, and develop insights without compromising security or data quality.” Transparency, a cornerstone of responsible business practices, is no longer a luxury reserved for the big players. Blockchain's inherent transparency features allow businesses to showcase their ethical practices without the need for extensive resources. This transparency becomes a powerful tool for consumer trust, irrespective of the size of the business. Entrepreneurs and startups can now focus on creating value and innovative solutions rather than being bogged down by data access constraints. The democratization of information sparks a new era of creativity and problem-solving, where ideas, not resources, become the currency of progress. Addressing the Challenges While the shift towards decentralized data access brings about profound positive changes, it also poses challenges. Privacy concerns, security issues, and the need for robust regulatory frameworks are crucial considerations. The democratization of data also comes with a responsibility to actively participate in shaping how this data is used. Many of today’s oracle systems suffer from meaningful drawbacks: they are often hard to decentralize, expensive, slow, and difficult to scale. This is particularly relevant for emerging use cases such as machine learning and real-world assets, which have intensive data demands. Builders will realize that current systems are not able to handle this much data in a secure and decentralized fashion. These challenges present opportunities for collaborative efforts to establish responsible guidelines that ensure the ethical use of decentralized data. “Emerging applications require a highly scalable, low cost, decentralized, and low latency solution for data. New projects will be drawn to platforms with well-considered, native, decentralized data acquisition architecture - like Flare,” notes Campion. “Data silos, security concerns, inconsistent data quality, and complex access procedures. These hurdles hinder seamless data flow, slowing down innovation and decision-making processes. Decentralized data solutions, like Demia, aim to address these challenges, enabling frictionless data access and trust,” says Yarger. A Fairer Society Decentralized access to data is a catalyst for societal change. Dismantling the data monopolies means that more individuals and organizations can contribute to an equitable and responsible future. How practitioners empower small businesses to leverage this data is critical as the role of data in shaping AI models like Chat GPT and Bard becomes increasingly evident. Just last week, from the results of its released Gemini AI. Apologizing for the incorrect representation of the US Founding Fathers is one thing, but fixing a politically sensitive model that has been trained on enormous amounts of inaccurate data is a different beast to tackle. Google received an extreme level of negative backlash Furthermore, the crucial questions of data origin, control, and utilization resonate across diverse industries. The quest for access to transparent data sources becomes paramount as official bodies continue to explore the potential of these emerging technologies and foster more data-driven decision-making.

From Monopoly to Democracy: The Rise of Decentralized Data Empowerment

The role of Quality Assurance (QA) Engineers and their needs are fast growing in significance due to fast-changing scenarios of software development. Historical trends associated with the software testing process have followed some of the traditional approaches like checklists, test cases, and test scenarios to identify the quality related to the software product. Tradition is indeed the most important, yet since technology is changing, testing methodologies should be changed. In this article, we will consider the traditional approach and offer an alternative: "Test Outlines." Test Outlines is an innovative approach introduced by Vital Shauchuk – QA Engineer and expert in software testing. Firstly, let us dive into the traditional methods of test organizing and management. These traditional methodologies are scaffolding, supporting systematic and structured software application examination. The above time-tested methodologies structure the framework in which QA engineers can negotiate the labyrinth of testing in such a way that critical functionalities are tested thoroughly. These checklists carry the inventory of criteria, test cases carrying detailed step-by-step instructions for verification, and test scenarios carrying end-to-end evaluations—a properly defined strategy of testing. These are important to know because they lay the foundation of what the new innovative approaches promise to carry the efficacy of software testing to a higher plane in our ever-changeable technology ecosystem. Checklists are basic for use in software testing as they give a way in which to ensure that crucial steps are followed chronologically without skipping any. Checklists are easy to develop and simple in structure but lack a few details; they lack a structure to give a sequence of items, or tasks to be checked, or they do not state how the task is to be done or what should be the outcome from doing it. Checklists Checklists are very useful when one would like to push to speed on the quick and informal type of testing process, but they do not work well enough in order to reach a level of completeness and deepness associated with systematic testing. Such lists of items to be checked or tasks to be accomplished are a most useful tool, mostly when a quick assessment of elementary functionalities should be made. Nevertheless, these are by nature short and quite simple, which makes them cover only a narrow scope of many complicated scenarios and exceptions that complex software applications throw up at them. While relying on the checklist, one risks missing out in a dynamic and ever-evolving development environment, since they are not flexible enough to cater to the changed requirements or change that comes about from unforeseen challenges. Comprehensive and system testing requires one much broader disposition, with the inclusion of many different testing methods working together to present a complete investigation of the functionality, reliability, and overall quality of the software being tested. Although checklists take an important place in the testing toolbox, their role should be complemented with the help of more powerful tools, like test cases and test scenarios, which would be of great assistance to increase the efficiency of the testing process and bring up more powerful software. are much more specific and provide a structure regarding how every feature or functionality is to be evaluated individually. The most common structure is composed of the following: Test Case ID, Test Case Title, Preconditions, Steps, Expected Results, Actual Results, Pass/Fail, Test Environment, and Comments. Test cases Being so specific and detailed, they have an associated trade-off: they need rigor and repetition in maintaining and creating them. Moreover, writing these step-by-step instructions requires an understanding at a very granular level with respect to the subtleties of the software, for which QA engineers have to invest a lot of time. And this challenge becomes all the more heavy with the enhancements of the software and when new features, functionalities, and changes are brought into its existing code. This dynamism is dangerous because test cases, by nature, are some of the easiest to be rendered out-of-date and irrelevant by further development efforts. The result is organizations stuck with potentially tens of thousands of outmoded test cases. But if they ensure that the test cases meet certain requirements and specifications precisely, their efficiency is directly proportional to how much aligning is done with the changing dynamics of the software and how much the effort of maintenance is proactive. Key to the utility of test cases within the scope of software testing, the advantage of the specificity of software test cases must be balanced against the challenge of tedium and potential obsolescence. From this, it is possible to infer that have a narrative structure that reflects comprehensive user action with software. A test scenario starts from the high-level, brief for some user story, or use case, giving background context in which it requires this software functionality to be evaluated. test scenarios Precondition combines seamlessly with the conditions that are supposed to be set beforehand and are necessary to be provided or satisfied before the development of the scenario; that is, the precondition sets the starting point just like a foundation to ensure a consistent starting point of user interaction simulation. It can be seen from the narrative that the details of using test data in a scenario would involve everything: input values, configurations, and variables driving the response that the software is supposed to provide during execution. Integration of relevant test data will, therefore, make the scenario more realistic and deliver a valid model of human user behavior. But the disadvantage can come from the fact that the framework of the test scenario is narrative. Their very high-level nature makes them very convenient for capturing realistic user interactions. This same characteristic, however, can bring about vagueness and ambiguity. With this inherent flexibility, every detail of this has to be documented and communicated within the team. Guidelines then need to be developed that address possible variations in interpretation. To avoid the disadvantages of traditional approaches to software testing, suggests an innovative way: " " Test outlines are a kind of test documentation that can inherit the positive sides of checklists, test cases, and test scenarios but is free from their drawbacks. Vital Shauchuk Test outlines. The idea of Test Outlines is a re-imagination of the traditional approach present in test cases, and simply—a new one at that, introducing a narrative similar to that found in the cohesiveness and context of test scenarios. This combination of the methodologies is laying a base for the testing approach, which is visionary over its predecessors. The narrative structure of Test Outlines goes beyond the boundaries of all steps of a test case and instead draws these steps into a convincing storyline of a user journey through the software. This sets a narrative lens, not only for simplified, overall testing documentation but also for a holistic way that end-users will interact with the software in real settings. This depth allows for much more scope in understanding the testing process, moving it from a simple step checklist to a dynamic heuristic around the user experience. On the other hand, a narrative approach will inspire movement from isolated functionality with an interrelationship of the features. This builds up capability in identifying critical dependencies, potential integration issues, and system behavior in general during the user's interface. The narrative is a tool that eases cognitive functions in the mental modeling of user experiences and their likely tribulations on the part of the testing teams. The Test Outline essentially indulges in a paradigm shift: compactifying conventional stepwise test cases to elevate the testing process through narrative-driven exploration. It is a new approach that overcomes the limitations of traditional methodologies and, in fact, has a better understanding of the fine dynamics set up between software and its users for allowing a more effective and exhaustive testing exercise.

Test Outlines: A Novel Approach to Software Testing

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 3. ANALYSIS AND VALIDATION 3.1. Stellar Host Characterization 3.1.1. Semi-empirical Stellar Parameters We constructed the spectral energy distribution (SED) of EPIC 249631677 using photometric magnitudes from Gaia (GBP and GRP ; Gaia Collaboration et al. 2018) and the AllWISE source catalog (J, H, Ks, W1, W2, and W3; Cutri et al. 2013). The corresponding fluxes for these magnitudes are tabulated on VizieR (Ochsenbein et al. 2000) and shown in Figure 5 and Table 1. The parallax of EPIC 249631677 is π = 17.61 ± 0.09 mas, which yields a distance of 56.8 ± 0.3 pc (Gaia Collaboration et al. 2018; Stassun & Torres 2018). We then derived the stellar luminosity L∗ by integrating over the SED, which yielded L∗ = 0.0041 ± 0.0001L . Two independent methods were applied to obtain stellar mass. First, we used the empirical M∗−MKs relation (applying the metallicity obtained in Section 3.1.2) from Mann et al. (2019) to obtain M∗ = 0.1721 ± 0.0044M . We also applied stellar evolution modeling, using the models presented in Fernandes et al. (2019), using as a constraint the luminosity inferred above and the metallicity derived in Sect. 3.1.2). We considered the stellar age to be >1 Gyr in the absence of signs of youth, such as presence of prominent flares (Ilin et al. 2019, see Section 3.1.3). We obtained with this method M∗ = 0.176 ± 0.004 M . This uncertainty reflects the error propagation on the stellar luminosity and metallicity, but also the uncertainty associated with the input physics of the stellar models. We combined these two mass estimates as in Van Grootel et al. (2018) to obtain M∗ = 0.174±0.004M as our best estimate for the stellar mass of EPIC 249631677. Given its proximity, we expect minimal extinction (Av) for the target; the SED fitting analysis described in Section 3.2.1 similarly constrains it to be 0.02 at 3σ confidence and we adopt that upper limit here. Finally, we note that given its luminosity, mass, and Gaia colors this star is likely to be fully convective (Jao et al. 2018; Rabus et al. 2019). Due to the absence of a strong constraint on the stellar density from the transits, we further obtained stellar radius, surface gravity, effective temperature and density from our evolutionary models. Table 1 summarizes the results from this analysis, along with other properties of the star. Our values are consistent with those listed in the TESS Input Catalog (Stassun et al. 2019), and we adopt them for the remainder of this analysis. 3.1.2. Reconaissance Spectroscopy To confirm EPIC 249631677’s stellar properties and better characterize the system, we acquired an optical spectrum using Keck/HIRES (Vogt et al. 1994) on UT 30 May 2020. The observation took place in 0.6 00 effective seeing and using the C2 decker without the HIRES iodine gas cell, giving an effective resolution of λ/∆λ ≈ 55, 000 from 3600 ˚A to 7990 ˚A. We exposed for 1800 s and obtained SNR of roughly 23 per pixel. Data reduction followed the standard approach of the California Planet Search consortium (Howard et al. 2010). We used our Keck/HIRES radial velocity and Gaia DR2 data to estimate the 3D galactic (UV W) space velocity using the online kinematics calculator[2] of Rodriguez (2016). Following Chubak et al. (2012), our Keck/HIRES spectrum gives a barycentric radial velocity of 6.25±0.17 km s−1 . With the Gaia-derived coordinates, proper motion, and distance listed in Table 1, we find (U, V, W) values of (−17.02, −9.06, +33.66) km s−1 , indicating a likely membership in the Milky Way’s thin disk (Bensby et al. 2014). Using the SpecMatch-Empirical algorithm (Yee et al. 2017), we derive from our HIRES spectrum stellar parameters of Teff = 3195 ± 70 K, R∗ = 0.23 ± 0.10R , and [Fe/H]= −0.24 ± 0.09, consistent with the values tabulated in Table 1. The three best-matching stars in the SpecMatch-Empirical template library are GJ 15B, GJ 447, and GJ 725B, which have spectral types of M3.5V, M4V, and M3.5V, respectively. Given the close match between the spectra of these stars and our target (see Fig. 4), we therefore classify EPIC 249631677 as an M dwarf with subclass 3.5±0.5. We see no evidence of emission line cores at Hα, consistent with our determination that our target is not a young star. We see no evidence of spectral broadening compared to these three stars (which all have v sin i < 2.5 km s−1 ; Reiners et al. 2012), so we set an upper limit on EPIC 249631677’s projected rotational velocity of < 5 km s−1 , comparable to the spectral resolution of HIRES. 3.1.3. Stellar Variability The long-term variations apparent in the everest light curve (Figure 1) are not evident in light curves from other reduction pipelines (e.g., K2SFF). These variations likely arise from systematics and are not reliable for estimating the stellar rotation period (Esselstein et al. 2018). Similarly, no flares are apparent either in the K2 or SPECULOOS data. Flare rates peak for ∼M3.5 stars in TESS data (G¨unther et al. 2020). However, given the long integration time of 29.4 minutes as well as a need for data processing which corrects for the saw-tooth pattern, flare signals, unless very prominent, are expected to be difficult to detect in K2 long cadence data. 3.2. Vetting In order to produce a transit depth on the level of 0.2% in the light curve of the primary target, a background eclipsing binary producing eclipses with depths of 25% to 50% would have to be 5.25 to 6.0 mag fainter than the target, respectively. Qualitatively, the odds of EPIC 249631677 hosting a planet are higher than the odds of such magnitude contrast eclipsing binary being present within the SPECULOOS aperture, given occurrence rates of M-dwarf planets (Dressing & Charbonneau 2013; Mulders et al. 2015; Hardegree-Ullman et al. 2019). A stringent quantitative constraint can be placed using the ingress/egress duration (T12/T34) compared to the total transit duration (T14) (Seager & Mall´enOrnelas 2003, Equation 21). Such a test yields an upper limit on the relative radius of the transiting body. By assuming equal effective surface temperatures, the lower magnitude limit ∆m (corresponding to a flux difference ∆F) for a blended binary mimicking a signal of depth δ is given by: Using the posterior for the transit fit (See Section 3.3), we find for EPIC 249631677 that such a background object can be fainter at most by 1.73 mag at the 3σ level. Fortunately, EPIC 249631677 has a significant proper motion, ∼140 mas yr−1 , which allows us to investigate the presence of background sources at its current sky position. We looked at archival imaging of EPIC 246331677 going back to 1953[3] . A POSS I plate from 1953 is the publicly available oldest image of EPIC 249631677, and it does not show any background source at the current position of the target as shown in Figure 6. The plate is sensitive to objects at least 3.5 magnitudes fainter than the target. Similarly, the Hubble Guide Star Catalogue (GSC), with a limiting magnitude of 20 (Lasker et al. 1990), does not show any background source. While POSS II would go the deepest in terms of limiting magnitude (20.8, Reid et al. 1991), the star has moved appreciably closer to its current location, precluding a definitive measurement from this image. Overall, using archival images we can rule out the possibility of the transit signal originating from background star at a high level of confidence. 3.2.1. Binarity of the Host Star Despite the lack of background sources, the host star could produce a false-positive transit signal if it were a grazing eclipsing binary or a hierarchical eclipsing binary. We investigated the evidence for host star binarity using the isochrones software package (Morton 2015), which performs isochrone fitting in the context of the MESA (Paxton et al. 2011, 2013, 2015) Isochrones and Stellar Tracks database (Dotter 2016; Choi et al. 2016). Single-star and binary evolutionary models are available within isochrones, and the inference is performed via the nested sampling algorithm MULTINEST (Feroz et al. 2009) (as implemented in the PyMultiNest software package (Buchner et al. 2014)), which allows for direct comparisons of the Bayesian evidence ln Z. We tested both single-star and binary models using the priors on photometric magnitudes and stellar distance described in Section 3.1.1. The inferred properties from the single-star model fit are consistent with those given in Table 1 at the 2σ level. The ln Z for the single-star model is −213.86±0.04, whereas the ln Z for the binary model is −229.6 ± 0.2. According to Kass & Raftery (1995), the corresponding Bayes factor of 16 indicates “decisive” evidence in favor of the single-star model. We also examined our Keck/HIRES spectrum for secondary lines that would indicate the presence of another star following the approach of Kolbl et al. (2015). We found no evidence of additional lines down to the method’s standard sensitivity limit of ∆V = 5 mag for ∆v > 10 km s−1 , consistent with EPIC 249631677 being a single, isolated star. We therefore conclude that the available data strongly support EPIC 246331677 being a single star. 3.2.2. Photometric Tests We performed a series of tests on the photometric data to rule out false-positive scenarios. First, we performed an even-odd test on the target using K2 photometry. The even and odd transits are consistent with one another in transit depth to within 1σ. We also looked for secondary eclipses in the phase-folded light curve and found none to be present. Note that since we observe consistent signals in both the K2 and SPECULOOS data sets, we can rule out the signal originating from systematics. The transit depth in SPECULOOS observations with I+z filter, which is redder than Kepler bandpass, are consistent to K2 transit depths within 1σ level, keeping up with the expectation of the achromatic nature of planetary transit. Furthermore, a massive companion, such as a faint white dwarf, can be ruled out using the ellipsoidal variation, which puts a 3σ upper limit on the mass of any companions at the given orbital period of the transit signal as ∼100 MJup (Morris 1985; Niraula et al. 2018). From the transit fit, we can rule out a grazing eclipse originating from a larger transiting object (i.e. ≥ 2R⊕) at >3σ confidence. Together, these tests rule out the object at 3.14 days being a massive companion. 3.3. Transit Fitting We used the refined estimates of the host properties together with a joint analysis of the K2 and SPECULOOS light curves to derive the planetary properties. In order to calculate the transit model, we used batman (Kreidberg 2015). We simultaneously model both the K2 observation as well as the ground-based observations with 21 parameters in a Monte Carlo Markov Chain (MCMC) framework using the emcee package (ForemanMackey et al. 2013). We use a Gaussian prior on the scaled semi-major axis of the orbit a/R∗ of N (25.72, 0.27), derived using Eq. 30 from Winn (2010) along with the stellar density of 32.6 ± 1.0 g cm−3 (see Section 3.1.1) and orbital period of 3.1443 days from the TLS search. As for the limb darkening, we use the non-informative q1, q2 parameterization of the quadratic limb-darkening law as suggested by Kipping (2013). We fixed the eccentricity to 0, given that the expected time of circularization is 50 Myr (assuming a quality factor Qp ∼ 500, Goldreich & Soter 1966; Patra et al. 2017), which is at least an order of magnitude smaller than the estimated age of the system. For K2 data, we supersample the transits by a factor of 15 in batman to take into account the effect of non-negligible integration time. As for the ground-based data, we use second-order polynomials to detrend against the observables airmass and FWHM. We ran the MCMC for 50,000 steps with 150 walkers performing a combined fit of K2 and SPECULOOS — (1) This work. (2) Huber et al. (2016). (3) Stassun et al. (2019). (4) Cutri et al. (2003). (5) Gaia Collaboration et al. (2018). (6) Cutri et al. (2013). (7) This work, evolutionary model analysis. (8) This work, Keck/HIRES analysis. (9) This work, SED analysis. References data, and use the last half of the run to build the parameter posteriors. We assessed the convergence of walkers using the suggested autocorrelation test for emcee. The resulting median values from the fit with 1σ deviation are reported in Table 2, while the best-fit transit models are shown in Figure 2 and Figure 3. [2] http://kinematics.bdnyc.org/query [3] http://stdatu.stsci.edu/cgi-bin/dss form

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