Building a credit card-first wedge into healthcare

Ness

Many of us have made changes to data based on requirements that later evolve. By the time we realize it, it becomes impossible to roll back. Change isn't just a constant; it's an essential part of managing data that demands a sophisticated approach. Enter the Git-like functionality of Dremio's data catalog Nessie. Just as Git has become fundamental to software development, data engineers need similar tools to work in parallel, compare data versions, promote changes to production and roll back data when needed. Nessie provides data engineers with a Git-like version control system for managing data versions, branches, merges, and commits. This can be very helpful when multiple data engineers are working with and transforming data at the same time. Nessie allows each engineer to work in separate branches while maintaining a single source of truth in the form of the main branch. This functionality empowers data engineering teams to maintain data quality collaboratively in the face of unrelenting change. This article provides a step-by-step guide demonstrating how Nessie, Dremio and MinIO work together to enhance data quality and collaboration in your data engineering workflows. Whether you're a data engineer, ML engineer, or just a modern data lake enthusiast, this blog equips you with the knowledge and tools needed to effectively enhance your data versioning practices. Understanding the Basics: Nessie Workloads Nessie allows for Git-like workloads that let you test, develop and push to production. Let's break down some key concepts: Just like in Git, branches in Nessie allow you to work on different data engineering tasks concurrently. For example, you might have a branch for feature development, data cleaning and data transformation. Each branch can have its own set of data changes. Branches: In Nessie, a commit represents a snapshot of the data at a specific point in time. When you make changes to your data, you create a new commit, which records those changes. Commits are linked to a specific branch. When you need to revert your data to a stable or known-good state, Nessie makes it easy to select a specific commit and roll back to that data version. This ensures data quality and consistency. Commits: : Nessie allows you to merge the changes from one branch into another. This is similar to merging code changes in Git. When you merge a branch, the data changes made in that branch become part of the target branch. Merges The Components is a distributed analytics engine that operates as an open-source platform, offering an intuitive self-service interface for data exploration, transformation and collaborative work. Its design is grounded in a high-speed columnar memory format. Dremio Apache Arrow, We’ve already explored how to deploy Dremio with Kubernetes, and also how to . For additional information about Dremio, consult . query Iceberg tables on MinIO with Dremio Dremio Resources is high-performance object storage. Renowned for its exceptional and , MinIO serves as a pivotal component in building and maintaining modern data lake infrastructures. MinIO empowers architects to efficiently manage and store massive volumes of data on-prem, on bare metal, on the edge, or on any of the public clouds. MinIO speed scalability is an open table format suited for managing large volumes of data in data lakes. Unique features like time travel, dynamic schema evolution, and Partition Evolution make it a game-changer, allowing query engines to work concurrently on the same data safely and efficiently. See for more information about Iceberg features. Apache Iceberg MinIO The Definitive Guide to Lakehouse Architecture with Iceberg and Getting Started You’ll need the and for this tutorial. The easiest way to get both if you don’t already have them is to install Docker Desktop. Docker Engine Docker Compose This part of the tutorial is based on Dremio’s . You can get the repo for this project . blog post here To begin, open a terminal and navigate to the folder where you cloned/downloaded the repo, and run the command below to start up Dremio. docker-compose up dremio Wait a few moments and then navigate to to access Dremio. Update the requested fields and then click Next. http://localhost:9047 Next, run the following command to start MinIO. docker-compose up minio The final command below will start up Nessie. docker-compose up docker-compose up nessie Navigate to to log in to MinIO with the username and password of . You’ll be prompted to create a bucket. http://localhost:9001 minioadmin:minioadmin Make one named . iceberg-datalake Then head back over to Dremio at and click on Add Source and select Nessie. http://localhost:9047 Set the Name to nessie Set the endpoint URL to http://nessie:19120/api/v2 Set the authentication to none Don’t click Save yet. Instead, in the navigation panel on the left, click Storage. MinIO is S3-API compatible object storage and can use the same connection pathways as AWS S3. For your access key, set minioadmin For your secret key, set minioadmin Set root path to /iceberg-datalake Scroll down for the next set of instructions. Click on the button under to create and configure the following properties. Add Property Connection Properties to fs.s3a.path.style.access true to fs.s3a.endpoint minio:9000 to dremio.s3.compat true Uncheck Encrypt connection Then click Save. You should now see Nessie Catalogs in your data sources. Create a Data Source In Dremio navigate to SQL Runner on the left-hand side. Make sure that in the upper right area of the text editor is set to our Nessie source. Otherwise, you will have to reference the context like instead of just to run this query. Copy and paste the SQL below and run. Context nessie.SalesData SalesData CREATE TABLE SalesData (
    id INT,
    product_name VARCHAR,
    sales_amount DECIMAL,
    transaction_date DATE
) PARTITION BY (transaction_date); Run the query below to insert data into the table you just created. INSERT INTO SalesData (id, product_name, sales_amount, transaction_date)
VALUES
    (1, 'ProductA', 1500.00, '2023-10-15'),
    (2, 'ProductB', 2000.00, '2023-10-15'),
    (3, 'ProductA', 1200.00, '2023-10-16'),
    (4, 'ProductC', 1800.00, '2023-10-16'),
    (5, 'ProductB', 2200.00, '2023-10-17'); Navigate back to MinIO to see that your data lake has been populated with the Iceberg tables. Branching and Merging with Nessie Return to Dremio at . Begin by querying a table on the main branch using the syntax: http://localhost:9047 AT BRANCH SELECT * FROM  nessie.SalesData AT BRANCH main; Create an ETL (Extract Transform and Load) branch to allow you to play around with and transform data without impacting production. CREATE BRANCH etl_06092023 in nessie Within the ETL branch, insert new data into the table: USE BRANCH etl_06092023 in nessie;
INSERT INTO nessie.SalesData (id, product_name, sales_amount, transaction_date) VALUES
(6, 'ProductC', 1400.00, '2023-10-18'); Confirm the immediate availability of the new data within the ETL branch: SELECT * FROM nessie.SalesData AT BRANCH etl_06092023; Note the isolation of changes from users on the main branch: SELECT * FROM nessie.SalesData AT BRANCH main; Merge the changes from the ETL branch back into the main branch: MERGE BRANCH etl_06092023 INTO main in nessie; Select the main branch again to see that the changes have indeed been merged. SELECT * FROM nessie.SalesData AT BRANCH main This branching strategy enables data engineers to independently handle numerous transactions across multiple tables. When they are ready, data engineers can merge these transactions into a single, comprehensive multi-table transaction within the main branch. Conclusion This blog post delved into the power of Git-like version control in data engineering, emphasizing how seamlessly manages data versions, branches and merges. This step-by-step guide demonstrates how Nessie, in collaboration with Dremio and MinIO, as the object storage foundation, enhances data quality and collaboration in data engineering workflows. Nessie Let us know what your data lakehouse looks like at or on . hello@minio.io our Slack channel Also appears . here

Data Lake Mysteries Revealed: Nessie, Dremio, and MinIO Make Waves

ডেটা লেক রহস্য প্রকাশিত হয়েছে: নেসি, ড্রেমিও এবং মিনিও মেক ওয়েভস

Data-Lake-Rätsel aufgedeckt: Nessie, Dremio und MinIO sorgen für Aufsehen

Misterios del lago de datos revelados: Nessie, Dremio y MinIO hacen olas

Les mystères du Data Lake révélés : Nessie, Dremio et MinIO font des vagues

डेटा लेक रहस्य का खुलासा: नेस्सी, ड्रेमियो और मिनिओ मेक वेव्स

データレイクの謎が明らかに: Nessie、Dremio、MinIO が波紋を起こす

데이터 레이크의 미스터리 공개: Nessie, Dremio 및 MinIO가 물결을 일으키다

Mistérios do Data Lake revelados: Nessie, Dremio e MinIO fazem ondas

Тайны озера данных раскрыты: Несси, Дремио и MinIO вызывают фурор

Data Lake'in Gizemleri Ortaya Çıktı: Nessie, Dremio ve MinIO Dalga Yaratıyor

Những bí ẩn về hồ dữ liệu được tiết lộ: Nessie, Dremio và MinIO tạo nên làn sóng

数据湖谜团揭开：Nessie、Dremio 和 MinIO 掀起波澜

FTC v. Amazon Court Filing, retrieved on Sep 26, 2023, is part of . You can jump to any part in this filing . This is part 60 of 80. HackerNoon’s Legal PDF Series here COUNT IV: UNFAIR METHOD OF COMPETITION (15 U.S.C. § 45(a)) 457. Plaintiff FTC re-alleges and incorporates by reference the allegations in paragraphs 1-456 above. 458. Amazon has engaged in an unfair method of competition, called Project Nessie, that (redacted) 459. Amazon designed and used its Project Nessie pricing system (redacted) 460. Amazon’s Project Nessie pricing system (redacted) 461 (Redacted) 462. Amazon’s use of its Project Nessie pricing system is an unfair method of competition in violation of Section 5(a) of the FTC Act, 15 U.S.C. § 45(a). 463. There is no valid and cognizable justification for Amazon’s use of Project Nessie. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 2:23-cv-01495 retrieved on October 2, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ftc.gov

How Amazon's Project Nessie Supports Its Anti-Competitive Tactics

Getting Started With Apache Iceberg and Resources if You Would Like To Go Further

Apache Iceberg is an innovative data lakehouse table format designed to revolutionize how you manage large-scale data across various storage layers, such as Hadoop and AWS S3. By treating these diverse storage solutions as a cohesive, universal database, Apache Iceberg facilitates seamless integration with numerous tools, platforms, and interfaces, enhancing both flexibility and accessibility. One of the standout features of Apache Iceberg is its support for ACID transactions, which ensures data integrity by enabling atomicity, consistency, isolation, and durability in data processing operations. Additionally, Apache Iceberg's Time Travel capability allows users to query historical versions of data, providing valuable insights into data changes over time. The format also supports dynamic schema evolution and partitioning adjustments without downtime, which simplifies the management of data as it grows and changes. Apache Iceberg also includes a host of other advanced features that enhance data usability and management. These include snapshot isolation for concurrent data access, upserts and deletes within tables, and handling large-scale metadata efficiently. To help you harness the full potential of Apache Iceberg, our latest blog series gathers a collection of straightforward, hands-on tutorials. These guides are designed to provide you with practical experience in working with Apache Iceberg, from setting up your first table to executing complex data operations. Whether you're a data scientist, engineer, or analyst, these tutorials offer a valuable opportunity to enhance your data handling skills and leverage Apache Iceberg’s powerful features in your projects. Self-Contained Exercises These are completely self-contained and can be done from your laptop; all infrastructure is spun up as Docker containers. End-to-End Basic Data Engineering Tutorial (Spark, Dremio, Superset)
From Postgres to Dashboards with Dremio and Apache Iceberg
From SQLServer to Dashboards with Dremio and Apache Iceberg
From MongoDB to Dashboards with Dremio and Apache Iceberg
Intro to Dremio, Nessie, and Apache Iceberg on Your Laptop
Using Flink with Apache Iceberg and Nessie
Getting Started with Project Nessie, Apache Iceberg, and Apache Spark Using Docker Require Online Services These are tutorials that will require signing up for services like Upsolver, Dremio, AWS, and others to complete. How to Convert JSON Files Into an Apache Iceberg Table with Dremio
How to Convert CSV Files into an Apache Iceberg table with Dremio
Run Graph Queries on Apache Iceberg Tables with Dremio & Puppygraph
BI Dashboards with Apache Iceberg Using AWS Glue and Apache Superset
Streaming and Batch Data Lakehouses with Apache Iceberg, Dremio and Upsolver
Git for Data with Dremio’s Lakehouse Catalog: Easily Ensure Data Quality in Your Data Lakehouse
How to Create a Lakehouse with Airbyte, S3, Apache Iceberg, and Dremio Video Playlists: Hands-On With Apache Iceberg


Apache Iceberg Lakehouse Engineering 101
In summary, Apache Iceberg is more than just a data storage format; it is a comprehensive solution that adapts to the complexities of modern data environments, ensuring robust data integrity, flexibility, and scalability. Apache Iceberg is an innovative data lakehouse table format designed to revolutionize how you manage large-scale data across various storage layers, such as Hadoop and AWS S3. By treating these diverse storage solutions as a cohesive, universal database, Apache Iceberg facilitates seamless integration with numerous tools, platforms, and interfaces, enhancing both flexibility and accessibility. One of the standout features of Apache Iceberg is its support for ACID transactions, which ensures data integrity by enabling atomicity, consistency, isolation, and durability in data processing operations. Additionally, Apache Iceberg's Time Travel capability allows users to query historical versions of data, providing valuable insights into data changes over time. The format also supports dynamic schema evolution and partitioning adjustments without downtime, which simplifies the management of data as it grows and changes. Apache Iceberg also includes a host of other advanced features that enhance data usability and management. These include snapshot isolation for concurrent data access, upserts and deletes within tables, and handling large-scale metadata efficiently. To help you harness the full potential of Apache Iceberg, our latest blog series gathers a collection of straightforward, hands-on tutorials. These guides are designed to provide you with practical experience in working with Apache Iceberg, from setting up your first table to executing complex data operations. Whether you're a data scientist, engineer, or analyst, these tutorials offer a valuable opportunity to enhance your data handling skills and leverage Apache Iceberg’s powerful features in your projects. Self-Contained Exercises These are completely self-contained and can be done from your laptop; all infrastructure is spun up as Docker containers. End-to-End Basic Data Engineering Tutorial (Spark, Dremio, Superset) From Postgres to Dashboards with Dremio and Apache Iceberg From SQLServer to Dashboards with Dremio and Apache Iceberg From MongoDB to Dashboards with Dremio and Apache Iceberg Intro to Dremio, Nessie, and Apache Iceberg on Your Laptop Using Flink with Apache Iceberg and Nessie Getting Started with Project Nessie, Apache Iceberg, and Apache Spark Using Docker End-to-End Basic Data Engineering Tutorial (Spark, Dremio, Superset) End-to-End Basic Data Engineering Tutorial (Spark, Dremio, Superset) End-to-End Basic Data Engineering Tutorial (Spark, Dremio, Superset) From Postgres to Dashboards with Dremio and Apache Iceberg From Postgres to Dashboards with Dremio and Apache Iceberg From Postgres to Dashboards with Dremio and Apache Iceberg From SQLServer to Dashboards with Dremio and Apache Iceberg From SQLServer to Dashboards with Dremio and Apache Iceberg From SQLServer to Dashboards with Dremio and Apache Iceberg From MongoDB to Dashboards with Dremio and Apache Iceberg From MongoDB to Dashboards with Dremio and Apache Iceberg From MongoDB to Dashboards with Dremio and Apache Iceberg Intro to Dremio, Nessie, and Apache Iceberg on Your Laptop Intro to Dremio, Nessie, and Apache Iceberg on Your Laptop Intro to Dremio, Nessie, and Apache Iceberg on Your Laptop Using Flink with Apache Iceberg and Nessie Using Flink with Apache Iceberg and Nessie Using Flink with Apache Iceberg and Nessie Getting Started with Project Nessie, Apache Iceberg, and Apache Spark Using Docker Getting Started with Project Nessie, Apache Iceberg, and Apache Spark Using Docker Getting Started with Project Nessie, Apache Iceberg, and Apache Spark Using Docker Require Online Services These are tutorials that will require signing up for services like Upsolver, Dremio, AWS, and others to complete. How to Convert JSON Files Into an Apache Iceberg Table with Dremio How to Convert CSV Files into an Apache Iceberg table with Dremio Run Graph Queries on Apache Iceberg Tables with Dremio & Puppygraph BI Dashboards with Apache Iceberg Using AWS Glue and Apache Superset Streaming and Batch Data Lakehouses with Apache Iceberg, Dremio and Upsolver Git for Data with Dremio’s Lakehouse Catalog: Easily Ensure Data Quality in Your Data Lakehouse How to Create a Lakehouse with Airbyte, S3, Apache Iceberg, and Dremio How to Convert JSON Files Into an Apache Iceberg Table with Dremio How to Convert JSON Files Into an Apache Iceberg Table with Dremio How to Convert JSON Files Into an Apache Iceberg Table with Dremio How to Convert CSV Files into an Apache Iceberg table with Dremio How to Convert CSV Files into an Apache Iceberg table with Dremio How to Convert CSV Files into an Apache Iceberg table with Dremio Run Graph Queries on Apache Iceberg Tables with Dremio & Puppygraph Run Graph Queries on Apache Iceberg Tables with Dremio & Puppygraph Run Graph Queries on Apache Iceberg Tables with Dremio & Puppygraph BI Dashboards with Apache Iceberg Using AWS Glue and Apache Superset BI Dashboards with Apache Iceberg Using AWS Glue and Apache Superset BI Dashboards with Apache Iceberg Using AWS Glue and Apache Superset Streaming and Batch Data Lakehouses with Apache Iceberg, Dremio and Upsolver Streaming and Batch Data Lakehouses with Apache Iceberg, Dremio and Upsolver Streaming and Batch Data Lakehouses with Apache Iceberg, Dremio and Upsolver Git for Data with Dremio’s Lakehouse Catalog: Easily Ensure Data Quality in Your Data Lakehouse Git for Data with Dremio’s Lakehouse Catalog: Easily Ensure Data Quality in Your Data Lakehouse Git for Data with Dremio’s Lakehouse Catalog: Easily Ensure Data Quality in Your Data Lakehouse How to Create a Lakehouse with Airbyte, S3, Apache Iceberg, and Dremio How to Create a Lakehouse with Airbyte, S3, Apache Iceberg, and Dremio How to Create a Lakehouse with Airbyte, S3, Apache Iceberg, and Dremio Video Playlists: Hands-On With Apache Iceberg Apache Iceberg Lakehouse Engineering 101
In summary, Apache Iceberg is more than just a data storage format; it is a comprehensive solution that adapts to the complexities of modern data environments, ensuring robust data integrity, flexibility, and scalability. Hands-On With Apache Iceberg Hands-On With Apache Iceberg Hands-On With Apache Iceberg Hands-On With Apache Iceberg Apache Iceberg Lakehouse Engineering 101 In summary, Apache Iceberg is more than just a data storage format; it is a comprehensive solution that adapts to the complexities of modern data environments, ensuring robust data integrity, flexibility, and scalability. Apache Iceberg Lakehouse Engineering 101 Apache Iceberg Lakehouse Engineering 101 Apache Iceberg Lakehouse Engineering 101 In summary, Apache Iceberg is more than just a data storage format; it is a comprehensive solution that adapts to the complexities of modern data environments, ensuring robust data integrity, flexibility, and scalability.

16 Guides to Get You Started with Apache Iceberg

Metaphysics and Mathematics 2: The Complex Space of Prime Numbers

The Multitudes Within "Be yourself." The well-meaning advice plastered on motivational posters and whispered by concerned aunts the world over. Sounds nice in theory, right? A comforting idea for those moments when we feel like imposters, fumbling awkwardly through new job interviews or high-stakes presentations. But here's the thing…it's pretty terrible, lazy advice. Why? Because it's woefully incomplete and often downright lazy. Let me paint you a picture. Imagine gearing up for an important meeting. You've spent days carefully crafting your pitch, meticulously researching every nuanced detail. You step into that room, ready to crush it…and the first thing someone tells you is, "Just be yourself." …Helpful. Thanks a bunch. See, the trouble is are not a monolith. We all contain a complex cocktail of personalities, strengths, and quirks. Which version of yourself should you "be"? The goofy, pun-loving friend from brunch? The ultra-competitive version you channel on the basketball court? The quiet, introspective poet who emerges in solitude? you The Multitudes Within The truth is, there's no single "you" to whip out for every situation life tosses your way. "Being yourself" implies a static, unchanging identity, and that, my friends, is a myth. We grow, we evolve, we . adapt Imagine yourself as an RPG character (remember those?). You have different skill sets – charisma, intellect, focus, whatever your build focuses on. Now, when facing different challenges, you wisely choose which of those skills to bring to the forefront. That's not being fake, that's being strategic. So, instead of "be yourself," I propose an upgrade: "You have multitudes. Which version of yourself is suited to the task? The negotiator, the visionary, the empathetic listener… deploy strategically." The School of Applied Self-ness Let's break this down into a few actionable steps: It's time to Marie Kondo your personality. Not everything sparks joy, but it's all useful data. Start by listing your strengths, weaknesses, and the different "roles" you play in life. Brutal honesty is your friend here. The Inventory: Just like a good spy scopes out the scene before a mission, analyze the situation you're about to enter. What's the goal? Who's the audience? What outcome do you want? Situational Awareness: Time to play to your strengths. If a negotiation calls for cool-headed logic, maybe downplay the emotional side and channel your inner data analyst. Need to inspire a team? Time for the charismatic hype-machine to take the stage. Skill Match: Here’s some real-world scenarios where this approach pays off: Scenario 1: The Difficult Conversation Nobody likes confrontation, but sometimes you need to address a tough issue. Maybe it's a conflict with a coworker, a delicate piece of feedback, or a heart-to-heart with a loved one. In these situations, the "blunt and brutally honest" version of you might not be the best ambassador. Focus on understanding the other person's perspective. Let your genuine compassion lead as you carefully build a bridge to connect perspectives. Think of yourself as a skilled diplomat, negotiating a peaceful resolution for all parties involved. Deploy the Empath: Scenario 2: The Big Presentation You're up on stage, about to deliver the pitch of a lifetime. This isn't the time for your shy, self-deprecating side. Time to bring the thunder. Channel boundless enthusiasm and infectious energy. Paint a vivid picture of the future, the possibilities, the sheer awesomeness of the idea you're presenting. Let your passion carry the day, sweeping the audience along with you. Deploy the Visionary: Scenario 3: The Job Interview First impressions matter. You need to exude competence and confidence, but also build a genuine connection. Where's the sweet spot? Balance warmth with professionalism. Showcase your research on the role and company, weaving in how your particular skills and experiences make you the perfect fit. Think of yourself as a puzzle piece, demonstrating exactly how you'll slot into their larger picture. Deploy the Strategist: The Art of the Shift Now, does this mean you're "faking it"? No. You're not suddenly a different person, just emphasizing certain traits over others. The key is to understand your own defaults. If you're naturally analytical, you might need to work on amplifying your charisma. If you're a people-pleaser, it'll be valuable to practice assertive communication. This isn't about losing yourself; it's about gaining self-awareness. The more you understand your strengths and weaknesses, the more you can consciously play to the situation at hand. A Word on Personal Growth This approach naturally ties into your personal development journey. As you learn more, gain experience, and challenge yourself, the pool of "personas" you can tap into expands. What might feel forced now can become second nature with practice. A Caveat Does this mean you need to be ruthlessly calculated with every interaction? Absolutely not. Authenticity is still key, but let's think of it as authenticity. It's about showcasing the "you" that's best equipped to get the job done. calibrated And the elephant in the room. What about truly close relationships? Should you also be constantly strategizing with your friends, partners, or family? Here's where a balanced approach is crucial. While a degree of adaptability is always useful, your closest circle deserves a healthy dose of the unfiltered, messy, REAL you. Those are the people who earn the right to see everything, even the not-so-shiny parts. Until then, remember – you're not just one thing. You're a dynamic, adaptable force. Unleash the right part of yourself at the right time, and the world won't know what hit it. Have a great weekend! Scott Also appears . here

It's Time to Ditch "Be Yourself"

এটি "নিজেকে হও" ত্যাগ করার সময়

Es ist Zeit, „Sei du selbst“ aufzugeben

Es hora de deshacerse del "sé tú mismo"

Il est temps d’abandonner « Soyez vous-même »

अब "खुद बनो" को छोड़ने का समय आ गया है

「自分らしくあること」を捨てる時が来た

이제 "Be Yourself"를 버릴 시간입니다

É hora de abandonar o "seja você mesmo"

Пришло время отказаться от «будь собой»

"Kendin Ol"u Bırakmanın Zamanı Geldi

Đã đến lúc từ bỏ "Hãy là chính mình"

是时候放弃“做你自己”了

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This paper is available on arxiv under CC 4.0 license. Authors: (1) Thomas Pethick, EPFL (LIONS) thomas.pethick@epfl.ch; (2) Wanyun Xie, EPFL (LIONS) wanyun.xie@epfl.ch; (3) Volkan Cevher, EPFL (LIONS) volkan.cevher@epfl.ch. Table of Links Abstract & Introduction Related work Setup Inexact Krasnosel’ski˘ı-Mann iterations Approximating the resolvent Last iterate under cohypomonotonicity Analysis of Lookahea Experiments Conclusion & limitations Acknowledgements & References 10 Acknowledgements This work was supported by Google. This work was supported by Hasler Foundation Program: Hasler Responsible AI (project number 21043). This work was supported by the Swiss National Science Foundation (SNSF) under grant number 200021_205011. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement n° 725594 - time-data). References Banach, S. 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Stable Nonconvex-Nonconcave Training via Linear Interpolation: Acknowledgements & References

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