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Coalesce

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Introduction The Data space is almost as volatile as the AI space this year, with many players consolidating. In the summer of 2022, I wrote about The Data Table Format Wars, and the ecosystem has only gotten more interesting since then. I won’t get deep into the weeds on Data Lakes vs. Data Warehouse, etc., but suffice it to say, we are seeing everything coalesce around decoupling data storage from computing. Table formats are a way to standardize lightly opinionated storage. As I opined a couple of years ago, Apache Iceberg seems to be where they are settling. These formats tend to use Apache Parquet on object storage and provide a method for tracking new data and data state changes. In 2023, Databricks announced the open-sourcing of their Unity Catalog, and Onehouse announced OneTable (now X-Table). These projects are meant to give you a single interface to the three popular table formats: Delta, Hudi, and Iceberg. Then, June 2024 happened… What Happened? Snowflake was having its annual Snowflake Summit conference, and on June 3, 2024, they announced Apache Polaris (incubating), a vendor-neutral, open catalog for Apache Iceberg. The next day, and a week before the Databricks conference, Databricks announced their acquisition of Tabular, a company founded by the creators of Iceberg. This still seems weird to everyone from the outside because Iceberg is a spec; it isn’t a product, and Tabular isn’t even the main contributor to Iceberg. In my opinion, Databricks will somehow roll their Deltalake into Iceberg. Behind the scenes, Snowflake had been trying to acquire Tabular. Something happened at the 11th hour, and Databricks came in and laid down a ton more money, and the Snowflake bid was discarded. Snowflake was already two years into their commitment to supporting Iceberg as external storage, which made a lot of sense for them regardless. Snowflake has a reputation concerning the release speed after an announcement, so there was some skepticism, and some even wondered if any code had been written. It turns out Snowflake had been working on this for a while, and it was made available in very short order, with vendors like Dremio, Starburst, CelerData, and Upsolver showing solutions using it in less than two months: a delightful surprise, and a positive sign of things to come. All of that is a long way to set up Apache Polaris, and hopefully, it will give you a sense of the importance that a good catalog plays in all of this. How will any of your compute tools work if you don’t have a unified system to describe all those parquet files in your data lake? Polaris Overview Apache Polaris is an open-source catalog implementation for Apache Iceberg tables. Its main features include: REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines.
Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog.
Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines.
Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables.
Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage.
Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution.
Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality.
Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs.
Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place.
Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. And a lovely little graphic from the docs to put a picture on everything. Borrowing from the Polaris docs for a picture once again, let’s dive into what is happening here in this entity hierarchy graphic: We can have multiple catalogs in a single Polaris install; in this diagram, it is only Catalog1 that has any namespaces or tables defined. A single catalog can be either internal or external. The internal tables allow for read/write functionality, meaning you have full control. The external tables are currently read-only and are intended to mirror data that is in some external catalog, such as a Glue catalog, which Polaris will see as the source of truth once connected. The catalog organizes your iceberg tables; you’ll want to configure it with your storage configuration for the applicable cloud system that you are on. This forms the first architectural layer in the Iceberg table spec. A namespace can have an arbitrary level of nesting and is used to logically group Iceberg tables within a catalog. An Iceberg table will always belong to a namespace. With this configuration in place, you can then use the Polaris catalog to describe your Iceberg tables in such a way as to make them easily accessible and manageable using a large variety of tools. That’s a quick overview of what is happening and what it looks like. The implementation inside of Snowflake provides a really handy UI to manage the implementation; other vendors have their own, and you can just do it through Spark if you like. Summary I continue to believe that Apache Iceberg is going to “win” the table format wars. Apache Polaris focusing on Iceberg is not a bad thing. I want to dig into how RBAC has been implemented in Polaris another time and see if it is as robust as what the Tabular product implemented, which I was a big fan of. I believe Polaris is a big win for the Iceberg community and will help accelerate adoption even further. I’ve seen talk about the Dremio Nessie project getting rolled in, and I’m curious to see what that will look like. I haven’t looked at Nessie in a couple of years, and I thought their git-like semantics mainly had been covered by the Iceberg tagging and branching, which I wrote about last year—yet another story for another day. I’m a big fan of what is happening here. If you are in this space, this is the right train to jump on. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB?
What the Heck Is Malloy?
What the Heck is PRQL?
What the Heck is GlareDB?
What the Heck is SeaTunnel?
What the Heck is LanceDB?
What the heck is SDF?
What the Heck is Paimon?
What the Heck is Proton?
What the Heck is PuppyGraph?
What the Heck is GPTScript?
What the Heck is WarpStream? Introduction Introduction The Data space is almost as volatile as the AI space this year, with many players consolidating. In the summer of 2022, I wrote about The Data Table Format Wars , and the ecosystem has only gotten more interesting since then. I won’t get deep into the weeds on Data Lakes vs. Data Warehouse, etc., but suffice it to say, we are seeing everything coalesce around decoupling data storage from computing. The Data Table Format Wars Table formats are a way to standardize lightly opinionated storage. As I opined a couple of years ago, Apache Iceberg seems to be where they are settling. These formats tend to use Apache Parquet on object storage and provide a method for tracking new data and data state changes. In 2023, Databricks announced the open-sourcing of their Unity Catalog, and Onehouse announced OneTable (now X-Table). These projects are meant to give you a single interface to the three popular table formats: Delta, Hudi, and Iceberg. Then, June 2024 happened… announced announced What Happened? Snowflake was having its annual Snowflake Summit conference, and on June 3, 2024, they announced Apache Polaris (incubating), a vendor-neutral, open catalog for Apache Iceberg. The next day, and a week before the Databricks conference, Databricks announced their acquisition of Tabular, a company founded by the creators of Iceberg. This still seems weird to everyone from the outside because Iceberg is a spec; it isn’t a product, and Tabular isn’t even the main contributor to Iceberg. In my opinion, Databricks will somehow roll their Deltalake into Iceberg. announced announced Behind the scenes, Snowflake had been trying to acquire Tabular. Something happened at the 11th hour, and Databricks came in and laid down a ton more money, and the Snowflake bid was discarded. Snowflake was already two years into their commitment to supporting Iceberg as external storage, which made a lot of sense for them regardless. Snowflake has a reputation concerning the release speed after an announcement, so there was some skepticism, and some even wondered if any code had been written. It turns out Snowflake had been working on this for a while, and it was made available in very short order, with vendors like Dremio, Starburst, CelerData, and Upsolver showing solutions using it in less than two months: a delightful surprise, and a positive sign of things to come. All of that is a long way to set up Apache Polaris , and hopefully, it will give you a sense of the importance that a good catalog plays in all of this. How will any of your compute tools work if you don’t have a unified system to describe all those parquet files in your data lake? Apache Polaris Polaris Overview Apache Polaris is an open-source catalog implementation for Apache Iceberg tables. Its main features include: REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines. Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog. Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines. Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables. Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage. Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution. Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality. Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs. Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place. Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. REST API implementation: Polaris implements Iceberg's REST API, enabling interoperability across multiple query engines. Multi-engine compatibility: It allows various query engines, such as Apache Spark, Apache Flink, Trino, and others, to read and write Iceberg tables through a single catalog. Centralized management: Polaris provides centralized and secure access to Iceberg tables across REST-compatible query engines. Namespace and metadata management: It supports creating namespaces for logical grouping and manages metadata for Iceberg tables. Storage configurations: Polaris offers robust storage configurations for cloud providers like S3, Azure, and Google Cloud Storage. Security features: It implements role-based access control (RBAC) and uses credential vending to secure query execution. Open-source nature: Polaris is fully open-source, allowing for community contributions and vendor neutrality. Interoperability: It enables multiple engines to be used on a single copy of data, reducing storage duplication and ETL costs. Consistent access control: Polaris allows you to manage role-based access control and storage layer security consistently across engines from one place. Flexibility in hosting: It can be hosted on various infrastructures, providing flexibility and reducing vendor lock-in. And a lovely little graphic from the docs to put a picture on everything. Borrowing from the Polaris docs for a picture once again, let’s dive into what is happening here in this entity hierarchy graphic: We can have multiple catalogs in a single Polaris install; in this diagram, it is only Catalog1 that has any namespaces or tables defined. A single catalog can be either internal or external. The internal tables allow for read/write functionality, meaning you have full control. The external tables are currently read-only and are intended to mirror data that is in some external catalog, such as a Glue catalog, which Polaris will see as the source of truth once connected. The catalog organizes your iceberg tables; you’ll want to configure it with your storage configuration for the applicable cloud system that you are on. This forms the first architectural layer in the Iceberg table spec. A namespace can have an arbitrary level of nesting and is used to logically group Iceberg tables within a catalog. An Iceberg table will always belong to a namespace. With this configuration in place, you can then use the Polaris catalog to describe your Iceberg tables in such a way as to make them easily accessible and manageable using a large variety of tools. That’s a quick overview of what is happening and what it looks like. The implementation inside of Snowflake provides a really handy UI to manage the implementation; other vendors have their own, and you can just do it through Spark if you like. Summary I continue to believe that Apache Iceberg is going to “win” the table format wars. Apache Polaris focusing on Iceberg is not a bad thing. I want to dig into how RBAC has been implemented in Polaris another time and see if it is as robust as what the Tabular product implemented, which I was a big fan of. I believe Polaris is a big win for the Iceberg community and will help accelerate adoption even further. I’ve seen talk about the Dremio Nessie project getting rolled in, and I’m curious to see what that will look like. I haven’t looked at Nessie in a couple of years, and I thought their git-like semantics mainly had been covered by the Iceberg tagging and branching, which I wrote about last year—yet another story for another day. wrote about I’m a big fan of what is happening here. If you are in this space, this is the right train to jump on. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is LanceDB? What the heck is SDF? What the Heck is Paimon? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is WarpStream? What The Heck Is DuckDB? What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is SeaTunnel? What the Heck is LanceDB? What the Heck is LanceDB? What the heck is SDF? What the heck is SDF? What the Heck is Paimon? What the Heck is Paimon? What the Heck is Proton? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is PuppyGraph? What the Heck is GPTScript? What the Heck is GPTScript? What the Heck is WarpStream? What the Heck is WarpStream?

What The Heck is Apache Polaris?

How to Add Full Text Search to Your Django App with django-watson

How to Build a Production-Grade Text2SQL Engine

কিভাবে একটি প্রোডাকশন-গ্রেড Text2SQL ইঞ্জিন তৈরি করবেন

So erstellen Sie eine produktionsreife Text2SQL-Engine

Cómo crear un motor Text2SQL de nivel de producción

Comment créer un moteur Text2SQL de niveau production

प्रोडक्शन-ग्रेड टेक्स्ट2एसक्यूएल इंजन कैसे बनाएं

本番環境レベルの Text2SQL エンジンを構築する方法

프로덕션 등급 Text2SQL 엔진을 구축하는 방법

Como construir um mecanismo Text2SQL de nível de produção

Как создать Text2SQL-движок промышленного уровня

Üretim Seviyesinde Bir Text2SQL Motoru Nasıl Oluşturulur

Cách xây dựng một công cụ Text2SQL cấp sản xuất

如何构建生产级 Text2SQL 引擎

Authors:
(1) Keivan Alizadeh;
(2) Iman Mirzadeh, Major Contribution;
(3) Dmitry Belenko, Major Contribution;
(4) S. Karen Khatamifard;
(5) Minsik Cho;
(6) Carlo C Del Mundo;
(7) Mohammad Rastegari;
(8) Mehrdad Farajtabar. Table of Links Abstract and 1. Introduction 2. Flash Memory & LLM Inference and 2.1 Bandwidth and Energy Constraints 2.2 Read Throughput 3 Load From Flash 3.1 Reducing Data Transfer 3.2 Improving Transfer Throughput with Increased Chunk Sizes 3.3 Optimized Data Management in DRAM 4 Results 4.1 Results for OPT 6.7B Model 4.2 Results for Falcon 7B Model 5 Related Works 6 Conclusion and Discussion, Acknowledgements and References 2.2 Read Throughput Flash memory systems perform optimally with large sequential reads. For instance, benchmarks on an Apple MacBook Pro M2 with 2TB flash demonstrate speeds exceeding 6GiB/s for a 1GiB linear read of an uncached file. However, this high bandwidth is not replicated for smaller, random reads due to the inherent multi-phase nature of these reads, encompassing the operating system, drivers, interrupt handling, and the flash controller, among others. Each phase introduces latency, disproportionately affecting smaller reads. To circumvent these limitations, we advocate two primary strategies, which can be employed jointly. The first involves reading larger chunks of data. For smaller blocks, a substantial part of the overall read time is spent waiting for data transfer to begin. This is often referred to as latency to first byte. This latency reduces the overall throughput of each read operation considerably, because the overall measured throughput has to take into account not just the speed of transfer once it begins, but the latency before it begins as well, which penalizes small reads. This means that if we coalesce the reads for rows and colums of the FFN matrices, we can pay the latency cost only once for any given row/column pair in both matrices, and higher throughput can be realized. This principle is depicted in Figure 2b. Perhaps a counterintuitive yet interesting observation is that in some scenarios, it will be worthwhile to read more than needed (but in larger chunks) and then discard, than only reading strictly the necessary parts but in smaller chunks. The second strategy leverages parallelized reads, utilizing the inherent parallelism within storage stacks and flash controllers. Our results indicate that throughputs appropriate for sparse LLM inference are achievable on modern off-the-shelf hardware using 32KiB or larger random reads across multiple threads. Motivated by the challenges described in this section, in section 3, we propose methods to optimize data transfer volume and enhance read throughput to significantly enhance inference speeds. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Keivan Alizadeh; (2) Iman Mirzadeh, Major Contribution; (3) Dmitry Belenko, Major Contribution; (4) S. Karen Khatamifard; (5) Minsik Cho; (6) Carlo C Del Mundo; (7) Mohammad Rastegari; (8) Mehrdad Farajtabar. Authors: Authors: (1) Keivan Alizadeh; (2) Iman Mirzadeh, Major Contribution; (3) Dmitry Belenko, Major Contribution; (4) S. Karen Khatamifard; (5) Minsik Cho; (6) Carlo C Del Mundo; (7) Mohammad Rastegari; (8) Mehrdad Farajtabar. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Flash Memory & LLM Inference and 2.1 Bandwidth and Energy Constraints 2. Flash Memory & LLM Inference and 2.1 Bandwidth and Energy Constraints 2.2 Read Throughput 2.2 Read Throughput 3 Load From Flash 3 Load From Flash 3.1 Reducing Data Transfer 3.1 Reducing Data Transfer 3.2 Improving Transfer Throughput with Increased Chunk Sizes 3.2 Improving Transfer Throughput with Increased Chunk Sizes 3.3 Optimized Data Management in DRAM 3.3 Optimized Data Management in DRAM 4 Results 4 Results 4.1 Results for OPT 6.7B Model 4.1 Results for OPT 6.7B Model 4.2 Results for Falcon 7B Model 4.2 Results for Falcon 7B Model 5 Related Works 5 Related Works 6 Conclusion and Discussion, Acknowledgements and References 6 Conclusion and Discussion, Acknowledgements and References 2.2 Read Throughput Flash memory systems perform optimally with large sequential reads. For instance, benchmarks on an Apple MacBook Pro M2 with 2TB flash demonstrate speeds exceeding 6GiB/s for a 1GiB linear read of an uncached file. However, this high bandwidth is not replicated for smaller, random reads due to the inherent multi-phase nature of these reads, encompassing the operating system, drivers, interrupt handling, and the flash controller, among others. Each phase introduces latency, disproportionately affecting smaller reads. To circumvent these limitations, we advocate two primary strategies, which can be employed jointly. The first involves reading larger chunks of data. For smaller blocks, a substantial part of the overall read time is spent waiting for data transfer to begin. This is often referred to as latency to first byte. This latency reduces the overall throughput of each read operation considerably, because the overall measured throughput has to take into account not just the speed of transfer once it begins, but the latency before it begins as well, which penalizes small reads. This means that if we coalesce the reads for rows and colums of the FFN matrices, we can pay the latency cost only once for any given row/column pair in both matrices, and higher throughput can be realized. This principle is depicted in Figure 2b. Perhaps a counterintuitive yet interesting observation is that in some scenarios, it will be worthwhile to read more than needed (but in larger chunks) and then discard, than only reading strictly the necessary parts but in smaller chunks. The second strategy leverages parallelized reads, utilizing the inherent parallelism within storage stacks and flash controllers. Our results indicate that throughputs appropriate for sparse LLM inference are achievable on modern off-the-shelf hardware using 32KiB or larger random reads across multiple threads. Motivated by the challenges described in this section, in section 3, we propose methods to optimize data transfer volume and enhance read throughput to significantly enhance inference speeds. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Large Language Models on Memory-Constrained Devices Using Flash Memory: Read Throughput

I Made ezpkg.io: Simplify Your Go Projects with Reusable Packages

Any views expressed in the below are the personal views of the author and should not form the basis for making investment decisions, nor be construed as a recommendation or advice to engage in investment transactions. Mayday Mayday Mayday Some of you degens were screaming “Mayday” as you watched crypto markets puke from mid-April until the present. There was so much pain that momentum-chasing paper hands like proclaimed that they were “done” with crypto. As if – she will dutifully return posting crypto thirst traps once Bitcoin is back trending up and to the right in short order. Irene Zhao The price action played out as I expected. US tax season, consternation over what the Fed will do, the Bitcoin halving sell the news event, and a slowdown of US Bitcoin ETF asset under management (AUM) growth coalesced over the prior fortnight to produce a well-needed market cleansing. The tourists will sit out the next phase on the beach… if they can afford it. Us hard motherfuckers will hodl, and if possible, accumulate more of our favorite crypto reserve assets such as Bitcoin and Ether, and/or high-beta shitcoins like Solana, Dog Wif Hat, and dare I say Dogecoin (the OG doggie coin). This is not meant to be a fully fleshed-out global macroeconomics, politics, and crypto essay. Rather, I want to highlight why the US Treasury, Federal Reserve (Fed), and the Republic First Bank bailout provide fiat liquidity or a path to increased fiat liquidity now and into the near future. I will quickly step through a few tables that underpin my boooolishness. QT Taper = QE When the average muppet equates quantitative easing (QE) with printing money and inflation, it spells trouble for the elites. Therefore they need to change up the nomenclature and method of providing the junkie, that is, the fiat financial system, its hit of monetary heroin. Reducing the pace of asset runoff from the Fed’s balance in accordance with their quantitative tightening (QT) program sounds benign. But make no mistake – by reducing the rate of QT from $95 billion to $60 billion per month, the Fed is essentially adding $35 billion per month of dollar liquidity. When you combine the Interest on Reserve Balances, RRP payments, and interest payments on US Treasury debt, the reduction in QT increases the amount of stimulus provided to the global asset markets each month. The Fed announced this week that it would taper QT by the aforementioned amounts at its May 2024 meeting. Using a handy chart, let us examine the dollar liquidity situation before and after the meeting. Before Size of Facility Rate PA Monthly Amount (USD Bn) Interest Expense NA NA $88.27 IORB 3271.706 5.40% $14.72 RRP 438.148 5.30% $1.94 QT NA NA -$77.59 Total $27.34 Note that the QT line item is the actual monthly average taper amount in 2024 based on the . As you can see, the Fed has undershot the monthly target of $95 billion. It begs the question of whether the target is $60 billion per month and whether the Fed will undershoot that as well. Undershooting the target pace is positive for dollar liquidity. Fed’s weekly reported balance sheet After Size of Facility Rate PA Monthly Amount (USD Bn) Interest Expense NA NA $88.27 IORB 3271.706 5.40% $14.72 RRP 438.148 5.30% $1.94 QT NA NA -$60.00 Total $44.93 Increase in $ Liq per Month 64.32% “High” interest rates, which require the Fed and US Treasury to hand out interest payment stimmies to rich folks, coupled with a reduction in the pace of QT, are even more stimulative. Please read “ ” to understand what each line item means. Kite or Board That’s what beta cuck towel boy Powell is up to. What about his dominatrix, Bad Gurl Yellen? US Treasury Quarterly Refunding Announcement (QRA) Because the US is in the throes of fiscal dominance, Bad Gurl Yellen's pronouncements are more important than those of any other monetary official. Every quarter, the US Treasury publishes the QRA to guide the market as to the quantity and type of debt that must be issued to fund the government. Before the 2Q24 QRA, I had questions: Would Yellen be borrowing more or less than last quarter, and why? What is the maturity profile of the debt to be issued? What would be the target Treasury General Account (TGA) balance? Question 1: During the April – June 2024 quarter, Treasury expects to borrow $243 billion in privately-held net marketable debt, assuming an end-of-June cash balance of $750 billion. The borrowing estimate is $41 billion higher than announced in January 2024, largely due to lower cash receipts, partially offset by a higher beginning of-quarter cash balance. [2] [3] Source: QRA That’s no bueno if you are a holder of Treasuries. There will be more supply, and tax receipts were underwhelming despite the roaring US economy and stonk markets. This hastens the pace at which the bond market throws a fit and ratchets long-end rates markedly higher. Yellen’s response to that will be some form of yield curve control, and that’s when Bitcoin begins its ascent for realz to $1 million. Question 2: Given current fiscal forecasts, the Treasury Department expects to increase the 4-, 6-, and 8-week bill auction sizes in the coming days to ensure sufficient liquidity to meet our one-week cash needs around the end of May.  Then, in anticipation of the June 15th non-withheld and corporate tax date, Treasury expects to implement modest reductions to short-dated bill auction sizes during early to mid-June.  Subsequently, throughout July, the Treasury Department anticipates returning short-dated bill auction sizes to levels at or near the highs from February and March. Source: QRA Yellen needs to increase the issuance of short-dated bills because the market couldn’t handle it if she swung her BSD at the long end of the curve. The added benefit of issuing more bills is that it drains the RRP, which adds dollar liquidity to the system. To understand why, please read “ ”. Bad Gurl Question 3: During the July – September 2024 quarter, Treasury expects to borrow $847 billion in privately-held net marketable debt, assuming an end-of-September cash balance of $850 billion. Source: QRA The TGA balance target is $850 billion. It currently stands at $941 billion, which equates to a roughly $90 billion reduction over the next three months. To understand why, please read “ .” Bad Gurl The impact of this QRA is mildly dollar liquidity positive. It is not a bombshell like the November 2023 announcement, which sent bond, stonk, and crypto prices flying. But it will help pump our bags slowly over time. Republic First Bank Have you ever heard of this tiny, piece-of-shit bank? I hadn’t, until they went bust. The fact that another non-Too Big To Fail (TBTF) bank failed is not noteworthy. But what is important is the response of the monetary mandarins in charge of the Pax Americana. The US government (via the FDIC) insures deposits in any US bank up to $250,000. When a bank fails, uninsured depositors should get zeroed. However, that is politically unpalatable in an election year, especially if the powers that be have continuously assured the public the banking system is sound. Here is an excerpt from the : FDIC As of January 31, 2024, Republic Bank had approximately $6 billion in total assets and $4 billion in total deposits.  The FDIC estimates that the cost to the Deposit Insurance Fund (DIF) related to the failure of Republic Bank will be $667 million. The FDIC determined that compared to other alternatives, Fulton Bank’s acquisition of Republic Bank is the least costly resolution for the DIF, an insurance fund created by Congress in 1933 and managed by the FDIC to protect the deposits at the nation’s banks. Understanding what happened in plain speech requires reading between the lines. Fulton agreed to purchase Republic First and ensure all depositors were made whole only if the FDIC ponied up some cash. The FDIC insurance gave Fulton $667 million so that all Republic First depositors were made whole. Why is the insurance fund being used for all deposits when some deposits were not insured? The reason is that if all deposits weren’t covered, then a bank run would start. Any large depositor with a non-TBTF bank would instantly transfer money to a TBTF bank, which sport a full government guarantee of all deposits. Subsequently, thousands of banks would go under across the country. That is not a good look in a democratic republic with elections every two years. Once the public is educated that the bank failures are 100% due to Fed and US Treasury policy, some overpaid idiots will have to get real jobs. Rather than suffer at the ballot box, those in charge essentially now guarantee all deposits in the US banking system. That is a stealth addition of $6.7 trillion, as this is the amount of uninsured deposits as . reported by the St. Louis Fed This leads to money printing because the FDIC’s insurance fund doesn’t have $6.7 trillion. Maybe they need to ask CZ for advice because funds ain’t SAFU. Once the fund is exhausted, the FDIC will borrow money from the Fed, which will print money to satisfy the loan. Like the other stealth money printing policies discussed in this essay, there is no massive liquidity injection today. However, we now know with full confidence that trillions of contingent liabilities have been added to the Fed’s balance sheet, which will be funded with printed money. Buy in May, Go Away! The slow addition of billions of dollars of liquidity each month will dampen negative price movement from here on out. While I don’t expect crypto to fully realize the recent US monetary announcements' inflationary nature immediately, I expect prices to bottom, chop, and begin a slow grind higher. As the northern-hemispheric summer kicks into full gear, crypto muppets will be out enjoying being pre-rich at all the usual hot spots. I see you at DC10, Scorpios, Chiltern … I see you playa. I certainly won’t be babysitting Bitcoin when I could be two-stepping. The recent intense puke out provides an excellent opportunity to unstake my and spend synthetic dollars on high beta shitcoins. USDe I’m buying Solana and doggie coins for momentum trading positions. For longer-term shitcoin positions, I’m upping my allocations in Pendle* and will identify other tokens that are “on sale.” I will use the rest of May to increase my exposure. And then it’s time to set it, forget it, and wait for the market to appreciate the inflationary nature of the recent US monetary policy announcements. 同志们好 China is not to be forgotten. The CNY will be devalued, but it won’t be against USD; rather, it will be against gold and Bitcoin. I will explain this theory in an in-depth essay later this summer. For those of you who need bullet points on my predictions, here you go: Yes Did Bitcoin hit a local low at around $58,600 earlier this week? A rally to above $60,000 and then range-bound price action between $60,000 and $70,000 until August. What is your price prediction? Yes. Are the recent Fed and Treasury policy announcements stealth forms of money printing? Yachtzee!! Disclaimer: I am an advisor to and investor in Pendle, and have received Pendle tokens as compensation for my advisory services.

Mayday

মে দিবস

Maifeiertag

May Day

Au secours

मई दिवस

メーデー

노동절

Socorro

Первое мая

Mayıs günü

ngày tháng năm

劳动节

Authors:
(1) Yelena Mejova, ISI Foundation, Italy;
(2) Arthur Capozzi, Università degli Studi di Torino, Italy;
(3) Corrado Monti, CENTAI, Italy;
(4) Gianmarco De Francisci Morales, CENTAI, Italy. Table of Links Introduction Background and Related Work Methods Results Discussion and References 3 METHODS 3.1 Data collection To examine the use of memetic engagement by Ukraine during the first 8 months of the 2022 conflict, we begin with a large collection of tweets matching war-related keywords (see Appendix A) spanning from 27 February 2022 to 12 October 2022. By examining this collection, we identify three prominent accounts: (1) @Ukraine: self-described as “the official Twitter account of Ukraine”, this verified account is labeled as an “Ukraine government organization” by Twitter. It posts war-related and political updates about Ukraine. (2) @uamemesforces: self-described as the “Source of the best Ukrainian memes”, this unverified account states as its goal ”to convey the truth about what is happening now in Ukraine with the help of memes”.[8] (3) @DefenceU: or “Defense of Ukraine”, is the “Official page of the Ministry of Defense of Ukraine”. Like @Ukraine, this verified account is described by Twitter as a “Ukraine government organization”. Its posts concern battlefield updates and news. We select the accounts @Ukraine and @DefenceU because of their large following (at the end of the collection period), because they are official accounts not associated with individuals or specific media outlets, and because of the focus on the conflict (many popular Ukrainian accounts are by non-relevant celebrities). Additionally, we select @uamemesforces, the most popular Ukrainian unofficial account that posts memes about the conflict. Note that we are limited in the number of accounts we can study due to the labor-intensive labeling process. We retrieve all of the posts for these accounts via Twitter Timeline API. We treat each post with media content as a potential meme, no matter the format. To ensure preserving content posted by these accounts, we also download the images associated with the posts and the users who retweeted or liked the post (see Table 1 for summary statistics). We associate each user with a country in the GeoNames database via their location field (a free-text field optionally filled by the user). Considering only tweets with media, out of 586 042 users, 44% are successfully mapped to a country. We retain only countries with at least 1000 geolocated retweeting users for geographic analysis. In order to explore the relationship between Ukrainian memes and each country’s actions towards the conflict, we use the Ukraine Support Tracker data from the Kiel Institute for the World Economy (downloaded on 20 June 2023), which “quantifies military, financial, and humanitarian aid transferred by governments to Ukraine since the end of diplomatic relations between Russia and Ukraine on January 24, 2022”.[9] The dataset provides a quantitative measure of a country’s aid to Ukraine. Finally, to examine the attitude of each country’s population towards Ukraine, we use the Eurobarometer STD98 survey which took place in winter 2022-2023 in 30 European countries [22]. Specifically, we use use QE2, answer 6: “To what extent do you agree or disagree with: providing financial support to Ukraine.” 3.2 Annotation To understand the visual and narrative attributes of the captured data, all authors perform a manual annotation of a random selection of the tweets. We use all of @Ukraine tweets and a random sample of roughly 40% of those from the other two accounts are selected for analysis, for a total of 1063 tweets (last column of Table 1). We remove self-replies (an account replying to its own post) which have no retweets of their own. Guided by previous literature, we compose a codebook to capture visual and narrative aspects of the content. First, we adopt the significant features of the content characterization framework proposed by Ling et al. [41], which is used to model the virality of political memes. These attributes include: (1) Number of panels: multiple or single; (2) Type of image: photo, screenshot, or illustration; (3) Scale: close-up, medium shot, long shot, other; (4) Type of subject: object, character, scene, creature, text, other; (5) Main attribute of the subject (if character): facial expression, posture, poster (6) Character emotion (if character): positive, negative, or neutral; (7) Contains words: whether image itself (not tweet caption) contains words. Second, we employ the narrative framework by Karpman [35] later extended by de Saint Laurent et al. [18]. It defines two key dimensions to characterize a narrative: moral quality (benevolent or malevolent) and power (strong or weak), thus resulting in four character archetypes: heroes (benevolent, strong), victims (benevolent, weak), villains (malevolent, strong), and fools (malevolent, weak). We also label the actors mentioned in these narratives (as an open category, guided by an initial set of main actors in the conflict, such as Zelensky and Putin). We further contextualize the narratives by their emotional appeal [8], in particular humor, pride, fear, outrage, and compassion (the label was originally open, but the labelers coalesced on this set). Finally, we record whether there is a specific intent in terms of a call to action or information sharing. Note that, as much as possible, we try to label the narratives as intended from the point of view of the posting account (although some biases may creep in, as we discuss in Section 5). Appendix B reports the full codebook. Using this literature-driven codebook, all authors of this manuscript perform a deductive coding of the selected posts (for more on such process see Linneberg and Korsgaard [42]). We rely on Google Translate to translate non-English language content. All coders first annotated a selection of 50 posts, discussed insights and disagreements, and came up with standard labels for emotional appeal, intent, and actor (though the label remained open). Subsequently, the rest of the posts are annotated by one author each, with uncertain cases discussed collectively. Appendix C reports the inter-annotator agreement on a selection of 48 posts (sampled from each account). On average, the agreement is 0.750 for @Ukraine, 0.604 for @uamemesforces, and 0.667 for @DefenceU (with a Krippendorff’s alpha of around 0.65 for all accounts and tasks), which is high considering the subjective nature of the tasks, and that some features were open and could have multiple labels. The annotated dataset is available at https://github.com/corradomonti/ukranian-tweets. 3.3 Popularity model Using the data collected and annotated as per the previous sections, we construct models to find whether there are trends in (𝑖) what kind of content becomes more popular, and (𝑖𝑖) who it reaches. Specifically, we use linear regression to model the log-transformed number of retweets with the content variables outlined above as independent variables. As the three accounts have posted a vastly different number of tweets, we weigh the data points (tweets) inverseproportionally to their representation in the dataset, so that each account weighs equally on the outcomes. We build several versions of the model to ascertain the benefit of each class of variables: visual [41], actor, emotion, intent, and narrative. A baseline model simply uses the identity of the author (i.e., group fixed effects), the number of followers of that account (log transformed) as a proxy for the potential audience, and the number of days from the invasion to account for changing interest in the topic over time. We then choose the best model by the Bayesian Information Criterion (BIC)—a model selection metric that captures both unexplained variation in the dependent variable and the number of explanatory variables. The coefficients of this model reveal the associations between these variables and the virality of the content. Next, we group retweets by the country of the user (those who can be geolocated) to understand the preferences of users who spread these messages. We enrich our characterization of the relationships between each country and Ukraine by considering the amount of financial assistance the country provides to Ukraine (normalized by the country’s GDP) or by their opinion on this topic (via the Eurobarometer survey). As mentioned above, we consider these measures as different proxies of support for Ukraine’s military efforts. First, we examine the retweeting rate (normalized by population). Then, we compare the narrative preferences to the amount of support for Ukraine: are there narratives associated with increased support for Ukraine? We operationalize this preference as the log odds of users from a country retweeting content having a specific narrative. Finally, we examine the geographical distribution of the retweeting rate and narrative preferences across Europe by plotting these on choropleth maps. [8] https://www.patreon.com/uamemesforces [9] https://www.ifw-kiel.de/topics/war-against-ukraine/ukraine-support-tracker This paper is available on arxiv under CC 4.0 license. Authors: (1) Yelena Mejova, ISI Foundation, Italy; (2) Arthur Capozzi, Università degli Studi di Torino, Italy; (3) Corrado Monti, CENTAI, Italy; (4) Gianmarco De Francisci Morales, CENTAI, Italy. Authors: (1) Yelena Mejova, ISI Foundation, Italy; (2) Arthur Capozzi, Università degli Studi di Torino, Italy; (3) Corrado Monti, CENTAI, Italy; (4) Gianmarco De Francisci Morales, CENTAI, Italy. Table of Links Introduction Introduction Background and Related Work Background and Related Work Methods Methods Results Results Discussion and References Discussion and References 3 METHODS 3.1 Data collection To examine the use of memetic engagement by Ukraine during the first 8 months of the 2022 conflict, we begin with a large collection of tweets matching war-related keywords (see Appendix A) spanning from 27 February 2022 to 12 October 2022. By examining this collection, we identify three prominent accounts: (1) @Ukraine: self-described as “the official Twitter account of Ukraine”, this verified account is labeled as an “Ukraine government organization” by Twitter. It posts war-related and political updates about Ukraine. @Ukraine: (2) @uamemesforces: self-described as the “Source of the best Ukrainian memes”, this unverified account states as its goal ”to convey the truth about what is happening now in Ukraine with the help of memes”.[8] @uamemesforces: (3) @DefenceU: or “Defense of Ukraine”, is the “Official page of the Ministry of Defense of Ukraine”. Like @Ukraine, this verified account is described by Twitter as a “Ukraine government organization”. Its posts concern battlefield updates and news. @DefenceU: We select the accounts @Ukraine and @DefenceU because of their large following (at the end of the collection period), because they are official accounts not associated with individuals or specific media outlets, and because of the focus on the conflict (many popular Ukrainian accounts are by non-relevant celebrities). Additionally, we select @uamemesforces, the most popular Ukrainian unofficial account that posts memes about the conflict. Note that we are limited in the number of accounts we can study due to the labor-intensive labeling process. We retrieve all of the posts for these accounts via Twitter Timeline API. We treat each post with media content as a potential meme, no matter the format. To ensure preserving content posted by these accounts, we also download the images associated with the posts and the users who retweeted or liked the post (see Table 1 for summary statistics). We associate each user with a country in the GeoNames database via their location field (a free-text field optionally filled by the user). Considering only tweets with media, out of 586 042 users, 44% are successfully mapped to a country. We retain only countries with at least 1000 geolocated retweeting users for geographic analysis. In order to explore the relationship between Ukrainian memes and each country’s actions towards the conflict, we use the Ukraine Support Tracker data from the Kiel Institute for the World Economy (downloaded on 20 June 2023), which “quantifies military, financial, and humanitarian aid transferred by governments to Ukraine since the end of diplomatic relations between Russia and Ukraine on January 24, 2022”.[9] The dataset provides a quantitative measure of a country’s aid to Ukraine. Finally, to examine the attitude of each country’s population towards Ukraine, we use the Eurobarometer STD98 survey which took place in winter 2022-2023 in 30 European countries [22]. Specifically, we use use QE2, answer 6: “To what extent do you agree or disagree with: providing financial support to Ukraine.” 3.2 Annotation To understand the visual and narrative attributes of the captured data, all authors perform a manual annotation of a random selection of the tweets. We use all of @Ukraine tweets and a random sample of roughly 40% of those from the other two accounts are selected for analysis, for a total of 1063 tweets (last column of Table 1). We remove self-replies (an account replying to its own post) which have no retweets of their own. Guided by previous literature, we compose a codebook to capture visual and narrative aspects of the content. First, we adopt the significant features of the content characterization framework proposed by Ling et al. [41], which is used to model the virality of political memes. These attributes include: (1) Number of panels: multiple or single; (2) Type of image: photo, screenshot, or illustration; (3) Scale: close-up, medium shot, long shot, other; (4) Type of subject: object, character, scene, creature, text, other; (5) Main attribute of the subject (if character): facial expression, posture, poster (6) Character emotion (if character): positive, negative, or neutral; (7) Contains words: whether image itself (not tweet caption) contains words. Second, we employ the narrative framework by Karpman [35] later extended by de Saint Laurent et al. [18]. It defines two key dimensions to characterize a narrative: moral quality (benevolent or malevolent) and power (strong or weak), thus resulting in four character archetypes: heroes (benevolent, strong), victims (benevolent, weak), villains (malevolent, strong), and fools (malevolent, weak). We also label the actors mentioned in these narratives (as an open category, guided by an initial set of main actors in the conflict, such as Zelensky and Putin). We further contextualize the narratives by their emotional appeal [8], in particular humor, pride, fear, outrage, and compassion (the label was originally open, but the labelers coalesced on this set). Finally, we record whether there is a specific intent in terms of a call to action or information sharing. Note that, as much as possible, we try to label the narratives as intended from the point of view of the posting account (although some biases may creep in, as we discuss in Section 5). Appendix B reports the full codebook. Using this literature-driven codebook, all authors of this manuscript perform a deductive coding of the selected posts (for more on such process see Linneberg and Korsgaard [42]). We rely on Google Translate to translate non-English language content. All coders first annotated a selection of 50 posts, discussed insights and disagreements, and came up with standard labels for emotional appeal, intent, and actor (though the label remained open). Subsequently, the rest of the posts are annotated by one author each, with uncertain cases discussed collectively. Appendix C reports the inter-annotator agreement on a selection of 48 posts (sampled from each account). On average, the agreement is 0.750 for @Ukraine, 0.604 for @uamemesforces, and 0.667 for @DefenceU (with a Krippendorff’s alpha of around 0.65 for all accounts and tasks), which is high considering the subjective nature of the tasks, and that some features were open and could have multiple labels. The annotated dataset is available at https://github.com/corradomonti/ukranian-tweets. 3.3 Popularity model Using the data collected and annotated as per the previous sections, we construct models to find whether there are trends in (𝑖) what kind of content becomes more popular, and (𝑖𝑖) who it reaches. Specifically, we use linear regression to model the log-transformed number of retweets with the content variables outlined above as independent variables. As the three accounts have posted a vastly different number of tweets, we weigh the data points (tweets) inverseproportionally to their representation in the dataset, so that each account weighs equally on the outcomes. We build several versions of the model to ascertain the benefit of each class of variables: visual [41], actor, emotion, intent, and narrative. A baseline model simply uses the identity of the author (i.e., group fixed effects), the number of followers of that account (log transformed) as a proxy for the potential audience, and the number of days from the invasion to account for changing interest in the topic over time. We then choose the best model by the Bayesian Information Criterion (BIC)—a model selection metric that captures both unexplained variation in the dependent variable and the number of explanatory variables. The coefficients of this model reveal the associations between these variables and the virality of the content. Next, we group retweets by the country of the user (those who can be geolocated) to understand the preferences of users who spread these messages. We enrich our characterization of the relationships between each country and Ukraine by considering the amount of financial assistance the country provides to Ukraine (normalized by the country’s GDP) or by their opinion on this topic (via the Eurobarometer survey). As mentioned above, we consider these measures as different proxies of support for Ukraine’s military efforts. First, we examine the retweeting rate (normalized by population). Then, we compare the narrative preferences to the amount of support for Ukraine: are there narratives associated with increased support for Ukraine? We operationalize this preference as the log odds of users from a country retweeting content having a specific narrative. Finally, we examine the geographical distribution of the retweeting rate and narrative preferences across Europe by plotting these on choropleth maps. [8] https://www.patreon.com/uamemesforces [9] https://www.ifw-kiel.de/topics/war-against-ukraine/ukraine-support-tracker This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Understanding Memetic Warfare Tactics: Social Media Tactics in the Russo-Ukrainian War

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