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Stablecoin Economy: A Brief History of the Top Ten Stablecoins 

This paper is available on arxiv under CC 4.0 license. Authors: (1) Pietro Ghezzi, Brighton & Sussex Medical School, Falmer, Brighton, UK; (2) Peter G Bannister, Brighton & Sussex Medical School, Falmer, Brighton, UK; (3) Gonzalo Casino, Communication Department, Pompeu Fabra University, Barcelona, Spain and Iberoamerican Cochrane Center, Barcelona, Spain; (4) Alessia Catalani, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (5) Michel Goldman, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (6) Jessica Morley, Oxford Internet Institute, University of Oxford, Oxford, UK; (7) Marie Neunez, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (8) Andreu Prados, Communication Department, Pompeu Fabra University, Barcelona, Spain, Iberoamerican Cochrane Center, Barcelona, Spain, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles, Oxford Internet Institute, University of Oxford, Oxford, UK, and Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain; (9) Mariarosaria Taddeo, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK; (10) Tania Vanzolini, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (11) Luciano Floridi, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK. Table of Links Abstract and Introduction Methods Web search and content analysis Results Discussion Conclusion and References Table 1. Example of classification of webpages Table 2. Percentage of vaccine-negative webpages in different SERPs Table 2. Percentage of vaccine-negative webpages in different SERPs Total of 30 webpages for each SERP. Cells are colour coded: median (20%), yellow; maximum (53%), red; minimum (0%), green. *Significantly different from Google.com by two-tailed Fisher’s test corrected for multiple comparison using the method of Benjamini, Krieger and Yekutieli at a false discovery rate of 5%. #significantly different from the localized version of Google.

Online Information of Vaccines: Table 2. Percentage of Vaccine-negative Webpages in Different SERPs

This paper is available on arxiv under CC 4.0 license. Authors: (1) Pietro Ghezzi, Brighton & Sussex Medical School, Falmer, Brighton, UK; (2) Peter G Bannister, Brighton & Sussex Medical School, Falmer, Brighton, UK; (3) Gonzalo Casino, Communication Department, Pompeu Fabra University, Barcelona, Spain and Iberoamerican Cochrane Center, Barcelona, Spain; (4) Alessia Catalani, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (5) Michel Goldman, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (6) Jessica Morley, Oxford Internet Institute, University of Oxford, Oxford, UK; (7) Marie Neunez, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (8) Andreu Prados, Communication Department, Pompeu Fabra University, Barcelona, Spain, Iberoamerican Cochrane Center, Barcelona, Spain, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles, Oxford Internet Institute, University of Oxford, Oxford, UK, and Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain; (9) Mariarosaria Taddeo, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK; (10) Tania Vanzolini, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (11) Luciano Floridi, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK. Table of Links Abstract and Introduction Methods Web search and content analysis Results Discussion Conclusion and References Table 1. Example of classification of webpages Table 2. Percentage of vaccine-negative webpages in different SERPs Table 1. Example of classification of webpages Positive stance on vaccines https://www.historyofvaccines.org/content/articles/do-vaccines-cause-autism https://www.nhs.uk/news/medication/no-link-between-mmr-and-autism-major-study-finds/ https://www.nhs.uk/conditions/vaccinations/mmr-vaccine/ https://www.autism.org.uk/get-involved/media-centre/position-statements/mmr-vaccine.aspx https://kidshealth.org/en/parents/autism-studies.html https://vaccine-safety-training.org/mmr-vaccine-increases.html https://www.nejm.org/doi/full/10.1056/NEJMoa021134 https://www.ncbi.nlm.nih.gov/pubmed/30986133 https://www.ncbi.nlm.nih.gov/pubmed/15366972 Negative stance on vaccines https://www.thelancet.com/journals/lancet/article/PIIS0140673605756968/fulltext www.whale.to/vaccine/vaccine_autism_proven.html www.vaccineriskawareness.com/Infant-Vaccines-Produce-Autism-Symptoms-In-Primates https://leftbrainrightbrain.co.uk/2014/07/17/more-of-that-vaccineautism-research-that-doesnt-exist/ edition.cnn.com/2011/HEALTH/01/05/autism.vaccines/index.html https://www.coasttocoastam.com/show/2018/02/21 nocompulsoryvaccination.com/2014/08/22/vaccine-autism-cover-up/ https://www.newswars.com/vaccine-autism-questioned-by-doctor-congressman-elect/ vaxtruth.org/2011/08/vaccines-do-not-cause-autism/ Neutral stance on vaccines https://www.physicsforums.com/threads/vaccine-and-autism-link.880852/ www.discovermagazine.com/2009/jun/06-why-does-vaccine-autism-controversy-live-on https://www.ecso.org/news/autism-charity-founder-anti-vaccination-campaigner/ https://www.theguardian.com/society/2019/jun/01/professor-who-links-vaccines-to-autismfunded-through-university-portal https://www.independent.co.uk/news/world/americas/trump-vaccines-autism-links-anti-vaxxerus-president-false-vaccine-a8331836.html https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2376879/

Online Information of Vaccines: Table 1. Example of Classification of Webpages

Let's explore how temporary tables work in MariaDB. First, we have to connect to the server. For example (use your own connection details): mariadb -h 127.0.0.1 -u root -p"RootPassword!" --database demo Now, just to point something out, let's create a standard (permanent) table. Here's how: CREATE TABLE t (
    c INT
); This table, t, will persist in the database even after we exit the client: exit When we reconnect and check the existing tables using SHOW TABLES;, the table t will still be listed: mariadb -h 127.0.0.1 -u root -p"RootPassword!" --database demo SHOW TABLES; +----------------+
| Tables_in_demo |
+----------------+
| t              |
+----------------+ All this is pretty obvious, but now, let's recreate this table and try something different: CREATE OR REPLACE TEMPORARY TABLE t (
    c INT
); Notice the TEMPORARY keyword. After creating this table, if we run SHOW TABLES;, it appears in the list. We can insert data into it, query it, and join it with other tables. It behaves like a normal table during the current session. However, if we exit the client, then reconnect, and perform a SHOW TABLES; again, the temporary table t will not be listed. A temporary table only exists for the duration of the session in which it was created and other sessions won't be able to see it. Use Case for Temporary Tables Temporary tables are quite useful for transient data operations. For instance, consider a table called products in our database: CREATE TABLE products (
  id INT NOT NULL AUTO_INCREMENT,
  code VARCHAR(100) NOT NULL,
  name VARCHAR(250) NOT NULL,
  description TEXT DEFAULT NULL,
  PRIMARY KEY (id),
  UNIQUE KEY code (code)
) We can create a temporary table that mimics the structure of products: CREATE TEMPORARY TABLE t LIKE products; We can confirm this by running: DESCRIBE t; +-------------+--------------+------+-----+---------+----------------+
| Field       | Type         | Null | Key | Default | Extra          |
+-------------+--------------+------+-----+---------+----------------+
| id          | int(11)      | NO   | PRI | NULL    | auto_increment |
| code        | varchar(100) | NO   | UNI | NULL    |                |
| name        | varchar(250) | NO   |     | NULL    |                |
| description | text         | YES  |     | NULL    |                |
+-------------+--------------+------+-----+---------+----------------+ Initially, t will be empty. However, suppose we want to transfer some data from products to t. Let’s assume we only want to include products that contain the number 0 in their code: INSERT INTO t
SELECT * FROM products
WHERE code LIKE '%0%'; After running this command, if we query the temporary table t: SELECT * FROM t; +----+--------+------------------+---------------------------------------------------+
| id | code   | name             | description                                       |
+----+--------+------------------+---------------------------------------------------+
|  1 | BG2024 | BugBlaster       | Eradicates software bugs with a single scan.      |
|  3 | FW001  | FireWhale        | An oversized, comprehensive firewall solution.    |
|  4 | CLD404 | CloudNine Finder | Find your way back from cloud outages and errors. |
+----+--------+------------------+---------------------------------------------------+ We see the filtered data. Conclusion Temporary tables offer a powerful way to handle data for temporary processing without affecting the persistent data store. They are particularly useful in scenarios where data needs to be manipulated or transformed temporarily. You can use permanent tables for this kind of data manipulation but temporary tables are useful when you need automatic cleanup, reduced risk of naming conflicts, isolation and security, and resource management for query performance. Let's explore how temporary tables work in MariaDB. First, we have to connect to the server. For example (use your own connection details): temporary tables mariadb -h 127.0.0.1 -u root -p"RootPassword!" --database demo mariadb -h 127.0.0.1 -u root -p"RootPassword!" --database demo Now, just to point something out, let's create a standard (permanent) table. Here's how: CREATE TABLE t (
    c INT
); CREATE TABLE t (
    c INT
); This table, t , will persist in the database even after we exit the client: t exit exit When we reconnect and check the existing tables using SHOW TABLES; , the table t will still be listed: SHOW TABLES; t mariadb -h 127.0.0.1 -u root -p"RootPassword!" --database demo mariadb -h 127.0.0.1 -u root -p"RootPassword!" --database demo SHOW TABLES; SHOW TABLES; +----------------+
| Tables_in_demo |
+----------------+
| t              |
+----------------+ +----------------+
| Tables_in_demo |
+----------------+
| t              |
+----------------+ All this is pretty obvious, but now, let's recreate this table and try something different: CREATE OR REPLACE TEMPORARY TABLE t (
    c INT
); CREATE OR REPLACE TEMPORARY TABLE t (
    c INT
); Notice the TEMPORARY keyword. After creating this table, if we run SHOW TABLES; , it appears in the list. We can insert data into it, query it, and join it with other tables. It behaves like a normal table during the current session. However, if we exit the client, then reconnect, and perform a SHOW TABLES; again, the temporary table t will not be listed. TEMPORARY SHOW TABLES; SHOW TABLES; t A temporary table only exists for the duration of the session in which it was created and other sessions won't be able to see it. Use Case for Temporary Tables Temporary tables are quite useful for transient data operations. For instance, consider a table called products in our database: products CREATE TABLE products (
  id INT NOT NULL AUTO_INCREMENT,
  code VARCHAR(100) NOT NULL,
  name VARCHAR(250) NOT NULL,
  description TEXT DEFAULT NULL,
  PRIMARY KEY (id),
  UNIQUE KEY code (code)
) CREATE TABLE products (
  id INT NOT NULL AUTO_INCREMENT,
  code VARCHAR(100) NOT NULL,
  name VARCHAR(250) NOT NULL,
  description TEXT DEFAULT NULL,
  PRIMARY KEY (id),
  UNIQUE KEY code (code)
) We can create a temporary table that mimics the structure of products : products CREATE TEMPORARY TABLE t LIKE products; CREATE TEMPORARY TABLE t LIKE products; We can confirm this by running: DESCRIBE t; DESCRIBE t; +-------------+--------------+------+-----+---------+----------------+
| Field       | Type         | Null | Key | Default | Extra          |
+-------------+--------------+------+-----+---------+----------------+
| id          | int(11)      | NO   | PRI | NULL    | auto_increment |
| code        | varchar(100) | NO   | UNI | NULL    |                |
| name        | varchar(250) | NO   |     | NULL    |                |
| description | text         | YES  |     | NULL    |                |
+-------------+--------------+------+-----+---------+----------------+ +-------------+--------------+------+-----+---------+----------------+
| Field       | Type         | Null | Key | Default | Extra          |
+-------------+--------------+------+-----+---------+----------------+
| id          | int(11)      | NO   | PRI | NULL    | auto_increment |
| code        | varchar(100) | NO   | UNI | NULL    |                |
| name        | varchar(250) | NO   |     | NULL    |                |
| description | text         | YES  |     | NULL    |                |
+-------------+--------------+------+-----+---------+----------------+ Initially, t will be empty. However, suppose we want to transfer some data from products to t . Let’s assume we only want to include products that contain the number 0 in their code: t products t INSERT INTO t
SELECT * FROM products
WHERE code LIKE '%0%'; INSERT INTO t
SELECT * FROM products
WHERE code LIKE '%0%'; After running this command, if we query the temporary table t : t SELECT * FROM t; SELECT * FROM t; +----+--------+------------------+---------------------------------------------------+
| id | code   | name             | description                                       |
+----+--------+------------------+---------------------------------------------------+
|  1 | BG2024 | BugBlaster       | Eradicates software bugs with a single scan.      |
|  3 | FW001  | FireWhale        | An oversized, comprehensive firewall solution.    |
|  4 | CLD404 | CloudNine Finder | Find your way back from cloud outages and errors. |
+----+--------+------------------+---------------------------------------------------+ +----+--------+------------------+---------------------------------------------------+
| id | code   | name             | description                                       |
+----+--------+------------------+---------------------------------------------------+
|  1 | BG2024 | BugBlaster       | Eradicates software bugs with a single scan.      |
|  3 | FW001  | FireWhale        | An oversized, comprehensive firewall solution.    |
|  4 | CLD404 | CloudNine Finder | Find your way back from cloud outages and errors. |
+----+--------+------------------+---------------------------------------------------+ We see the filtered data. Conclusion Temporary tables offer a powerful way to handle data for temporary processing without affecting the persistent data store. They are particularly useful in scenarios where data needs to be manipulated or transformed temporarily. You can use permanent tables for this kind of data manipulation but temporary tables are useful when you need automatic cleanup, reduced risk of naming conflicts, isolation and security, and resource management for query performance.

How to Use Temporary Tables in MariaDB

মারিয়াডিবিতে অস্থায়ী টেবিলগুলি কীভাবে ব্যবহার করবেন

So verwenden Sie temporäre Tabellen in MariaDB

Cómo utilizar tablas temporales en MariaDB

Comment utiliser des tables temporaires dans MariaDB

मारियाडीबी में अस्थायी तालिकाओं का उपयोग कैसे करें

MariaDB で一時テーブルを使用する方法

MariaDB에서 임시 테이블을 사용하는 방법

Como usar tabelas temporárias no MariaDB

Как использовать временные таблицы в MariaDB

MariaDB'de Geçici Tablolar Nasıl Kullanılır?

Cách sử dụng bảng tạm thời trong MariaDB

如何在 MariaDB 中使用临时表

Why I Switched to Table Driven Testing approach in Go

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com;
(2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com;
(3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. Table of Links Abstract and Introduction
Problem Setup and Related Work
HyperTransformer
Experiments
Conclusion and References
A Example of a Self-Attention Mechanism For Supervised Learning
B Model Parameters
C Additional Supervised Experiments
D Dependence On Parameters and Ablation Studies
E Attention Maps of Learned Transformer Models
F Visualization of The Generated CNN Weights
G Additional Tables and Figures G ADDITIONAL TABLES AND FIGURES This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com; (2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com; (3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com; (2) Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com; (3) Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com. Table of Links Abstract and Introduction Problem Setup and Related Work HyperTransformer Experiments Conclusion and References A Example of a Self-Attention Mechanism For Supervised Learning B Model Parameters C Additional Supervised Experiments D Dependence On Parameters and Ablation Studies E Attention Maps of Learned Transformer Models F Visualization of The Generated CNN Weights G Additional Tables and Figures Abstract and Introduction Abstract and Introduction Problem Setup and Related Work Problem Setup and Related Work HyperTransformer HyperTransformer Experiments Experiments Conclusion and References Conclusion and References A Example of a Self-Attention Mechanism For Supervised Learning A Example of a Self-Attention Mechanism For Supervised Learning B Model Parameters B Model Parameters C Additional Supervised Experiments C Additional Supervised Experiments D Dependence On Parameters and Ablation Studies D Dependence On Parameters and Ablation Studies E Attention Maps of Learned Transformer Models E Attention Maps of Learned Transformer Models F Visualization of The Generated CNN Weights F Visualization of The Generated CNN Weights G Additional Tables and Figures G Additional Tables and Figures G ADDITIONAL TABLES AND FIGURES

HyperTransformer: G Additional Tables and Figures

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 E Contingency Table: Baseline vs. Text-STILT Table 8 shows the contingency table – as one would prepare for a McNemar’s Test between two classifiers (McNemar, 1947) – between the model trained with Text-STILT on 60% Memes and Baseline trained on 100% Memes available which had the most similar performance. While the two models performed similarly in terms of Weighted F1- scores, Text-STILT correctly classified a notable number of memes that Baseline did not and vice versa. Examples of such memes are discussed in Section 4.1. Furthermore, approximately 40% of memes in the testing set were incorrectly classified by both models. This suggests that these memes convey sentiment in a way that cannot be reliably predicted by either approach. This paper is under CC 4.0 license. available on arxiv

Contingency Table: Baseline vs. Text-STILT

How to Union Two Tables in Power BI: A Comprehensive Guide Combining data from multiple tables is a common requirement in Power BI to create unified datasets for analysis and visualization. There are two primary methods to achieve this task in Power BI: using the DAX UNION function and using the Append Queries feature in Power Query. In this guide, we will walk you through the step-by-step process of both methods. Method 1: Using the DAX UNION Function The DAX UNION function allows you to combine two or more tables with identical columns into a single table. Here's how you can use the UNION function in Power BI: Launch Power BI Desktop and load the tables that you want to union. Go to the Modeling tab on the Power BI ribbon. Click on New Table to create a new DAX table. Enter the following DAX formula to union two tables, for example: Table = UNION('Query1','Query2') Replace 'Query1' and 'Query2' with the actual table names you want to union. Press Enter to create a new table with the combined data from both tables. Method 2: Using Append Queries in Power Query Appending queries in Power Query allows you to combine tables by stacking one on top of the other. Here's a step-by-step guide to using the Append Queries feature: Load the tables into Power Query by selecting the table and clicking on the Transform Data option. In the Power Query Editor, select the first table you want to append. Go to the Home tab on the Power Query ribbon. Click on Append Queries and select Append Queries as New. Choose the second table you want to append in the dialog box that appears. Configure the append operation by selecting the appropriate options (e.g., append queries with matching columns). Click OK to append the tables. Once appended, you can further transform the data or load it into Power BI for analysis. Conclusion By following these two methods, you can effectively union two tables in Power BI using either DAX or Power Query. Choose the method that best suits your data structure and analysis requirements to integrate and consolidate your datasets seamlessly. Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on or (@Luca_DataTeam). You can also catch glimpses of my personal life on , Happy exploring!👋 LinkedIn X Instagram

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