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Draft:MemeGen
AFC submission
MemeGen (sometimes stylised Memegen) is a name shared by three unrelated meme-generation tools:
Draft:MemeGen
Google internal tool
Google internal tool Launched in 2010 by two Google engineers, Google’s private Memegen is an intranet message board that lets employees create image macros and vote on each other’s jokes. The board has become a barometer of employee sentiment; popular posts often critique corporate policy, leadership decisions and product launches. In 2024 Google removed the “thumbs-down” reaction and view-count metrics to reduce perceived negativity.
Draft:MemeGen
Open-source MemeGen API (memegen.link)
Open-source MemeGen API (memegen.link) Memegen.link is a free, stateless REST API written in Python that renders meme images entirely from URL parameters. Users choose from hundreds of templates or supply custom images; the service outputs PNG, JPEG, GIF, or WebP and is widely embedded in Slack and Discord bots.
Draft:MemeGen
MemeGen AI
MemeGen AI MemeGen AI (meme-gen.ai) is a public web application, introduced in 2024, that applies generative-AI models to turn uploaded photos into short animated GIF or video memes with preset effects. The service is free to use, watermark-free, and offers one-click templates such as “Face Slap” and “Power Blast” for rapid meme creation.
Draft:MemeGen
See also
See also Imgflip Meme Generator
Draft:MemeGen
References
References
Draft:MemeGen
Table of Content
AFC submission, Google internal tool, Open-source MemeGen API (memegen.link), MemeGen AI, See also, References
Ford government (Ontario)
#
redirect Ford ministry
Ford government (Ontario)
Table of Content
#
Category:September 2008 sports events in Italy
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Sports
Category:September 2008 sports events in Italy
Table of Content
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Category:September 2009 sports events in Italy
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Sports
Category:September 2009 sports events in Italy
Table of Content
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Valery Gabisoniya
Short description
Valery Borisovich Gabisoniya (; born May 29, 1959) is a Russian swimming coach, Master of Sports of the USSR, Honored Coach of Russia, and coach of the Russian national swimming team since 2006.
Valery Gabisoniya
Biography
Biography Valery Borisovich Gabisoniya was born in the city of Sukhumi. He began swimming from an early age, learning to stay afloat from the age of three. During his school years, he began training under the guidance of Nikolai Ivanovich Yakhiopulo, the head coach of the Abkhazian swimming team. He was the decisive influence on the choice of his future profession. "I learned to stay afloat from the age of three. And in the second grade, I accidentally ended up with coach Nikolai Ivanovich Yakhiopulo. I was late for the cinema, saw a crowd of guys, and joined them in participating in the selection for the swimming section," Valery Borisovich recalled in an interview for the magazine "Swimming." Gabisoniya is a Master of Sports of the USSR in swimming. From 1987 to 1994, he worked as a children's coach in the swimming pool of the sports club "Izhorets" in the Kolpino district of Saint Petersburg. Since 1994, he has been working as a coach and teacher at the College of Olympic Reserve No. 1 of Saint Petersburg. Over the years of his coaching career, he has trained more than one generation of talented athletes who have achieved high results at the Russian and international levels. His students have become medalists at the Olympic Games, world champions, European champions, Universiade champions, and Russian champions.
Valery Gabisoniya
Notable Students
Notable Students Vladimir Predkin - Honored Master of Sports of Russia, silver medalist at the 1996 Summer Olympics in the 4×100 m freestyle relay, medalist at the World Championships, European champion. Vyacheslav Prudnikov - Master of Sport of Russia, International Class, medalist at the World Championships, multiple winner and medalist at stages of the FINA Swimming World Cup. Evgeny Ayzetullov - Master of Sport of Russia, International Class, winner and medalist at the European Junior Championships, medalist at stages of the FINA Swimming World Cup and the World Junior Championships. Andrey Kapralov - Honored Master of Sports of Russia, winner and multiple medalist at the World Championships, European Championships, Universiades, and stages of the FINA Swimming World Cup.
Valery Gabisoniya
Achievements and Awards
Achievements and Awards Honored Coach of Russia (2004). Excellent Worker of Physical Culture and Sports. Multiple laureate of the Government of Saint Petersburg Prize.
Valery Gabisoniya
Interesting Facts
Interesting Facts He has been passionate about music since childhood: he has mastered the accordion, guitar, and other instruments. In his youth, he was a member of the rock band "Dangerous Zone," where he performed as a vocalist and lead guitarist. From 1976–1979, he played in the St. Petersburg jazz-rock band "Fram" as a vocalist, guitarist, and songwriter. He still pursues music—he has his own studio where he records his own compositions.
Valery Gabisoniya
References
References Category:Living people Category:1959 births Category:Swimming coaches Category:Russian sportspeople
Valery Gabisoniya
Table of Content
Short description, Biography, Notable Students, Achievements and Awards, Interesting Facts, References
Miss International Queen Vietnam 2025
Infobox beauty pageant
Miss International Queen Vietnam 2025, the 4th Miss International Queen Vietnam pageant, was held on 10 May 2025, in Phan Thiết, at Bikini Beach Square, NovaWorld. Nguyễn Hà Dịu Thảo of Hải Dương crowned her successor, Hà Tâm Như, at the end of the event.
Miss International Queen Vietnam 2025
Final results<ref>{{Cite web
Final results Placement Candidate Miss International Queen Vietnam 2025 Hà Tâm Như 1st runner-up Nguyễn Cao Minh Anh 2nd runner-up Nguyễn Thủy Tiên Top 6 Trần Khởi My Trần Quân Tô Ngọc Bảo LinhTop 14 Lộ Lộ Nikkie Song Phúc Huỳnh My Hoàng Bảo Anh Trần Mẫn Nhi Phạm Thư Kỳ Lê Tuệ Vy Bùi Phạm Phương Nhã
Miss International Queen Vietnam 2025
Special Awards
Special Awards Position ContestantBest National Costume Nguyễn Cao Minh AnhBest Face Cao Minh HyBest Catwalk Lê An DiBest Inspirer Lộ LộBest Talent Tô Ngọc Bảo LinhMost Popular Nikkie Song Phúc
Miss International Queen Vietnam 2025
Mentors
Mentors Lê Hoàng Phương — Miss Grand Vietnam 2023 & 4th Runner-Up Miss Grand International 2023. Chế Nguyễn Quỳnh Châu — 1st Runner-Up Miss Grand Vietnam 2022. Bùi Quỳnh Hoa — Miss Universe Vietnam 2023. Vũ Thúy Quỳnh — 2nd Runner- Up Miss Universe Vietnam 2023.
Miss International Queen Vietnam 2025
Judges
Judges Nguyễn Hương Giang — Miss International Queen 2018. Nguyễn Minh Tú — Silver Prize Vietnam Supermodel 2013. Vũ Hà Anh — Top 10 Miss Universe Vietnam 2008, 2nd Runner-up Miss Vietnam Global 2008. Nguyễn Huỳnh Như — Star Idol of Vietnam.
Miss International Queen Vietnam 2025
References
References
Miss International Queen Vietnam 2025
Table of Content
Infobox beauty pageant , Final results<ref>{{Cite web, Special Awards, Mentors, Judges, References
File:Iván Cornejo - Aquí Te Espero.jpg
Summary
Summary
File:Iván Cornejo - Aquí Te Espero.jpg
Licensing
Licensing
File:Iván Cornejo - Aquí Te Espero.jpg
Table of Content
Summary, Licensing
Micah Taylor
Short description
Mike Sharrer (May 21, 1977 – September 12, 2024) was an American professional wrestler best known for his appearances with Ohio Valley Wrestling, Deep South Wrestling and WWE. Later in his career, he worked in Japan and the independent circuit in the States.
Micah Taylor
Professional wrestling career
Professional wrestling career Trained by Rip Rogers, Taylor began pro wrestling in 2003 for Ohio Valley Wrestling, a developmental territory of the WWE. He signed a contract with WWE. He teamed with Rob Begley losing to Paul London and Brian Kendrick on WWE Velocity on October 21, 2003.
Micah Taylor
Personal life
Personal life
Micah Taylor
Death
Death Taylor died on September 9, 2024, at 47. Tributes on social media incldued Jax Dane, Bull James and his trainer Rip Rogers.
Micah Taylor
Championships and accomplishments
Championships and accomplishments Deep South Wrestling DSW Tag Team Championship (1 time) - with Tony Salantri Georgia Championship Wrestling / Great Championship Wrestling GCW Heavyweight Championship (1 time) Pro Wrestling Zero1 NWA United National Heavyweight Championship (1 time) Rampage Pro Wrestling RPW Heavyweight Championship (4 times)
Micah Taylor
References
References
Micah Taylor
External links
External links Category:1977 births Category:2024 deaths Category:21th-century male professional wrestlers Category:21th-century American professional wrestlers Category:American male professional wrestlers Category:Professional wrestlers from Hawaii
Micah Taylor
Table of Content
Short description, Professional wrestling career, Personal life, Death, Championships and accomplishments, References, External links
File:Sancho Panza cigars logo.png
Orphaned non-free revisions
File:Sancho Panza cigars logo.png
Summary
Summary
File:Sancho Panza cigars logo.png
Licensing
Licensing
File:Sancho Panza cigars logo.png
Table of Content
Orphaned non-free revisions, Summary, Licensing
Category:September 2014 sports events in Italy
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Sports
Category:September 2014 sports events in Italy
Table of Content
MonthCategoryNav
Category:Wikipedia sockpuppets of Azeemhafeez1
Sockpuppet category
Category:Wikipedia sockpuppets of Azeemhafeez1
Table of Content
Sockpuppet category
Draft:Jade Mitchell (soccer)
AFC submission
Jade Mitchell (born January 29, 2007) is a dual national Canadian and Jamaican soccer player who plays for the Vancouver Rise FC Academy in League1 British Columbia, as well as the Vancouver Rise FC in the Northern Super League on a youth development permit. Mitchell has played with notable youth soccer clubs in Western Canada including Port Moody Soccer Club, Coquitlam Metro Ford Soccer Club and Langley United Soccer Club, and joined the Whitecaps FC Girls Elite from September 2021 to May 2022 and again from July 2024 to present. Between May 2022 and July 2024, Mitchell spent 4 months training at Tovo Academy in Spain before returning to Canada to join Langley United BCSPL where she helped the team win three back to seasons from January 2023 to June 2024, and numerous tournaments and competitions. During this time she also played two seasons with Altitude FC in League 1 British Columbia becoming the team's top goal scorer in 2024 with 5 goals and 1 assist in 9 games.
Draft:Jade Mitchell (soccer)
References
References
Draft:Jade Mitchell (soccer)
Table of Content
AFC submission, References
Category:September 2016 sports events in Italy
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Sports
Category:September 2016 sports events in Italy
Table of Content
MonthCategoryNav
TV Favorites
Infobox album
Perry Como TV Favorites, or simply TV Favorites, subtitled As Sung by Perry on His Chesterfield TV Show, is an album by Perry Como released by RCA Victor in 1952.
TV Favorites
Recording
Recording The album was recorded with Mitchell Ayres and his orchestra.
TV Favorites
Release
Release The album was released on a 10-inch LP (cat. no. LPM 3013) and in several other formats: a set of four 78-rpm phonograph records (cat. no. P 334), a set of four 45-rpm records (WP 334) and a double EP in a gatefold cover (EPB 3013).
TV Favorites
Reception
Reception Billboard reviewed the album in its issue from 26 April 1952. The magazine explained that the album's title referred to the fact that it compiled "eight oldies" that Como had performed "from time to time on his television show" and noted that all of these songs "were excellent vehicles for the relaxed and warm voice of the crooner" and they all were "well-done with standout arrangements." The reviewer concluded: "For those who like the breath of nostalgia that yesterday's hits bring and for the many Como fans, this is a natural", giving the album 78 points out of 100 (which indicated a "good" rating).
TV Favorites
Track listing
Track listing 10-inch LP (RCA Victor LPM 3013)
TV Favorites
References
References Category:1952 albums Category:RCA Victor albums Category:Perry Como albums
TV Favorites
Table of Content
Infobox album , Recording, Release, Reception, Track listing, References
File:Iván Cornejo - Dónde Estás.jpg
Summary
Summary
File:Iván Cornejo - Dónde Estás.jpg
Licensing
Licensing
File:Iván Cornejo - Dónde Estás.jpg
Table of Content
Summary, Licensing
Draft:Sharpness Aware Minimization (SAM)
AfC submission
Sharpness Aware Minimization (SAM) is an optimization algorithm designed to improve the generalization performance of machine learning models, particularly deep neural networks. Instead of merely seeking parameters that achieve low training loss, SAM aims to find parameters that reside in neighborhoods of uniformly low loss, effectively favoring "flat" minima in the loss landscape over "sharp" ones. The intuition is that models converging to flatter minima are more robust to variations between training and test data distributions, leading to better generalization. SAM was introduced by Foret et al. in 2020 in the paper "Sharpness-Aware Minimization for Efficiently Improving Generalization".
Draft:Sharpness Aware Minimization (SAM)
Core idea and mechanism
Core idea and mechanism The core idea of SAM is to minimize a "sharpness-aware" loss function. This is typically formulated as a minimax problem: where: are the model parameters. is the training loss. is an adversarial perturbation. is a hyperparameter defining the size of the neighborhood ( ball) around . The inner maximization finds the perturbation that maximizes the loss within the -neighborhood. The outer minimization updates the weights to minimize this maximized loss. An optional L2 regularization term can also be included. In practice, solving the inner maximization problem exactly is often intractable. SAM approximates the solution by performing a single gradient ascent step to find the adversarial perturbation : The SAM optimizer then typically performs two steps per iteration: Ascent Step (Finding "Sharp" Weights): Calculate the gradient and compute the adversarial weights . Descent Step (Updating Original Weights): Compute the gradient using the adversarial weights and update the original weights using this gradient, typically with a base optimizer like SGD or Adam. This process encourages the model to converge to regions where the loss remains low even when small perturbations are applied to the weights.
Draft:Sharpness Aware Minimization (SAM)
Scenarios where SAM works well
Scenarios where SAM works well SAM has demonstrated significant success in various scenarios: Improved Generalization: SAM consistently leads to better generalization performance across a wide range of deep learning models (especially Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)) and datasets (e.g., ImageNet, CIFAR-10, CIFAR-100 dataset|CIFAR-100]]]). State-of-the-Art Results: It has helped achieve state-of-the-art or near state-of-the-art performance on several benchmark image classification tasks. Robustness to Label Noise: SAM inherently provides robustness to noisy labels in training data, performing comparably to methods specifically designed for this purpose. Out-of-Distribution (OOD) Generalization: Studies have shown that SAM and its variants can improve a model's ability to generalize to data distributions different from the training distribution. Gradual Domain Adaptation: SAM has shown benefits in settings where models are adapted incrementally across changing data domains. Overfitting Mitigation: It is particularly effective in scenarios where models might overfit due to seeing training examples multiple times.
Draft:Sharpness Aware Minimization (SAM)
Scenarios where SAM may not work well or has limitations
Scenarios where SAM may not work well or has limitations Despite its strengths, SAM also has limitations: Increased Computational Cost: The most significant drawback of SAM is its computational overhead. Since it requires two forward and backward passes per optimization step, it roughly doubles the training time compared to standard optimizers. Convergence Guarantees: While empirically successful, theoretical understanding of SAM's convergence properties is still evolving. Some works suggest SAM might have limited capability to converge to global minima or precise stationary points with constant step sizes. Effectiveness of Sharpness Approximation: The one-step gradient ascent used to approximate the worst-case perturbation might become less accurate as training progresses. Multi-step ascent could be more accurate but would further increase computational costs. Domain-Specific Efficacy: While highly effective in computer vision, its benefits might be less pronounced or require careful tuning in other domains. For instance, some studies found limited or no improvement for GPT-style language models that process each training example only once. Potential for Finding "Poor" Flat Minima: While the goal is to find generalizing flat minima, some research indicates that in specific settings, sharpness minimization algorithms might converge to flat minima that do not generalize well. Hyperparameter Sensitivity: SAM introduces new hyperparameters, such as the neighborhood size , which may require careful tuning for optimal performance.
Draft:Sharpness Aware Minimization (SAM)
Recent progress and variants
Recent progress and variants Research on SAM is highly active, focusing on improving its efficiency, understanding its mechanisms, and extending its applicability. Key areas of progress include: Efficiency Enhancements: SAMPa (SAM Parallelized): Modifies SAM to allow the two gradient computations to be performed in parallel. Sparse SAM (SSAM): Applies the adversarial perturbation to only a subset of the model parameters. Single-Step/Reduced-Step SAM: Variants that approximate the sharpness-aware update with fewer computations, sometimes using historical gradient information (e.g., S2-SAM, Momentum-SAM) or applying SAM steps intermittently. Lookahead SAM also aims to reduce overhead. Understanding SAM's Behavior: Implicit Bias Studies: Research has shown that SAM has an implicit bias towards flatter minima, and even applying SAM for only a few epochs late in training can yield significant generalization benefits. Component Analysis: Investigations into which components of the gradient contribute most to SAM's effectiveness in the perturbation step. Performance and Robustness Enhancements: Adaptive SAM (ASAM): Introduces adaptive neighborhood sizes, making the method scale-invariant with respect to the parameters. Curvature Regularized SAM (CR-SAM): Incorporates measures like the normalized Hessian trace to get a more accurate representation of the loss landscape's curvature. Random SAM (R-SAM): Employs random smoothing techniques in conjunction with SAM. Friendly SAM (F-SAM): Aims to refine the perturbation by focusing on the stochastic gradient noise component. Delta-SAM: This term has been used to describe approaches that use dynamic reweighting or other techniques to approximate per-instance adversarial perturbations more efficiently. Specific implementations and papers may vary. ** μP² (Maximal Update and Perturbation Parameterization): Proposes layerwise perturbation scaling to ensure SAM's effectiveness in very wide neural networks. * Broader Theoretical Frameworks: Development of universal classes of sharpness-aware minimization algorithms that can utilize different measures of sharpness beyond the one used in the original SAM (e.g., Frob-SAM using Frobenius norm of the Hessian, Det-SAM using the determinant of the Hessian).
Draft:Sharpness Aware Minimization (SAM)
Current open problems and future directions
Current open problems and future directions Despite significant advancements, several open questions and challenges remain: Bridging the Efficiency Gap: Developing SAM variants that achieve comparable generalization improvements with computational costs close to standard optimizers remains a primary goal. Deepening Theoretical Understanding: Providing tighter generalization bounds that fully explain SAM's empirical success. ** Establishing more comprehensive convergence guarantees for SAM and its variants under diverse conditions. Understanding the interplay between sharpness, flatness, and generalization, and why SAM-found minima often generalize well. Improved Sharpness Approximation: Designing more sophisticated and computationally feasible methods to find or approximate the "worst-case" loss in a neighborhood. Hyperparameter Optimization and Robustness: Developing adaptive methods for setting SAM's hyperparameters (like ) or reducing its sensitivity to them. Applicability Across Diverse Domains: Further exploring and optimizing SAM for a wider range of machine learning tasks and model architectures beyond computer vision, including large language models, reinforcement learning, and graph neural networks. Distinguishing Generalizing vs. Non-Generalizing Flat Minima: Investigating how SAM navigates the loss landscape to select flat minima that are genuinely good for generalization, and avoiding those that might be flat but still lead to poor out-of-sample performance. Interaction with Other Techniques: Understanding how SAM interacts with other regularization techniques, data augmentation methods, and architectural choices. SAM represents a significant step towards building more robust and generalizable deep learning models by explicitly considering the geometry of the loss landscape. Ongoing research continues to refine its efficiency, theoretical underpinnings, and practical applications.
Draft:Sharpness Aware Minimization (SAM)
References
References
Draft:Sharpness Aware Minimization (SAM)
Table of Content
AfC submission, Core idea and mechanism, Scenarios where SAM works well, Scenarios where SAM may not work well or has limitations, Recent progress and variants, Current open problems and future directions, References
Template:Did you know nominations/Scott Burnside
DYKsubpage
Template:Did you know nominations/Scott Burnside
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DYKsubpage
2024 Baden-Württemberg local elections
Infobox election
The 2024 Baden-Württemberg local elections were held on June 9, 2024 to elect members of Baden-Württemberg's 35 district councils and 1101 municipal and local councils. The elections were held on the same day as the 2024 European Parliament election in Germany.
2024 Baden-Württemberg local elections
Background
Background All Germans and European Union citizens who have reached the age of 16 and have resided in their municipality for at least three months were eligible to vote. For the first time, the full address of the candidates was no longer listed in the election announcements and on the ballots. This measure was implemented to protect candidates from harassment.
2024 Baden-Württemberg local elections
Results
Results Following the election local voters' associations remained the largest political grouping at the municipality level and the second largest grouping at the district level. Meanwhile the Christian Democratic Union (CDU) remained the second largest party at the municipal level and the largest party at the district level. The Social Democratic Party, Greens, Free Democratic Party, Die Linke all lost seats. Meanwhile, the Alternative for Germany (AfD) tripled its municipal representation, and gains were also made by a number of smaller political parties. Small parties that won district or municipal seats included: the Free Voters, Ecological Democratic Party, German Communist Party, Team Todenhöfer, Pirate Party Germany, Die PARTEI, and Volt Germany. In the state capital, Stuttgart, the Christian Democratic Union received the most votes, followed by the Greens. The CDU also received the most votes in the cities of Mannheim, Ravensburg, Aalen, and Baden-Baden. The Greens received the most votes in the cities of Karlsruhe, Heidelberg, Ulm, Freiburg im Breisgau, and Tübingen. The Alternative for Germany placed first in Pforzheim and second in Rastatt and Göppingen. |- ! colspan="2" | Party ! Votes (District) ! % ! +/- ! Seats ! +/- ! Votes (Municipal) ! % ! +/- ! Seats ! +/- |- | bgcolor=| | align=left | Christian Democratic Union (CDU) | align=right| 1,214,266 | align=right| 30.12 | align=right| 1.86 | align=right| 702 | align=right| 60 | align=right| 1,030,853 | align=right| 23.85 | align=right| 1.01 | align=right| 4,007 | align=right| 76 |- | bgcolor=| | align=left | Local voters' associations | align=right| 988,540 | align=right| 24.52 | align=right| 0.12 | align=right| 561 | align=right| 11 | align=right| 1,697,596 | align=right| 39.28 | align=right| 0.13 | align=right| 8,687 | align=right| 173 |- | bgcolor=| | align=left | Social Democratic Party (SPD) | align=right| 511,371 | align=right| 12.68 | align=right| 1.32 | align=right| 292 | align=right| 30 | align=right| 511,363 | align=right| 11.83 | align=right| 1.60 | align=right| 1,650 | align=right| 326 |- | bgcolor=| | align=left | The Greens (Grüne) | align=right| 514,031 | align=right| 12.75 | align=right| 4.74 | align=right| 292 | align=right| 93 | align=right| 465,231 | align=right| 10.77 | align=right| 2.13 | align=right| 1,077 | align=right| 181 |- | bgcolor=| | align=left | Alternative for Germany (AfD) | align=right| 469,939 | align=right| 11.66 | align=right| 6.11 | align=right| 277 | align=right| 152 | align=right| 181,598 | align=right| 4.20 | align=right| 2.28 | align=right| 350 | align=right| 233 |- | bgcolor=| | align=left | Free Democratic Party (FDP) | align=right| 212,429 | align=right| 5.27 | align=right| 0.93 | align=right| 121 | align=right| 17 | align=right| 151,014 | align=right| 3.49 | align=right| 0.37 | align=right| 315 | align=right| 52 |- | bgcolor=| | align=left | Die Linke | align=right| 51,346 | align=right| 1.27 | align=right| 0.72 | align=right| 27 | align=right| 13 | align=right| 42,251 | align=right| 0.98 | align=right| 0.37 | align=right| 40 | align=right| 23 |- | bgcolor=| | align=left | Joint election proposals | align=right| 37,852 | align=right| 0.94 | align=right| 0.22 | align=right| 22 | align=right| 6 | align=right| 171,804 | align=right| 3.98 | align=right| 0.38 | align=right| 784 | align=right| 42 |- | bgcolor=| | align=left | Other parties | align=right| 32,156 | align=right| 0.80 | align=right| 0.15 | align=right| 17 | align=right| 6 | align=right| 69,682 | align=right| 1.61 | align=right| 0.67 | align=right| 77 | align=right| 26 |- ! align=right colspan=2| Total ! align=right| 4,031,930 ! align=right| ! align=right| ! align=right| 2,311 ! align=right| 58 ! align=right| 4,321,392 ! align=right| ! align=right| ! align=right| 18,562 ! align=right| 112 |- ! align=right colspan=2| Invalid votes ! align=right| 112,019 ! align=right| ! align=right| ! align=right| ! align=right| ! align=right| 153,329 ! align=right| ! align=right| ! align=right| ! align=right| |- ! align=right colspan=2| Voter turnout ! align=right| 4,338,972 ! align=right| 62.42 ! align=right| ! align=right| ! align=right| ! align=right| 5,186,560 ! align=right| 60.83 ! align=right| ! align=right| ! align=right| |- ! align=right colspan=2| Eligible voters ! align=right| 7,064,954 ! align=right| ! align=right| ! align=right| ! align=right| ! align=right| 8,526,601 ! align=right| ! align=right| ! align=right| ! align=right| |}
2024 Baden-Württemberg local elections
References
References Category:Elections in Baden-Württemberg
2024 Baden-Württemberg local elections
Table of Content
Infobox election , Background, Results, References
File:Iván Cornejo - Baby Please.jpg
Summary
Summary
File:Iván Cornejo - Baby Please.jpg
Licensing
Licensing
File:Iván Cornejo - Baby Please.jpg
Table of Content
Summary, Licensing
Bartaliniaceae
Automatic taxobox
Bartaliniaceae is a family of fungi in the order Amphisphaeriales. , the family is not accepted by Index Fungorum.
Bartaliniaceae
Genera
Genera There are seven accepted genera under the family Bartaliniaceae: Bartalinia Broomella Hyalotia Hyalotiella Pestalozzina Truncatella Zetiasplona
Bartaliniaceae
References
References Category:Amphisphaeriales
Bartaliniaceae
Table of Content
Automatic taxobox , Genera, References
File:Imam Hussein Mosque, Kuwait City 1980 01.jpg
Summary
Summary
File:Imam Hussein Mosque, Kuwait City 1980 01.jpg
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File:Imam Hussein Mosque, Kuwait City 1980 01.jpg
Table of Content
Summary, {{int:license-header}}
Category:August 2021 sports events in Sweden
MonthCategoryNav
+ Sports
Category:August 2021 sports events in Sweden
Table of Content
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Me Prometí
Short description
"Me Prometí" is a single by American singer-songwriter Iván Cornejo, released on May 1, 2025. It was Cornejo himself, Arath Herce, Sara Schell and Frank Rio, who also produced the song with Cornejo.
Me Prometí
Composition
Composition The song blends "airy synths" with "stripped-down acoustic melodies", creating a tone reminiscent of rock music.
Me Prometí
Live performances
Live performances Iván Cornejo performed the song on The Tonight Show Starring Jimmy Fallon on May 6, 2025.
Me Prometí
Charts
Charts + Chart performance for "Me Prometí" Chart (2025) Peakposition US Billboard Hot 100 95 US Hot Latin Songs (Billboard) 9
Me Prometí
References
References Category:2025 singles Category:2025 songs Category:Iván Cornejo songs Category:Interscope Records singles
Me Prometí
Table of Content
Short description, Composition, Live performances, Charts, References
File:Iván Cornejo - Me Prometí.jpg
Summary
Summary
File:Iván Cornejo - Me Prometí.jpg
Licensing
Licensing
File:Iván Cornejo - Me Prometí.jpg
Table of Content
Summary, Licensing
Category:August 2021 in Sweden
Month events in country category header
Category:August 2021 in Sweden
Table of Content
Month events in country category header
Wikipedia:Articles for deletion/Aminu Abdallah
[[:Aminu Abdallah]]
:Aminu Abdallah – (View AfDView log | edits since nomination) () Made 30 appearances in his career for Championship teams before disappearing from the sport. Fails GNG. RossEvans19 (talk) 22:36, 19 May 2025 (UTC) Note: This discussion has been included in the deletion sorting lists for the following topics: Sportspeople, Football, and Ghana. RossEvans19 (talk) 22:36, 19 May 2025 (UTC) Delete – Fails in WP:GNG. Svartner (talk) 04:57, 20 May 2025 (UTC) Note: This discussion has been included in the list of Canada-related deletion discussions. Svartner (talk) 05:06, 20 May 2025 (UTC)