Record 13042026 · captured 2026-08-25
The world looked up 2026 Hungarian parliamentary election. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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What the most people looked up, ranked by Wikipedia pageviews for that day.
2026 Hungarian parliamentary election
Parliamentary elections were held in Hungary on 12 April 2026 to elect all 199 members of the National Assembly. It was the 10th parliamentary election and the highest-turnout election since Hungary's transition to democracy in 1990. The incumbent Fidesz–KDNP
Asha Bhosle was an Indian playback singer and actress who predominantly worked in Indian cinema. Known for her versatility, she was described in the media as one of the greatest and most influential singers in Hindi cinema. In a career spanning over eight deca
Rory Daniel McIlroy is a Northern Irish professional golfer who plays on the PGA Tour and the European Tour. He is a former world number one in the Official World Golf Ranking and has spent over 100 weeks in that position during his career. A six-time major ch
Péter Magyar is a Hungarian politician who has served as prime minister of Hungary since May 2026. He has been the president of the Tisza Party since 2024 and was a member of the European Parliament (MEP) from 2024 to 2026.
Cameron David Young is an American professional golfer who plays on the PGA Tour, where he has won three titles.
The Respect and Freedom Party, commonly known by its Hungarian abbreviations Tisza Party and TISZA, is a conservative, centre-right, pro-European, and populist political party in Hungary. It has been the governing party since the 2026 general election.
Justin Peter Rose is an English professional golfer who plays on the PGA Tour and the European Tour. He is a former world number one in the Official World Golf Ranking. He has won one major championship, the 2013 U.S. Open.
Viktor Mihály Orbán is a Hungarian lawyer and politician who served as the prime minister of Hungary from 1998 to 2002 and from 2010 to 2026. He has also been the president of Fidesz, which has been variously characterised as a Christian nationalist, illiberal
Eric Michael Swalwell is an American former politician who served as a U.S. representative from California from 2013 to 2026. A member of the Democratic Party, Swalwell previously served on the city council for Dublin, California from 2010 to 2013.
List of Masters Tournament champions
The Masters Tournament is a golf competition that was established in 1934, with Horton Smith winning the inaugural tournament. The Masters is the first of four major championships to be played each year, with the final round of the Masters always being schedul
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
Lata Dinanath Mangeshkar was an Indian playback singer and occasional music composer. She is considered to be one of the greatest and most influential singers of the Indian subcontinent. Her contribution to the Indian music industry in a career spanning eight
UFC 327: Procházka vs. Ulberg was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on April 11, 2026 at the Kaseya Center in Miami, Florida, United States.
2022 Hungarian parliamentary election
Parliamentary elections were held in Hungary on 3 April 2022 to elect the National Assembly, coinciding with a referendum. Hungary's incumbent prime minister Viktor Orbán won re-election to a fourth term. Addressing his supporters after the partial results sho
Scott Alexander Scheffler is an American professional golfer who plays on the PGA Tour. He is currently ranked world number one in the Official World Golf Ranking, a position he has held for over 175 weeks during his career. He has won four major championships
List of highest-grossing Indian films
This ranking lists the highest-grossing Indian films produced by Indian cinema, based on conservative global box office estimates as reported by organisations classified as green by Wikipedia. The figures are not adjusted for inflation. However, there is no of
Varsha Bhosle was an Indian singer, journalist and writer based in Mumbai. She was the daughter of acclaimed playback singer Asha Bhosle.
Tyson Luke Fury is a British professional boxer. He held multiple world heavyweight championships, including unified titles from 2015 to 2016, the Ring magazine title twice between 2015 and 2022, and the World Boxing Council (WBC) title from 2020 to 2024. He a
Coachella 2026 was a music and arts festival that took place at the Empire Polo Club, in Indio, California, from April 10 to 19, 2026. It was the 25th edition of the festival. The scheduled headlining performers were Sabrina Carpenter, Justin Bieber, Karol G,
Joshua Seth Hokit is an American professional mixed martial artist who currently competes in the Heavyweight division of the Ultimate Fighting Championship (UFC). He formerly competed in Bellator MMA. As of July 25, 2026, he is #6 in the Meta UFC heavyweight r
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
Artemis II was a crewed flyby of the Moon. It is currently the only crewed flight beyond low Earth orbit since Apollo 17 in 1972. It was the first crewed flight of the NASA-led Artemis program, the first crewed flight of the Space Launch System (SLS), and the
Christina Hammock Koch is an American engineer and NASA astronaut. On her mission to the International Space Station in 2019–20 she was part of the first all‑female spacewalk and set the record for the longest spaceflight by a woman. On the Artemis II lunar fl
Neatsville is an unincorporated community in Adair County, in the U.S. state of Kentucky. It is located at the junction of Kentucky Route 206 and Kentucky Route 76. Its elevation is 705 feet (215 m). For unknown reasons, the town's name was spelled as Neetsvil
The following notable deaths occurred in 2026. Names are reported under the date of death, in alphabetical order. A typical entry reports information in the following sequence:Name, age, country of citizenship at birth, subsequent nationality, what subject was
Malcolm in the Middle: Life's Still Unfair
Malcolm in the Middle: Life's Still Unfair is an American television sitcom miniseries created by Linwood Boomer for Hulu. It is a revival of Malcolm in the Middle (2000–2006), produced by New Satin City Productions, The Jackal Group, Regency Television, and 2
Muzi Mei is a journalist and blogger from Guangzhou, China, who became an Internet celebrity in late 2003. Her blog contained frank descriptions of her sexual encounters with various men, which is believed to be a first for China.
Karen Boback is an American politician and educator who served as a Republican member of the Pennsylvania House of Representatives for the 117th legislative district from 2007 to 2022.
Colman Jason Domingo is an American actor, playwright and director. Prominent on both screen and stage since the 2010s, Domingo has received various accolades, including a Primetime Emmy Award, and nominations for two Academy Awards and two Tony Awards. Time m
List of men's major championships winning golfers
The men's major golf championships, also known simply as the majors, are the four most prestigious events in professional golf. The competitions are the Masters Tournament, the PGA Championship, the U.S. Open, and The Open Championship, contested annually.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Task-Distributionally Robust Data-Free Meta-Learning
Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models without accessing their original training data. While existing DFML methods typically generate synthetic data from these models to perform meta-learning, a comprehensive analysis of DFML's robustness-particularly its failure modes and vulnerability to potential attacks-remai
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to maximize vehicle speed and throughput. This paper explores advisory autonomy, in which real-time driving advisories are issued to the human drivers, thus achieving near-term performance of automated vehicles. Due to the complexity of traffic systems, recent studies of coordinating
Embedding Non-Distortive Cancelable Face Template Generation
Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage. We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model. Using the proposed app
Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction
Designing seamless, frictionless user experiences has long been a dominant trend in both applied behavioral science and artificial intelligence (AI), in which the goal of making desirable actions easy and efficient informs efforts to minimize friction in user experiences. However, in some settings, friction can be genuinely beneficial, such as the insertion of deliberate delays to increase reflection, preventing indi
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph learning methods in MARL are limited. They rely solely on one-step observations, neglecting crucial historical experiences, leading to deficient graphs that foster redundant or detrimental information exchanges. Additionally, high computatio
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for learning this graph primarily focus on agent-pair relations, neglecting higher-order relationships. While several approaches attempt to extend cooperation modelling to encompass behaviour similarities within groups, they commonly fall short in
Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplify the popularity bias in recommendation models, resulting in more frequent recommendation of hot items and fewer long-tail items, but also include a substantial amount of redundant information that is essentially meaningless for the model&#
Contrastive Feedback Mechanism for Simultaneous Speech Translation
Recent advances in simultaneous speech translation (SST) focus on the decision policies that enable the use of offline-trained ST models for simultaneous inference. These decision policies not only control the quality-latency trade-off in SST but also mitigate the impact of unstable predictions on translation quality by delaying translation for more context or discarding these predictions through stable hypothesis de
Detection and Characterization of Coordinated Online Behavior: A Survey
Coordination is a fundamental aspect of life. The advent of social media has made it integral also to online human interactions, such as those that characterize thriving online communities and social movements. At the same time, coordination is also core to effective disinformation, manipulation, and hate campaigns. This survey collects, categorizes, and critically discusses the body of work produced as a result of t
TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to either single-step inference or are constrained to natural language answers. In this work, we introduce TS-Reasoner, a domain-specialized agent designed for multi-step time series inference. By integrating large language model (LLM) reasoning w
Electrocardiogram (ECG) captures the heart's electrical signals, offering valuable information for diagnosing cardiac conditions. However, the scarcity of labeled data makes it challenging to fully leverage supervised learning in the medical domain. Self-supervised learning (SSL) offers a promising solution, enabling models to learn from unlabeled data and uncover meaningful patterns. In this paper, we show that
On Divergence Measures for Training GFlowNets
Generative Flow Networks (GFlowNets) are amortized inference models designed to sample from unnormalized distributions over composable objects, with applications in generative modeling for tasks in fields such as causal discovery, NLP, and drug discovery. Traditionally, the training procedure for GFlowNets seeks to minimize the expected log-squared difference between a proposal (forward policy) and a target (backward
FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening
Scalability of Graph Neural Networks (GNNs) remains a significant challenge. To tackle this, methods like coarsening, condensation, and computation trees are used to train on a smaller graph, resulting in faster computation. Nonetheless, prior research has not adequately addressed the computational costs during the inference phase. This paper presents a novel approach to improve the scalability of GNNs by reducing co
OmniPrism: Learning Disentangled Visual Concept for Image Generation
Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. However, existing methods are typically constrained to single-aspect concept generation or are easily disrupted by irrelevant concepts in multi-aspect concept scenarios, leading to concept confusion and hindering creative generation. To address this, we propose OmniPrism, a visual conce
Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges
Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability, largely attributed to their English-centric pre-training data. In this paper, we introduce and investigate cross-lingual latent transplantation (XTransplant), a probing framework which aims to further exploit the model's internalized multilingual knowledge during inference and examine its effects
Automatic Self-supervised Learning for Social Recommendations
In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendation methods require carefully designed auxiliary social tasks tailored to specific scenarios, which depend heavily on domain knowledge and expertise. To address this limitation, we propose Automatic Self-supervised Learning for Social Recommendations (AusRec), which integrates m
Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this issue by filtering attacked graphs. However, they struggle to defend effectively against multiple types of adversarial attacks (e.g., targeted attacks and non-targeted attacks) simultaneously due to limited flexibility. Additionally, these
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to converge to a single value and become indistinguishable. This issue stems from the inherent limitations of GNNs, which struggle to distinguish the importance of information from different neighborhoods. I
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hierarchy, and propose two computationally efficient inference algorithms. The first algorithm returns i
Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks
Subspace inference for neural networks assumes that a subspace of their parameter space suffices to produce a reliable uncertainty quantification. In this work, we underpin the validity of this assumption by using low rank techniques. We derive an expression for a subspace model to a Bayesian inference scenario based on the Laplace approximation that is, in a certain sense, optimal given a specific dataset. We empiri
Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution
Pervasive polysemanticity in large language models (LLMs) undermines discrete neuron-concept attribution, posing a significant challenge for model interpretation and control. We systematically analyze both encoder and decoder based LLMs across diverse datasets, and observe that even highly salient neurons for specific semantic concepts consistently exhibit polysemantic behavior. Importantly, we uncover a consistent p
Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models
Deep Spatio-Temporal Neural Network for Air Quality Reanalysis
Air quality prediction is key to mitigating health impacts and guiding decisions, yet existing models tend to focus on temporal trends while overlooking spatial generalization. We propose AQ-Net, a spatiotemporal reanalysis model for both observed and unobserved stations in the near future. AQ-Net utilizes the LSTM and multi-head attention for the temporal regression. We also propose a cyclic encoding technique to en
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key informati
Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past. However, the majority of the studies lack more robust feature alignment and finer aspect-level alignment. In this paper, we propose a novel framework, MSMO: Multi-Scale and Multi-Objective optimization for cross-lingual ABSA. During multi-scale alignment, we achieve cross-lingual sentence-level and aspect-l
Notable events recorded on this day and month across all years.