Record 14042026 · 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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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
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.
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
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
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.
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.
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
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
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
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
The 2026 Dynasty was a professional wrestling pay-per-view (PPV) event produced by All Elite Wrestling (AEW). It was the third annual Dynasty and took place on April 12, 2026, at Rogers Arena in Vancouver, British Columbia, Canada, marking the first AEW PPV to
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.
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,
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
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
Dacre railway station served the villages of Dacre and Summerbridge, North Yorkshire, England from 1862 to 1951 on the Nidd Valley Railway.
ArtsFest was an annual free arts festival held in September in and around Birmingham, England from 1997 to 2012. The festival was free for all attendees and featured varied performances ranging from orchestral music, ballet, rock bands, flash mobs, and dance g
Varsha Bhosle was an Indian singer, journalist and writer based in Mumbai. She was the daughter of acclaimed playback singer Asha Bhosle.
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
The term Harbin Russians or Russian Harbinites refers to several generations of Russians who lived in the city of Harbin, Heilongjiang, China. Russian settlers were responsible for turning Harbin into a Russian city with the majority of the population being fr
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
Judit Varga is a Hungarian lawyer and retired politician who served as Minister of Justice of Hungary from her appointment in July 2019, until her resignation in June 2023. In the 2022 Hungarian parliamentary election, she was elected to the National Assembly.
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
Pope Leo XIV is the head of the Catholic Church and sovereign of Vatican City.
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
Hungary is a landlocked country in Central Europe. Spanning much of the Carpathian Basin, it is bordered by Slovakia to the north, Ukraine to the northeast, Romania to the east and southeast, Serbia to the south, Croatia and Slovenia to the southwest, and Aust
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
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
Fidesz – Hungarian Civic Alliance is a political party in Hungary led by Viktor Orbán. It is most notable for dominating Hungarian politics from 2010 to 2026, a period known as the Orbán era. Earlier, from 1998 to 2002, it was the senior partner in a conservat
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
We introduce the Wasserstein Transform (WT), a general unsupervised framework for updating distance structures on given data sets with the purpose of enhancing features and denoising. Our framework represents each data point by a probability measure reflecting the neighborhood structure of the point, and then updates the distance by computing the Wasserstein distance between these probability measures. The Wasserstei
Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT device
Physical Unclonable Functions (PUFs) are promising security primitives for resource-constrained IoT devices. And the XOR Arbiter PUF (XOR-PUF) is one of the most studied PUFs, out of an effort to improve the resistance against machine learning attacks of probably the most lightweight delay-based PUFs - the Arbiter PUFs. However, recent attack studies reveal that even XOR-PUFs with large XOR sizes are still not safe a
AXIL: Exact Instance Attribution for Gradient Boosting
We derive an exact, prediction-specific instance-attribution method for fitted gradient boosting machines (GBMs) trained with squared-error loss, with the learned tree structure held fixed. Each prediction can be written as a weighted sum of training targets, with coefficients determined only by the fitted tree structure and learning rate. These coefficients are exact instance attributions, or AXIL weights. Our main
A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising
In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly correlated with noise information, while also balancing the data fidelity and regularization terms. However, the difficulty of assigning weight is expected to be substantial when the noise pattern is beyond independent identical Gaussian distribution, e.g., impulse noise, strip
Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
A novel form of inference attack in vertical federated learning (VFL) is proposed, where two parties collaborate in training a machine learning (ML) model. Logistic regression is considered for the VFL model. One party, referred to as the active party, possesses the ground truth labels of the samples in the training phase, while the other, referred to as the passive party, only shares a separate set of features corre
M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation
Accurate medical image segmentation is critical for early medical diagnosis. Most existing methods are based on U-shape structure and use element-wise addition or concatenation to fuse different level features progressively in decoder. However, both the two operations easily generate plenty of redundant information, which will weaken the complementarity between different level features, resulting in inaccurate locali
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform aggregation. Existing attempts of heterophilous GNNs incorporate long-range or global aggregations to distinguish nodes in the graph. However, these aggregations usually require iteratively maintaining and updating full-gr
Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings
Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-oriented tasks with low annotation cost. Annotating and gathering utterance relationships in conversations are difficult, while token-level annotations, \eg, entities, slots and templates, are much easier to obtain. Other sentence embedding methods are usually sentence-level self-
RoMa: Robust Dense Feature Matching
Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences. The aim is to learn a robust model, i.e., a model able to match under challenging real-world changes. In this work, we propose such a model, leveraging frozen pretrained features from the foundation model DINOv2. Although these features a
SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions
Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving LLMs to align existing foundation models with scientific disciplines, concepts and goals. In this work, we present \textit{SciTune} as a tuning framework to improve the ability of LLMs to follow multimodal instructions generated from scientific publicatio
A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network
Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional workload bursts, which makes workload prediction challenging. The time series forecasting methods relying on periodicity information, often assume fixed and known periodicity length, which does not align with the periodicity-changeable nature of cloud service workloads. Alt
MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets
Multimodal large language models are typically trained in two stages: first pre-training on image-text pairs, and then fine-tuning using supervised vision-language instruction data. Recent studies have shown that large language models can achieve satisfactory results even with a limited amount of high-quality instruction-following data. In this paper, we introduce MM-LIMA, which is fine-tuned on a small dataset compr
Learning Parallax for Stereo Event-based Motion Deblurring
Due to the extremely low latency, events have been recently exploited to supplement lost information for motion deblurring. Existing approaches largely rely on the perfect pixel-wise alignment between intensity images and events, which is not always fulfilled in the real world. To tackle this problem, we propose a novel coarse-to-fine framework, named NETwork of Event-based motion Deblurring with STereo event and int
A Survey on Deep Learning Techniques for Action Anticipation
The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numerous methods have been introduced for action anticipation in recent years, with deep learning-based approaches being particularly popular. In this work, we review the recent advances of action anticipation algorithms with a particular focus
In a digital epoch where cyberspace is the emerging nexus of geopolitical contention, the melding of information operations and Large Language Models (LLMs) heralds a paradigm shift, replete with immense opportunities and intricate challenges. As tools like the Mistral 7B LLM (Mistral, 2023) democratise access to LLM capabilities (Jin et al., 2023), a vast spectrum of actors, from sovereign nations to rogue entities
We present a novel method for matrix completion, specifically designed for matrices where one dimension is significantly larger than the other. Our Columns Selected Matrix Completion (CSMC) method combines Column Subset Selection with Low-Rank Matrix Completion to efficiently reconstruct incomplete datasets. CSMC substantially reduces computational cost while preserving the solution quality of state-of-the-art convex
GaNI: Global and Near Field Illumination Aware Neural Inverse Rendering
In this paper, we present GaNI, a Global and Near-field Illumination-aware neural inverse rendering technique that can reconstruct geometry, albedo, and roughness parameters from images of a scene captured with co-located light and camera. Existing inverse rendering techniques with co-located light-camera focus on single objects only, without modeling global illumination and near-field lighting more prominent in scen
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control
By leveraging the blur-noise trade-off, imaging with non-uniform exposures largely extends the image acquisition flexibility in harsh environments. However, the limitation of conventional cameras in perceiving intra-frame dynamic information prevents existing methods from being implemented in the real-world frame acquisition for real-time adaptive camera shutter control. To address this challenge, we propose a novel
Language Reconstruction with Brain Predictive Coding from fMRI Data
Many recent studies have shown that the perception of speech can be decoded from brain signals and subsequently reconstructed as continuous language. However, there is a lack of neurological basis for how the semantic information embedded within brain signals can be used more effectively to guide language reconstruction. Predictive coding theory suggests the human brain naturally engages in continuously predicting fu
Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score
Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explored generalisation abilities of these models in few-shot classification tasks and a few studies have addressed far out-of-distribution (OOD) of the models, their potential for addressing near OOD detection remains underexplored. Existing methods either require training fro
Intensity control is a class of continuous-time dynamic optimization problems with many important applications in Operations Research including queueing and revenue management. In this study, we propose a practical continuous-time reinforcement learning framework for intensity control using choice-based network revenue management as a case study, which is a classical problem in revenue management that features a larg
Learning Color Equivariant Representations
In this paper, we introduce group convolutional neural networks (GCNNs) equivariant to color variation. GCNNs have been designed for a variety of geometric transformations from 2D and 3D rotation groups, to semi-groups such as scale. Despite the improved interpretability, accuracy and generalizability of these architectures, GCNNs have seen limited application in the context of perceptual quantities. Notably, the rec
An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs
Relevance and utility are two frequently used measures to evaluate the effectiveness of an information retrieval (IR) system. Relevance emphasizes the aboutness of a result to a query, while utility refers to the result's usefulness or value to an information seeker. In retrieval-augmented generation (RAG), high-utility results should be prioritized to feed to LLMs due to their limited input bandwidth. Re-examini
LaMI: Augmenting Large Language Models via Late Multi-Image Fusion
Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual grounding (e.g., "what color is an emperor penguin's belly?"). Visual Language Models (VLMs) perform better on visually grounded tasks but face two limitations: (i) often reduced performance on text-only commonsense reasoning compared to text-trained LLMs, and (ii) ad
Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal
Multiplicative noise widely exists in radar images, medical images and other important fields' images. Compared to normal noises, multiplicative noise has a generally stronger effect on the visual expression of images. Aiming at the denoising problem of multiplicative noise, we linearize the nonlocal means algorithm with deep learning and propose a linear attention mechanism based deep nonlocal means filtering (L
Notable events recorded on this day and month across all years.