Record 15042026 · captured 2026-08-25
The world looked up Eric Swalwell. 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.
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.
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
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
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
Samrat Choudhary is an Indian politician who is serving as the 24th Chief Minister of Bihar since 15 April 2026. He has been a member of the Bihar Legislative Assembly representing Tarapur Assembly constituency since 2025 and previously served as deputy chief
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.
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
Bhimrao Ramji Ambedkar was an Indian jurist, economist, social reformer and politician who chaired the committee that drafted the Constitution of India based on the debates of the Constituent Assembly of India and the first draft of Sir Benegal Narsing Rau. Am
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
Moya Brennan, also known as Máire Brennan, was an Irish folk singer, songwriter, harpist, and philanthropist. She began performing professionally in 1970 when her family formed the band Clannad. Brennan released her first solo album in 1992 called Máire, a suc
Avatar Aang: The Last Airbender
Avatar Aang: The Last Airbender is a 2026 American animated fantasy action-adventure film directed by Lauren Montgomery from a screenplay by Tim Hedrick and Christopher Yost, based on a story by Bryan Konietzko, Michael Dante DiMartino, Hedrick, and Kenneth Li
Patricia Mae Giraldo is an American singer and songwriter. In the US, she has two multi-platinum albums, five platinum albums, and 15 US Billboard top 40 singles, while in Canada she had eight straight platinum albums, and has sold over 36 million albums world
Phir Kabhi is a 2009 Indian Hindi-language romance film directed by V. K. Prakash and produced by Ronnie Screwvala and Pradeep Guha under their banners UTV Motion Pictures and Culture Company Pvt.Ltd. The film stars Mithun Chakraborty, Dimple Kapadia and Rati
Hung Liu (劉虹) was a Chinese-born American contemporary artist. She was predominantly a painter, but also worked with mixed-media and site-specific installation and was also one of the first artists from China to establish a career in the United States.
Guus "Guusje" ter Horst is a retired Dutch politician of the Labour Party (PvdA) and psychologist. She is a member of the supervisory board of Royal Dutch Shell since 1 January 2013 and chairwoman of the supervisory board of the Institute for Sound and Vision
Azzi Jazlyn Fudd is an American professional basketball player for the Dallas Wings of the Women's National Basketball Association (WNBA). She played college basketball for the UConn Huskies.
Neil Thomas Giraldo is an American musician, record producer, arranger, and songwriter best known as the musical partner of Pat Benatar since 1979 – and husband since 1982. He has also performed, written and produced for artists including Rick Derringer, John
Ruby Rose Langenheim is an Australian actress, television presenter, and model. She gained prominence for her role in season three of the Netflix series Orange Is the New Black (2015–2016) and for portraying Kate Kane / Batwoman in the Arrowverse television fr
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
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
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
WrestleMania 42, also promoted as WrestleMania Vegas, was a 2026 professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 42nd annual WrestleMania and took place as a two-night event on Saturday, April 18 and Sunday, April
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
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 Western and Central factions of chimpanzees of the Ngogo hill region in Kibale National Park, Uganda, have been engaged in a violent conflict since 2015. The conflict has been characterized by one-sided violence, including killing, brutal attacks, and muti
Pope Leo XIV is the head of the Catholic Church and sovereign of Vatican City.
Ernest Anthony Gonzales II is an American politician and United States Navy veteran who served as the U.S. representative for Texas's 23rd congressional district from 2021 until his resignation in April 2026. He is a member of the Republican Party.
Jonathan Gregory Brandis was an American actor. Beginning his career as a child model, Brandis moved on to acting in commercials and subsequently won television and film roles. Brandis made his acting debut in 1982 as Kevin Buchanan on the soap opera One Life
Justin Drew Bieber is a Canadian singer. Regarded as a prominent figure in contemporary popular music, he rose to fame in the late 2000s after being discovered by American talent manager Scooter Braun, who signed him to Raymond Braun Media Group (RBMG). Bieber
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Deep Learning using Rectified Linear Units (ReLU)
The Rectified Linear Unit (ReLU) is a foundational activation function in artficial neural networks. Recent literature frequently misattributes its origin to the 2018 (initial) version of this paper, which exclusively investigated ReLU at the classification layer. This paper formally corrects the citation record by tracing the mathematical lineage of piecewise linear functions from early biological models to their de
NarrativeTime: Dense Temporal Annotation on a Timeline
For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation framework that achieves full coverage of all possible TLinks. To compare with the previous SOTA in dense temporal annotation, we perform full re-annotation of TimeBankDense corpus, which shows comparable agreement with a significant increa
Pictorial and apictorial polygonal jigsaw puzzles from arbitrary number of crossing cuts
Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered visual fragments, is fundamental to numerous applications, and yet most of the literature of the last two decades has focused thus far on less realistic puzzles whose pieces are identical squares. Here, we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting th
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
We examine whether news can improve realised volatility forecasting using a modern yet operationally simple NLP framework. News text is transformed into embedding-based representations, and forecasts are evaluated both as a standalone, news-only model and as a complement to standard realised volatility benchmarks. In out-of-sample tests on a cross-section of stocks, news contains useful predictive information, with s
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogonal input vectors, a precise description of the gradient flow dynamics of training one-hidden layer ReLU neural networks for the mean squared error at small initialisation. In this s
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fail to connect the generated outputs and desired target concepts/preferences implied by the prompts. Current research addressing this limitation has largely focused on enhancing the prompts before output generation or improving the model'
Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization
Unsupervised anomaly localization aims to identify anomalous regions that deviate from normal sample patterns. Most recent methods perform feature matching or reconstruction for the target sample with pre-trained deep neural networks. However, they still struggle to address challenging anomalies because the deep embeddings stored in the memory bank can be less powerful and informative. Specifically, prior methods oft
Echocardiography (echo) is the first imaging modality used when assessing cardiac function. The measurement of functional biomarkers from echo relies upon the segmentation of cardiac structures and deep learning models have been proposed to automate the segmentation process. However, in order to translate these tools to widespread clinical use it is important that the segmentation models are robust to a wide variety
Uncertainty Quantification on Graph Learning: A Survey
Graphical models have demonstrated their exceptional capabilities across numerous applications. However, their performance, confidence, and trustworthiness are often limited by the inherent randomness in data generation and the lack of knowledge to accurately model real-world complexities. There has been increased interest in developing uncertainty quantification (UQ) techniques tailored to graphical models. In this
A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model
Recent booming development of Generative Artificial Intelligence (GenAI) has facilitated model commercialization to reinforce the model performance, including licensing or trading Deep Neural Network (DNN) models. However, DNN model trading may violate the benefit of the model owner due to unauthorized replications or misuse of the model. Model identity auditing is a challenging issue in protecting DNN model ownershi
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large population subgroups. High effectiveness, measured by the fraction o
OmniHands: Towards Robust 4D Hand Mesh Recovery via A Versatile Transformer
In this paper, we introduce OmniHands, a universal approach to recovering interactive hand meshes and their relative movement from monocular or multi-view inputs. Our approach addresses two major limitations of previous methods: lacking a unified solution for handling various hand image inputs and neglecting the positional relationship of two hands within images. To overcome these challenges, we develop a universal a
Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare. While deep learning techniques are becoming standard, adapting them to bioacoustics remains difficult. We address this with animal2vec, an interpretable large transformer model, and a self-supervised training scheme tailored for sparse and
LINSCAN -- A Linearity Based Clustering Algorithm
DBSCAN and OPTICS are powerful algorithms for identifying clusters of points in domains where few assumptions can be made about the structure of the data. In this paper, we leverage these strengths and introduce a new algorithm, LINSCAN, designed to seek lineated clusters that are difficult to find and isolate with existing methods. In particular, by embedding points as normal distributions approximating their local
Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk
The accuracy and understandability of bank failure prediction models are crucial. While interpretable models like logistic regression are favored for their explainability, complex models such as random forest, support vector machines, and deep learning offer higher predictive performance but lower explainability. These models, known as black boxes, make it difficult to derive actionable insights. To address this chal
E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning
Processing long contexts is increasingly important for Large Language Models (LLMs) in tasks like multi-turn dialogues, code generation, and document summarization. This paper addresses the challenges of achieving high long-context performance, low computational complexity, and compatibility with pretrained models -- collectively termed the ``impossible triangle''. We introduce E2LLM (Encoder Elongated Large
Towards Generalized Certified Robustness with Multi-Norm Training
Existing certified training methods can only train models to be robust against a certain perturbation type (e.g. $l_\infty$ or $l_2$). However, an $l_\infty$ certifiably robust model may not be certifiably robust against $l_2$ perturbation (and vice versa) and also has low robustness against other perturbations (e.g. geometric and patch transformation). By constructing a theoretical framework to analyze and mitigate
metasnf: Meta Clustering with Similarity Network Fusion in R
metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by clustering the solutions themselves, to clustering workflows based on similarity network fusion (SNF). SNF is a multi-modal data integration algorithm commonly used for biomedical subtype discovery. The package also contains functions to assist with cluster visualization, chara
GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization
With the increasing quality and spread of LLM assistants, the amount of generated content is growing rapidly. In many cases and tasks, such texts are already indistinguishable from those written by humans, and the quality of generation continues to increase. At the same time, detection methods are advancing more slowly than generation models, making it challenging to prevent misuse of generative AI technologies. We p
Scale-aware Message Passing For Graph Node Classification
Most Graph Neural Networks (GNNs) operate at the first-order scale, even though multi-scale representations are known to be crucial in domains such as image classification. In this work, we investigate whether GNNs can similarly benefit from multi-scale learning, rather than being limited to a fixed depth of $k$-hop aggregation. We begin by formalizing scale invariance in graph learning, providing theoretical guarant
Speaker effects in language comprehension: An integrative model of language and speaker processing
The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and proposes an integrative model of language and speaker processing that integrates distinct mechanistic perspectives. We argue that speaker effects arise from the interplay between bottom-up perception-based processes, driven by acoustic-episodic memory, and top-down expect
Clustering with Uniformity- and Neighbor-Based Random Geometric Graphs
We propose a graph-based clustering method based on Cluster Catch Digraphs (CCDs) that extends their applicability to moderate-dimensional data settings. Existing CCD variants, such as RK-CCDs, rely on spatial randomness tests based on Ripley's K function, which exhibit performance degradation as dimensionality increases. To address this limitation, we introduce a nearest-neighbor-distance (NND) based Monte Carlo
Diffusion models (DMs) have recently demonstrated remarkable generation capability. However, their training generally requires huge computational resources and large-scale datasets. To solve these, recent studies empower DMs with the advanced Retrieval-Augmented Generation (RAG) technique and propose retrieval-augmented diffusion models (RDMs). By incorporating rich knowledge from an auxiliary database, RAG enhances
AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought
Large language models (LLMs) have shown impressive multilingual capabilities through pretraining on diverse corpora. Although these models show strong reasoning abilities, their performance varies significantly between languages due to the imbalanced distribution of training data. Existing approaches using sample-level translation for extensive multilingual pretraining and cross-lingual tuning face scalability challe
RegD: Hierarchical Embeddings via Dissimilarity between Arbitrary Euclidean Regions
Hierarchical data is common in many domains like life sciences and e-commerce, and its embeddings often play a critical role. While hyperbolic embeddings offer a theoretically grounded approach to representing hierarchies in low-dimensional spaces, current methods often rely on specific geometric constructs as embedding candidates. This reliance limits their generalizability and makes it difficult to integrate with t
Notable events recorded on this day and month across all years.