Record 16042026 · 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.
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
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
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
Alang is a village in Sadan Rostaq-e Gharbi Rural District of the Central District in Kordkuy County, Golestan province, Iran.
The fifth and final season of the American satirical superhero television series The Boys, the first series in the franchise based on the comic book series of the same name created by Garth Ennis and Darick Robertson, was developed for television by Eric Kripk
The Turing test was designed by Alan Turing to assess a machine's ability to exhibit intelligent behaviour equivalent to that of a human by imitating interactive dialogue. In the modern version, a human evaluator judges a text transcript of a natural-language
James Mark Ward is an American body piercer. In a 2004 documentary, entitled The Social History of Piercing, MTV called him "the granddaddy of the modern body piercing movement."
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
Ramsey Khalid Ismael, better known as Johnny Somali, is an American former “nuisance” live streamer and YouTuber, best known for his provocative and hostile behavior while in other countries. Ismael has been banned from Twitch, Kick, Rumble and Parti.
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
Braden Eric Peters, better known as Clavicular or Clav, is an American livestreamer, internet personality, and influencer. He became known in 2025 on TikTok and Kick for his usage of incelosphere slang, for his "looksmaxxing" content, incorporating controversi
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
Michael Akpovie Olise is a professional footballer who plays as a winger or attacking midfielder for Bundesliga club Bayern Munich and the France national team. Widely regarded as one of the best players in the world, he is known for his creative playmaking, t
Deni Avdija is an Israeli-Serbian professional basketball player for the Portland Trail Blazers of the National Basketball Association (NBA). He plays the small forward position, and is nicknamed "Turbo" for his fast-paced drive and aggressive playing style. P
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
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 Boys is an American satirical superhero streaming television series developed by Eric Kripke for Amazon Prime Video. Based on the comic book series of the same name by Garth Ennis and Darick Robertson, it follows the eponymous team of vigilantes as they co
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
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
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
Lena Dunham is an American writer, director, actress, and producer. She is the creator, writer, and star of the HBO television series Girls (2012–2017), for which she received several Emmy Award nominations and two Golden Globe Awards. Dunham also directed sev
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
Dianna Marie Russini is an American former sports journalist who worked as a National Football League (NFL) reporter and insider.
The 2025–26 UEFA Champions League was the 71st season of Europe's premier club football tournament organised by UEFA, and the 34th season since it was rebranded from the European Cup to the UEFA Champions League.
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
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.
DOOM Level Generation using Generative Adversarial Networks
We applied Generative Adversarial Networks (GANs) to learn a model of DOOM levels from human-designed content. Initially, we analysed the levels and extracted several topological features. Then, for each level, we extracted a set of images identifying the occupied area, the height map, the walls, and the position of game objects. We trained two GANs: one using plain level images, one using both the images and some of
Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)
In this work, we present a novel matrix-encoding method that is particularly convenient for neural networks to make predictions in a privacy-preserving manner using homomorphic encryption. Based on this encoding method, we implement a convolutional neural network for handwritten image classification over encryption. For two matrices $A$ and $B$ to perform homomorphic multiplication, the main idea behind it, in a simp
SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion
Depth completion involves recovering a dense depth map from a sparse map and an RGB image. Recent approaches focus on utilizing color images as guidance images to recover depth at invalid pixels. However, color images alone are not enough to provide the necessary semantic understanding of the scene. Consequently, the depth completion task suffers from sudden illumination changes in RGB images (e.g., shadows). In this
Convex Hulls of Reachable Sets
We study the convex hulls of reachable sets of nonlinear systems with bounded disturbances and uncertain initial conditions. Reachable sets play a critical role in control, but remain notoriously challenging to compute, and existing over-approximation tools tend to be conservative or computationally expensive. In this work, we characterize the convex hulls of reachable sets as the convex hulls of solutions of an ordi
New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain sufficiently overparameterized settings, the SVM decision function coincides exactly with the minimum-norm label interpolant. This phenomenon of support vector proliferation
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data distribution, thus feature preprocessing, which transforms features from one distribution to another, is a crucial step to ensure good model quality. Manually constructing a feature preprocessing pipeline is challenging because data scientists need to make difficult decisions
Authorship Verification (AV) is a key area of research in digital text forensics, which addresses the fundamental question of whether two texts were written by the same person. Numerous computational approaches have been proposed over the last two decades in an attempt to address this challenge. However, existing AV methods often suffer from high complexity, low explainability and especially from a lack of clear scie
Topic-Based Watermarks for Large Language Models
The indistinguishability of large language model (LLM) output from human-authored content poses significant challenges, raising concerns about potential misuse of AI-generated text and its influence on future model training. Watermarking algorithms offer a viable solution by embedding detectable signatures into generated text. However, existing watermarking methods often involve trade-offs among attack robustness, ge
Nonparametric Sparse Online Learning of the Koopman Operator
The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. However, existing data-driven approaches to learning the Koopman operator rely on batch data. In this work, we present a sparse online learning algorithm that learns the Koopman operator iteratively via stochastic approximation, with explicit control over model complexity and provable convergence g
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
Recent advancements in large multimodal language models have demonstrated remarkable proficiency across a wide range of tasks. Yet, these models still struggle with understanding the nuances of human humor through juxtaposition, particularly when it involves nonlinear narratives that underpin many jokes and humor cues. This paper investigates this challenge by focusing on comics with contradictory narratives, where e
Fast training of accurate physics-informed neural networks without gradient descent
Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions, their accuracy and training speed are limited by two core barriers: gradient-descent-based iterative optimization over complex loss landscapes and non-causal treatment of time as an extra
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Positron emission tomography (PET) provides molecular biomarkers for Alzheimer's disease and related dementias (ADRD) and is increasingly used for diagnosis, staging, and clinical trial enrichment. However, its use is limited by cost, regulatory restrictions, and the invasiveness of radiotracer injection. Although current frameworks emphasize multimodal biomarker assessment, including the amyloid/tau/neurodegener
What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction
Vision-language models have shown impressive progress in recent years. However, existing models are largely limited to turn-based interactions, where each turn must be stepped (i.e., prompted) by the user. Open-ended, asynchronous interactions, where an AI model may proactively deliver timely responses or feedback based on the unfolding situation in real-time, are an open challenge. In this work, we present the QEVD
AutoPV: Automatically Design Your Photovoltaic Power Forecasting Model
Photovoltaic power forecasting (PVPF) is a critical area in time series forecasting (TSF), enabling the efficient utilization of solar energy. With advancements in machine learning and deep learning, various models have been applied to PVPF tasks. However, constructing an optimal predictive architecture for specific PVPF tasks remains challenging, as it requires cross-domain knowledge and significant labor costs. To
Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance
The stochastic gradient descent (SGD) algorithm has been widely used to optimize deep Cox neural network (Cox-NN) by updating model parameters using mini-batches of data. We show that SGD aims to optimize the average of mini-batch partial-likelihood, which is different from the standard partial-likelihood. This distinction requires developing new statistical properties for the global optimizer, namely, the mini-batch
On an $L^2$ norm for stationary ARMA processes
We propose an $L^2$ norm for stationary Autoregressive Moving Average (ARMA) models. We look at ARMA models within the Hilbert space of the past with present of a true purely linearly non-deterministic stationary process $X_t$, and compute the $L^2$ norm based on its Wold decomposition. As an application of this $L^2$ norm, we derive bounds on the mean square prediction error for AR(1) models of MA(1) processes, and
Soil sinkholes significantly influence soil degradation, infrastructure vulnerability, and landscape evolution. However, their irregular shapes, combined with interference from shadows and vegetation, make it challenging to accurately quantify their properties using remotely sensed data. In addition, manual annotation can be laborious and costly. In this study, we introduce a novel self-supervised framework for sinkh
Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait (FoG) critical. However, existing symptom monitoring technologies are power-hungry, rely on extensive amounts of labeled data, and operate in controlled settings. These shortcomings limit real-world deployment of the technology. This w
The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to prevent devastating chronic complications that arise from poor glucose control. However, forecasting blood glucose levels is challenging because blood glucose changes consistently in
Training Hamiltonian neural networks without backpropagation
Neural networks that synergistically integrate data and physical laws offer great promise in modeling dynamical systems. However, iterative gradient-based optimization of network parameters is often computationally expensive and suffers from slow convergence. In this work, we present a backpropagation-free algorithm to accelerate the training of neural networks for approximating Hamiltonian systems through data-agnos
Learning Hidden Physics and System Parameters with Deep Operator Networks
Discovering hidden physical laws and identifying governing system parameters from sparse observations are central challenges in computational science and engineering. Existing data-driven methods, such as physics-informed neural networks (PINNs) and sparse regression, are limited by their need for extensive retraining, sensitivity to noise, or inability to generalize across families of partial differential equations
Fused Deposition Modeling (FDM) is a widely used additive manufacturing (AM) technique valued for its flexibility and cost-efficiency, with applications in a variety of industries including healthcare and aerospace. Recent developments have made affordable FDM machines accessible and encouraged adoption among diverse users. However, the design, planning, and production process in FDM require specialized interdiscipli
TableMaster: A Recipe to Advance Table Understanding with Language Models
Tables serve as a fundamental format for representing structured relational data. While current language models (LMs) excel at many text-based tasks, they still face challenges in table understanding due to the complex characteristics of tabular data, such as their structured nature. In this paper, we aim to enhance LMs for improved table understanding. We identify four key challenges: 1) difficulty in locating targe
In politically sensitive scenarios like wars, social media serves as a platform for polarized discourse and expressions of strong ideological stances. While prior studies have explored ideological stance detection in general contexts, limited attention has been given to conflict-specific settings. This study addresses this gap by analyzing 9,969 Reddit comments related to the Israel-Palestine conflict, collected betw
Cancer surgery is a key treatment for gastrointestinal (GI) cancers, a group of cancers that account for more than 35% of cancer-related deaths worldwide, but postoperative complications are unpredictable and can be life-threatening. In this paper, we investigate how recent advancements in large language models (LLMs) can benefit remote patient monitoring (RPM) systems through clinical integration by designing RECOVE
Notable events recorded on this day and month across all years.