Record 16032026 · captured 2026-08-25
The world looked up Benjamin Netanyahu. 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.
Benjamin Netanyahu, nicknamed "Bibi", is an Israeli politician and diplomat who has served as Prime Minister of Israel since 2022. Having previously held office from 1996 to 1999 and from 2009 to 2021, Netanyahu is Israel's longest-serving prime minister.
Amy Marie Madigan is an American actress. Known for her work on stage and screen, her accolades include an Academy Award, an Actor Award, a Golden Globe Award, and a Critics' Choice Award, in addition to a nomination for a Primetime Emmy Award.
The 98th Academy Awards ceremony, presented by the Academy of Motion Picture Arts and Sciences (AMPAS), took place on March 15, 2026, at the Dolby Theatre in Hollywood, Los Angeles. During the gala, the AMPAS presented Academy Awards in 24 categories honoring
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
2026 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. The election saw the defeat
John James Shannon, better known by his stage name John Alford, was a British actor, singer, convicted child sex offender and drug dealer. He played Robbie Wright in the BBC series Grange Hill (1985–1990) and Billy Ray in the ITV series London's Burning (1993–
Cameron David Young is an American professional golfer who plays on the PGA Tour, where he has won three titles.
Harry Edward Styles is an English singer, songwriter, and actor. An influential figure in popular culture, he is known for his showmanship, artistry, and flamboyant fashion. Styles's musical career began in 2010 as part of One Direction, a boy band formed on t
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
Edward Allen Harris is an American actor and filmmaker. Harris received nominations for the Academy Award for Best Supporting Actor for his performances in Apollo 13 (1995), The Truman Show (1998), and The Hours (2002). He also directed and starred in Pollock
The Madison is a contemporary Western television series created by Taylor Sheridan for Paramount+. The series follows the Clyburn family, originally from New York City, who relocate to the Madison River valley of southwest Montana for emotional recovery follow
2026 Tamil Nadu Legislative Assembly election
Elections to appoint the 234 members of the 17th Tamil Nadu Legislative Assembly, the highest body of the Government of Tamil Nadu, were held on 23 April 2026. The results were declared on 4 May 2026 by the Election Commission of India. It recorded the highest
Andrea Kimi Antonelli is an Italian racing driver who competes in Formula One for Mercedes. Antonelli has won six Formula One Grands Prix since his debut in 2025.
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
Max Robert Dowman is an English professional footballer who plays as an attacking midfielder or a winger for Premier League club Arsenal.
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
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
One Piece is a fantasy adventure television series developed by Matt Owens and Steven Maeda for Netflix. The series is a live-action adaptation of the 1997 Japanese manga series One Piece by Eiichiro Oda, who also serves as a creative consultant. It is produce
2026 Formula One World Championship
The 2026 FIA Formula One World Championship is a motor racing championship for Formula One cars and the 77th running of the Formula One World Championship. It is recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of internati
Hamnet is a 2025 historical drama film directed by Chloé Zhao, who co-wrote the screenplay with Maggie O'Farrell, based on the 2020 novel by O'Farrell. The film dramatises the family life of William Shakespeare and his wife Agnes Hathaway as they cope with the
Scarpetta is an American crime drama television series developed by Liz Sarnoff and based on the book series of the same name by Patricia Cornwell. It stars Nicole Kidman as the titular character as Dr. Kay Scarpetta, a forensic pathologist who uses forensic t
Jessie Buckley is an Irish actress and singer. Her accolades include an Academy Award, two BAFTAs, an Actor Award, a Golden Globe Award, a Critics' Choice Award and a Laurence Olivier Award.
The Ides of March is the day on the Roman calendar marked as the Idus, roughly the midpoint of a month, of Martius, corresponding to 15 March on the Gregorian calendar. It was marked by several major religious observances. In 44 BC, it became notorious as the
Pathfinder Badge (United States)
The Pathfinder Badge is a military badge of the United States Army awarded to soldiers who complete the U.S. Army Sabalauski Air Assault School's Pathfinder Course or the Army National Guard, Warrior Training Center, Mobile Training Team's Pathfinder Course at
War Machine is a 2026 military science fiction action film directed, co-produced, and co-written by Patrick Hughes. It stars Alan Ritchson, Dennis Quaid, Stephan James, Jai Courtney, Esai Morales, Keiynan Lonsdale, and Daniel Webber, and follows a staff sergea
Weapons is a 2025 American supernatural mystery horror film directed, written, produced, and co-scored by Zach Cregger. It stars an ensemble cast including Josh Brolin, Julia Garner, Alden Ehrenreich, Austin Abrams, Cary Christopher, Toby Huss, Benedict Wong,
The World Baseball Classic (WBC), also referred to as The Classic, is a quadrennial international baseball tournament sanctioned by the World Baseball Softball Confederation (WBSC), the sport's global governing body, and organized by World Baseball Classic Inc
Hermann Wilhelm Göring was a German politician, aviator, military commander, and convicted war criminal. He was one of the most powerful figures in the Nazi Party, which controlled Germany from 1933 to 1945. He also served as Oberbefehlshaber der Luftwaffe, a
Louis Theroux is an American-British journalist, broadcaster, documentarian and author. He has received three British Academy Television Awards and a Royal Television Society Television Award.
Mojtaba Hosseini Khamenei is an Iranian Shia cleric and politician who has served as the third supreme leader of Iran since 2026. A member of the Khamenei family and the second son of second supreme leader Ali Khamenei, he previously served as Vakil of the Sup
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic
With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. In this paper we propose a deep reinforcement learning (DRL) algorithm to slice the available physical layer resources between ultra-reliable low-latency communications (URLLC) and
Trading Positional Complexity vs. Deepness in Coordinate Networks
It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features. Hitherto, the rationale for the effectiveness of these positional encodings has been mainly studied through a Fourier lens. In this paper, we strive to broaden this understanding by showing that alternative non-Fourier embedding funct
Review of Natural Language Processing in Pharmacology
Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey
Data-Driven Influence Functions for Optimization-Based Causal Inference
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inference. We study the case where probability distributions are not known a priori but need to be estimated from data. These estimated distributions lead to empirical Gateaux derivatives, and we study the relationships between empirical, numerica
Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm
In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of paramount importance. However, directly estimating these parameters using the empirical moment generating function (MGF) is infeasible. To address this, we suggest using the sub-Gaussian intrinsic moment norm [Buldygin and Kozachenko (2000), Theorem 1.3] achieved by maximizing a sequence of normalized moments. Significantly, the
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing
We study provable multi-agent reinforcement learning (RL) in the general framework of partially observable stochastic games (POSGs). To circumvent the known hardness results and the use of computationally intractable oracles, we advocate leveraging the potential \emph{information-sharing} among agents, a common practice in empirical multi-agent RL, and a standard model for multi-agent control systems with communicati
Why Softmax Attention Outperforms Linear Attention
Large transformer models have achieved state-of-the-art results in numerous natural language processing tasks. Among the pivotal components of the transformer architecture, the attention mechanism plays a crucial role in capturing token interactions within sequences through the utilization of softmax function. Conversely, linear attention presents a more computationally efficient alternative by approximating the soft
Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors
We propose denoising diffusion variational inference (DDVI), a black-box variational inference algorithm for latent variable models which relies on diffusion models as flexible approximate posteriors. Specifically, our method introduces an expressive class of diffusion-based variational posteriors that perform iterative refinement in latent space; we train these posteriors with a novel regularized evidence lower boun
Sampling and Uniqueness Sets in Graphon Signal Processing
In this work, we study the properties of sampling sets on families of large graphs by leveraging the theory of graphons and graph limits. To this end, we extend to graphon signals the notion of removable and uniqueness sets, which was developed originally for the analysis of signals on graphs. We state the formal definition of a $Λ-$removable set and conditions under which a bandlimited graphon signal can be represen
Adaptive $Q$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning
Offline reinforcement learning (RL) has progressed with return-conditioned supervised learning (RCSL), but its lack of stitching ability remains a limitation. We introduce $Q$-Aided Conditional Supervised Learning (QCS), which effectively combines the stability of RCSL with the stitching capability of $Q$-functions. By analyzing $Q$-function over-generalization, which impairs stable stitching, QCS adaptively integrat
Partially Recentralization Softmax Loss for Vision-Language Models Robustness
As Large Language Models make a breakthrough in natural language processing tasks (NLP), multimodal technique becomes extremely popular. However, it has been shown that multimodal NLP are vulnerable to adversarial attacks, where the outputs of a model can be dramatically changed by a perturbation to the input. While several defense techniques have been proposed both in computer vision and NLP models, the multimodal r
From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems, the field still lacks a comprehensive theoretical framework. This article aims to address this gap by delving into the intricate interplay between initialization and activation, prov
Computational lexical analysis of Flamenco genres
Flamenco, recognized by UNESCO as part of the Intangible Cultural Heritage of Humanity, is a profound expression of cultural identity rooted in Andalusia, Spain. However, there is a lack of quantitative studies that help identify characteristic patterns in this long-lived music tradition. In this work, we present a computational analysis of Flamenco lyrics, employing natural language processing and machine learning t
Latent diffusion models for parameterization and data assimilation of facies-based geomodels
Geological parameterization entails the representation of a geomodel using a small set of latent variables and a mapping from these variables to grid-block properties such as porosity and permeability. Parameterization is useful for data assimilation (history matching), as it maintains geological realism while reducing the number of variables to be determined. Diffusion models are a new class of generative deep-learn
MVGT: A Multi-view Graph Transformer Based on Spatial Relations for EEG Emotion Recognition
Electroencephalography (EEG), a technique that records electrical activity from the scalp using electrodes, plays a vital role in affective computing. However, fully utilizing the multi-domain characteristics of EEG signals remains a significant challenge. Traditional single-perspective analyses often fail to capture the complex interplay of temporal, frequency, and spatial dimensions in EEG data. To address this, we
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
The dynamics of probability density functions have been extensively studied in computational science and engineering to understand physical phenomena and facilitate algorithmic design. Of particular interest are dynamics formulated as gradient flows of energy functionals under the Wasserstein metric. The development of functional inequalities, such as the log-Sobolev inequality, plays a pivotal role in analyzing the
What Are Good Positional Encodings for Directed Graphs?
Positional encodings (PEs) are essential for building powerful and expressive graph neural networks and graph transformers, as they effectively capture the relative spatial relationships between nodes. Although extensive research has been devoted to PEs in undirected graphs, PEs for directed graphs remain relatively unexplored. This work seeks to address this gap. We first introduce the notion of Walk Profile, a gene
Machine learning approach for vibronically renormalized electronic band structures
We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural
Shadows, formed by the occlusion of light, play an essential role in visual perception and directly influence scene understanding, image quality, and visual realism. This paper presents a unified survey and benchmark of deep-learning-based shadow detection, removal, and generation across images and videos. We introduce consistent taxonomies for architectures, supervision strategies, and learning paradigms; review maj
Weight Conditioning for Smooth Optimization of Neural Networks
In this article, we introduce a novel normalization technique for neural network weight matrices, which we term weight conditioning. This approach aims to narrow the gap between the smallest and largest singular values of the weight matrices, resulting in better-conditioned matrices. The inspiration for this technique partially derives from numerical linear algebra, where well-conditioned matrices are known to facili
A DNN Biophysics Model with Topological and Electrostatic Features
In this project, we present a deep neural network (DNN)-based biophysics model that uses multi-scale and uniform topological and electrostatic features to predict protein properties, such as Coulomb energies or solvation energies. The topological features are generated using element-specific persistent homology (ESPH) on a selection of heavy atoms or carbon atoms. The electrostatic features are generated using a nove
Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models
Research has repeatedly demonstrated that intermediate hidden states extracted from large language models are able to predict measured brain response to natural language stimuli. Yet, very little is known about the representation properties that enable this high prediction performance. Why is it the intermediate layers, and not the output layers, that are most capable for this unique and highly general transfer task?
Neural Radiance Fields (NeRF) have been adapted for indoor 3D Object Detection (3DOD), offering a promising approach to indoor 3DOD via view-synthesis representation. But its implicit nature limits representational capacity. Recently, 3D Gaussian Splatting (3DGS) has emerged as an explicit 3D representation that addresses the limitation. This work introduces 3DGS into indoor 3DOD for the first time, identifying two m
Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data
Epilepsy affects around 50 million people globally. Electroencephalography (EEG) or Magnetoencephalography (MEG) based spike detection plays a crucial role in diagnosis and treatment. Manual spike identification is time-consuming and requires specialized training that further limits the number of qualified professionals. To ease the difficulty, various algorithmic approaches have been developed. However, the existing
LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space
Detecting anomalies such as an incorrect combination of objects or deviations in their positions is a challenging problem in unsupervised anomaly detection (AD). Since conventional AD methods mainly focus on local patterns of normal images, they struggle with detecting logical anomalies that appear in the global patterns. To effectively detect these challenging logical anomalies, we introduce Logical Anomaly Detectio
Notable events recorded on this day and month across all years.