Record 13082026 · captured 2026-08-25
The world looked up Joshua Kushner. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
Complete record JSON
What the most people looked up, ranked by Wikipedia pageviews for that day.
Joshua Kushner is an American businessman and venture capitalist. He is a founder and managing partner of the venture capital firm Thrive Capital, co-founder and vice-chairman of Oscar Health, and the youngest son of Charles Kushner. He is the younger brother
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
Lucy Clare Davis is an English actress and comedian known for playing Dawn Tinsley in the BBC mockumentary television sitcom The Office (2001–2003), Hilda Spellman in the Netflix supernatural horror television series Chilling Adventures of Sabrina (2018–2020),
Solar eclipse of August 12, 2026
A total solar eclipse occurred at the Moon's descending node of orbit on Wednesday, 12 August 2026, with a magnitude of 1.0386. Totality occurred in a narrow path across Earth's surface, with the partial solar eclipse visible over a surrounding region thousand
Brian Djomeni Madjo is a professional footballer who plays as a forward for Premier League club Aston Villa.
List of solar eclipses visible from the British Isles
A solar eclipse occurs when the Moon passes between Earth and the Sun, thereby totally or partially obscuring Earth's view of the Sun. Below is a complete list of total and annular eclipses visible anywhere within the modern extent of the United Kingdom betwee
David Crowley (Wisconsin politician)
David Carl Crowley is an American politician who has served as the seventh executive of Milwaukee County since 2020. A member of the Democratic Party, he previously represented Wisconsin's 17th Assembly district, including the west side of Milwaukee, from 2017
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
Francesca Hong is an American politician, former chef, and business owner who has represented Wisconsin's 76th Assembly district in the Wisconsin State Assembly since 2021. She is a member of the Democratic Party and the Democratic Socialists of America (DSA).
List of solar eclipses in the 21st century
During the 21st century, there will be 224 solar eclipses of which 77 will be partial, 72 will be annular, 68 will be total and 7 will be hybrids between total and annular eclipses. Of these, two annular and one total eclipse will be non-central, in the sense
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
Karlie Elizabeth Kloss is an American supermodel and entrepreneur. She was a Victoria's Secret Angel from 2013 until 2015, when she enrolled as a student at New York University. By 2019, Kloss had appeared on 40 international Vogue covers. She has signed with
Robert Alan Iger is an American media executive who was the chief executive officer (CEO) of the Walt Disney Company twice, from 2005 to 2020 and from 2022 to 2026. He previously was the president of the American Broadcasting Company (ABC) between 1994 and 199
Solar eclipse of August 2, 2027
A total solar eclipse, nicknamed the Eclipse of the Century, will occur at the Moon's descending node of orbit on Monday, August 2, 2027, with a magnitude of 1.079. A solar eclipse occurs when the Moon passes between Earth and the Sun, thereby totally or partl
Jared Corey Kushner is an American businessman, investor, government official and politician. Through his marriage to Ivanka Trump, he is the son-in-law of the 45th and 47th president of the United States Donald Trump. Kushner has served in both official and i
The Last House is a 2026 American science fiction horror film written by Matthew Robinson, and directed by Louis Leterrier. It stars Greta Lee and Wagner Moura. The film follows a family that finds themselves inexplicably sealed in their home, with the whole w
Charles Kushner is an American real estate developer, diplomat, and former attorney serving since 2025 as the United States ambassador to France and Monaco. He founded Kushner Companies in 1985.
A solar eclipse occurs when the Moon passes between Earth and the Sun, thereby obscuring the view of the Sun from a small part of Earth, totally or partially. Such an alignment occurs approximately every six months, during the eclipse season in its new moon ph
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
Verity is a 2018 psychological romantic thriller novel by American author Colleen Hoover. The narrative follows Lowen Ashleigh, a writer who is hired to complete a bestselling book series after its original author, Verity Crawford, is left unable to continue d
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 End of Oak Street is a 2026 American science fiction survival film written, co-produced, and directed by David Robert Mitchell. It stars Anne Hathaway, Ewan McGregor, Maisy Stella and Christian Convery as a family whose suburban neighborhood has been trans
Karoline Claire Leavitt is an American political spokesperson who has served as the 36th White House press secretary since 2025. A member of the Republican Party, she was the party's nominee in the 2022 election for New Hampshire's 1st congressional district.
Robert Norman Davis, known by his stage name Jasper Carrott, is an English comedian, writer, actor, singer and television presenter. His credits include An Audience with Jasper Carrott (1978), The Secret Policeman's Other Ball (1982), Carrott's Lib (1982–1983)
Russell Westbrook III is an American former professional basketball player who played 18 seasons in the National Basketball Association (NBA). Known for his agility, intensity and explosiveness, he is considered one of the greatest point guards in NBA history.
DC is a 2026 Indian Tamil-language romantic action film directed by Arun Matheswaran and produced by Kalanithi Maran's Sun Pictures. The film stars Lokesh Kanagaraj, Wamiqa Gabbi and Sanjana Krishnamoorthy. It follows Das, an outlaw and Chandra, a brutalised s
Abdulrahman Mohamed El-Sayed, commonly known as Abdul El-Sayed, is an American politician and epidemiologist who is the Democratic nominee in the 2026 United States Senate election in Michigan. A progressive member of the Democratic Party, El-Sayed was a candi
2026 Wisconsin gubernatorial election
The 2026 Wisconsin gubernatorial election is scheduled to take place on November 3, 2026, to elect the governor of Wisconsin. Democratic Milwaukee County executive David Crowley and Republican U.S. representative Tom Tiffany are the nominees for their respecti
Solar eclipse of August 11, 1999
A total solar eclipse occurred at the Moon's ascending node of orbit on Wednesday, August 11, 1999, with a magnitude of 1.0286. This was one of the most-viewed total solar eclipses in human history. A solar eclipse is when the Moon passes between the Earth and
Margaret Flanagan is an American politician and Ojibwe activist serving as the 50th lieutenant governor of Minnesota since 2019. A member of the Minnesota Democratic–Farmer–Labor Party (DFL), Flanagan served in the Minnesota House of Representatives from 2015
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Soft-Attention Improves Skin Cancer Classification Performance
In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network toachieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performanc
Joint AP Probing and Scheduling: A Contextual Bandit Approach
We consider a set of APs with unknown data rates that cooperatively serve a mobile client. The data rate of each link is i.i.d. sampled from a distribution that is unknown a priori. In contrast to traditional link scheduling problems under uncertainty, we assume that in each time step, the device can probe a subset of links before deciding which one to use. We model this problem as a contextual bandit problem with pr
Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor
Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure. This paper presents Chat Incremental Pattern Constructor (ChatIPC), a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by simi
Online Learning for Adaptive Probing and Scheduling in Dense WLANs
Existing solutions to network scheduling typically assume that the instantaneous link rates are completely known before a scheduling decision is made or consider a bandit setting where the accurate link quality is discovered only after it has been used for data transmission. In practice, the decision maker can obtain (relatively accurate) channel information, e.g., through beamforming in mmWave networks, right before
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. Howe
Un-EVIMO: Unsupervised Event-Based Independent Motion Segmentation
Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power consumption. Because of these properties, they are well-suited for processing fast motions that require rapid reactions. Although event cameras have recently shown competitive performance in unsupervised optical flow estimation, performance in detecting independently moving
Weak Correlations as the Underlying Principle for Linearization of Gradient-Based Learning Systems
Deep learning models, such as wide neural networks, can be conceptualized as nonlinear dynamical physical systems characterized by a multitude of interacting degrees of freedom. Such systems in the infinite limit, tend to exhibit simplified dynamics. This paper delves into gradient descent-based learning algorithms, that display a linear structure in their parameter dynamics, reminiscent of the neural tangent kernel.
A Rate-Distortion-Classification Approach for Lossy Image Compression
In lossy image compression, the objective is to achieve minimal signal distortion while compressing images to a specified bit rate. The increasing demand for visual analysis applications, particularly in classification tasks, has emphasized the significance of considering semantic distortion in compressed images. To bridge the gap between image compression and visual analysis, we propose a Rate-Distortion-Classificat
Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis
Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. Existing deep learning models tend to overlook the semantic interdependencies among activities and households and rely on restricted GPS data, while activity-based models depend on rigid assumptions and extensive data, making them costly and difficult to adapt to new regions, es
qMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model
Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffuser, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabil
Causal Agent based on Large Language Model
The large language model (LLM) has achieved significant success across various domains. However, the inherent complexity of causal problems and causal theory poses challenges in accurately describing them in natural language, making it difficult for LLM to comprehend and use them effectively. Causal methods are not easily conveyed through natural language, which hinders LLM's ability to apply them accurately. Add
Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Existing methods largely rely on direct alignment, which often fails to capture complex cross-modal rela
ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation
AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our framework incorporates ReXGradient, the largest test dataset co
Heterogeneous transfer learning for high-dimensional regression with feature mismatch
We study Heterogeneous Transfer Learning (HTL) for high-dimensional regression with differing feature sets. Such feature mismatch arises when some variables available in a data-rich source domain are unavailable in a data-poor target domain. Yet most homogeneous TL methods require the same feature space in both the source and target domains, limiting their practical applicability. Conversely, existing HTL methods lac
Explainability in Practice: A Survey of Explainable NLP Across Various Domains
Natural Language Processing (NLP) is now embedded in critical sectors including healthcare, finance, and customer relationship management, where models such as GPT-4o, Gemini, and BERT increasingly inform decisions. The black-box nature of these models has created an urgent need for transparency. This review examines explainable NLP (XNLP) as it is actually deployed, working through seven application domains: medicin
A Variational Analysis of Kernel Learning with Learnable Linear Transformations
The classical kernel ridge regression problem aims to find the best fit for the output $Y$ as a function of the input data $X\in \mathbb{R}^d$, with a fixed choice of regularization term imposed by a given choice of a reproducing kernel Hilbert space, such as a Sobolev space. Here we consider a generalization of the kernel ridge regression problem, by introducing an extra matrix parameter $U$, which aims to detect th
Gait refers to the patterns of limb movement generated during walking, which are unique to each individual due to both physical and behavioral traits. Walking patterns have been widely studied in biometrics, biomechanics, sports, and rehabilitation. While traditional methods rely on video and motion capture, advances in plantar pressure sensing technology now offer deeper insights into gait. However, underfoot pressu
On Benchmarking Human-Like Intelligence in Machines
Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition. However, we argue that many current evaluation paradigms for AI are insufficient for assessing human-like cognitive capabilities in these models. We identify a set of key shortcomings: a lack of human-
Proportional Committee Elections with Positive and Negative Votes
In the classic committee election setting each voter approves a subset of candidates and the goal is to select $k$ winners based on these preferences. A central focus of recent research in the area has been to achieve proportional representation. In this work, we explore notions of proportionality in a more expressive setting that allows voters to vote against candidates---a common feature on online polling platforms
Program Semantic Inequivalence Game with Large Language Models
Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics. Finding training examples to teach LLMs to solve these tasks can be challenging. In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game (SInQ): a generator agent crea
3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as immersive media, robotics, autonomous driving, and embodied AI. Early methods based on procedural rules offered scalability but limited diversity. Recent advances in deep generative models (e.g., GANs, diffusion models) and 3D representations (e.g., NeRF, 3D Gaussians) have e
While Large Language Models (LLMs) can generate fluent and convincing responses, they are not necessarily correct. This is especially apparent in the popular decompose-then-verify factuality evaluation pipeline, where LLMs evaluate generations by decomposing the generations into individual, valid claims. Factuality evaluation is especially important for medical answers, since incorrect medical information could serio
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases
Biomedical researchers increasingly rely on large-scale structured databases for complex analytical tasks. However, current text-to-SQL systems often struggle to map qualitative scientific questions into executable SQL, particularly when implicit domain reasoning is required. We introduce BiomedSQL, the first benchmark explicitly designed to evaluate scientific reasoning in text-to-SQL generation over a real-world bi
Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms
Solving systems of polynomial equations, particularly those with finitely many solutions, is a crucial challenge across many scientific fields. Traditional methods like Gröbner and Border bases are fundamental but suffer from high computational costs, which have motivated recent Deep Learning approaches to improve efficiency, albeit at the expense of output correctness. In this work, we introduce the Oracle Border Ba
Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons
We propose a new ternary spiking neuron model to improve the representation capacity of binary spiking neurons in deep Q-learning. Although a ternary neuron model has recently been introduced to overcome the limited representation capacity offered by binary spiking neurons, we show that its performance is worse than that of binary models in deep Q-learning tasks, contradicting previous findings from recent studies. T
Notable events recorded on this day and month across all years.