Record 14082026 · captured 2026-08-25
The world looked up Prichard Colón. 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.
Prichard Colón Meléndez was a Puerto Rican professional boxer, honorary WBC World Champion, and gold medal winner at the 2010 Pan American Youth Championship in the 64 kg category. After a 2015 match with Terrel Williams, in which he was repeatedly struck in t
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
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
A by-election for the United Kingdom parliamentary constituency of Clacton was held on 13 August 2026, following the resignation of Nigel Farage, its member of Parliament. Farage, who is the leader of Reform UK, had represented Clacton since the 2024 general e
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.
Brian Djomeni Madjo is a professional footballer who plays as a forward for Premier League club Aston Villa.
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
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
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
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.
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
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
Count Binface is a novelty candidate persona adopted by the British writer and comedian Jon Harvey to contest British elections. Binface is presented as an "intergalactic space warrior" who wears a dustbin-shaped helmet.
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
Natalie Harp is an American political aide and former television anchor who has served as special assistant and executive assistant to the President of the United States since 2025.
Google LLC is an American multinational technology corporation focused on information technology, online advertising, search engine technology, email, cloud computing, software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (
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
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
Reacher is an American action crime television series developed by Nick Santora for Amazon Prime Video. Based on the Jack Reacher novel series by Lee Child, it stars Alan Ritchson as the title character, a self-proclaimed drifter and former U.S. Army military
Amy Hunt is an English sprinter. She won the gold medal in the 100 metres, 200 metres, 4 × 100 metres relay, and 4 x 100 metres mixed relay at the 2026 European Championships, becoming the first athlete to win four gold medals at a single European Championship
List of highest-grossing films
Films generate income from several revenue streams, including theatrical exhibition, home video, television broadcast rights, and merchandising. However, theatrical box-office earnings are the primary metric for trade publications in assessing the success of a
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
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
Thomas Stanley Holland is a British actor. His accolades include a BAFTA Award as well as two Critics' Choice Awards nominations. Holland's films as a leading actor have grossed over $14.9 billion worldwide, making him the Fourth highest-grossing actor of all
Mark English is an Irish middle-distance runner. He is the Irish national record holder over 800 metres indoors and the 2026 European Champion in 800 metres, the first Irish male athlete to win an individual gold medal in the history of the Championships.
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
Matthew Rhys is a Welsh actor. Known for his roles on stage and screen, he has received various accolades including a Primetime Emmy Award as well as nominations for two Actor Awards, three BAFTA Awards, and four Golden Globe Awards.
Keri Lynn Russell is an American actress. Working mainly in dramatic television since the 1990s, she has received eight nominations for the Critics' Choice Television Award for Best Actress in a Drama Series. She won a Golden Globe Award in 1999 for her lead r
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),
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection
User-generated content (e.g., tweets and profile descriptions) and shared content between users (e.g., news articles) reflect a user's online identity. This paper investigates whether correlations between user-generated and user-shared content can be leveraged for detecting disinformation in online news articles. We develop a multimodal learning algorithm for disinformation detection. The latent representations o
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
We study stochastic Cubic Newton methods for solving general possibly non-convex minimization problems. We propose a new framework, which we call the helper framework, that provides a unified view of the stochastic and variance-reduced second-order algorithms equipped with global complexity guarantees. It can also be applied to learning with auxiliary information. Our helper framework offers the algorithm designer hi
Interpretable ODE-style Generative Diffusion Model via Force Field Construction
For a considerable time, researchers have focused on developing a method that establishes a deep connection between the generative diffusion model and mathematical physics. Despite previous efforts, progress has been limited to the pursuit of a single specialized method. In order to advance the interpretability of diffusion models and explore new research directions, it is essential to establish a unified ODE-style g
Revolutionizing Finance with LLMs: An Overview of Applications and Insights
In recent years, Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. Built on the Transformer architecture, these models are trained on extensive datasets, enabling them to understand and generate human language effectively. In the financial domain, the deployment of LLMs is gaining momentum. These models are being utilized for automating financial re
The United States Food and Drug Administration's (FDA's) 510(k) pathway allows manufacturers to gain medical device approval by demonstrating substantial equivalence to a legally marketed device. However, the inherent ambiguity of this regulatory procedure has been associated with high recall among many devices cleared through this pathway, raising significant safety concerns. In this paper, we develop a comb
Langevin dynamics for high-dimensional optimization: the case of multi-spiked tensor PCA
We study nonconvex optimization in high dimensions through Langevin dynamics, focusing on the multi-spiked tensor PCA problem. In this tensor estimation model, the goal is to recover a finite number of hidden signal vectors, or spikes, from noisy Gaussian tensor observations using maximum likelihood estimation. We characterize the number of samples required for Langevin dynamics to efficiently recover the spikes and
SelfDRSC++: Self-Supervised Dual Reversed Rolling Shutter Correction via Video Interpolation
Modern consumer cameras often use rolling shutter, capturing scenes row-by-row and causing distortion in dynamic scenes. Existing correction methods rely on supervised learning with high-frame-rate global shutter images as ground truth. We propose SelfDRSC++, a self-supervised framework for RS distortion correction {from simultaneously captured top-to-bottom and bottom-to-top RS images}. A lightweight network with a
A New First-Order Meta-Learning Algorithm with Convergence Guarantees
Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems. While Model-Agnostic Meta-Learning (MAML) is a leading approach, it suffers from significant computational and memory overhead due to the requirement of computing second-order meta-gradients. We propose \textbf{FO-B-MAML}, a novel first-order variant of MAML derived from a bi-level optimization perspective. Our framework
Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for developer productivity and participation in software development. To explore its impact on collaborative open-source software (OSS) development, we investigate the role of GitHub Copilot, a generative AI pair programmer, in OSS development where multiple distributed developers volu
To evaluate a multi-representational framework in which large language model (LLM)-generated expert summaries of intensive care unit (ICU) notes are fused with physiology for in-hospital mortality (IHM) prediction, and to determine how much of the resulting gain is non-redundant with the notes themselves. Using MIMIC-III (19,211 first ICU stays, 12.83% mortality), we encoded 48-hour physiology, clinical notes, and LL
Background: Clinical trials are essential to advancing cancer treatments, but fewer than 10% of adults with cancer enroll in therapeutic trials. Open-source AI trial matching tools could democratize access to trial options. Methods: We created MatchMiner-AI, co-developed with practicing clinical oncologists and trained on synthetic electronic health record (EHR) data. It uses open-weight LLMs to summarize patient his
Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to reveal ``true'' causality. In this paper, we critique the premise of causal learning by considering the epistemology of causality across disciplines, applying the Ordinary Language method of an anthropological investigation of customary word use in reasoning about
Cueless EEG imagined speech for subject identification: dataset and benchmarks
Electroencephalogram (EEG) signals have emerged as a promising modality for biometric identification. While previous studies have explored the use of imagined speech with semantically meaningful words for subject identification, most have relied on additional visual or auditory cues. In this study, we introduce a cueless EEG-based imagined speech paradigm, where subjects imagine the pronunciation of semantically mean
Unmasking Conversational Bias in AI Multiagent Systems
Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings. However, the majority of existing methodologies for identifying biases in generated text consider the models in isolation and neglect their contextual applications. Specifically, the biases that may arise in multi-agent systems involving generative models rem
Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use experimental resources to make better inferences. Various stochastic gradient-based BOED methods have been proposed as an alternative to Bayesian optimization and other experimental design heuristi
Regularization can make diffusion models more efficient
Diffusion models are one of the key architectures of generative AI. Their main drawback, however, is the computational costs. This study indicates that the concept of sparsity, well known especially in statistics, can provide a pathway to more efficient diffusion pipelines. Our mathematical guarantees prove that sparsity can reduce the input dimension's influence on the computational complexity to that of a much
Yes, Q-learning Helps Offline In-Context RL
Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL settings. In this study, we explore the integration of RL objectives within an offline ICRL framework. Through experiments on more than 150 GridWorld and MuJoCo environment-derived datasets, we demonstrate that optimizing RL objectives directl
Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries
The ubiquity and widespread use of digital and online technologies have transformed mental health support, with online mental health communities (OMHCs) providing safe spaces for peer support. More recently, generative AI and large language models (LLMs) have introduced new possibilities for scalable, around-the-clock mental health assistance that could potentially augment and supplement the capabilities of OMHCs. Al
Multiview Representation Learning via Distributed Joint Latent Space Structuring
We study distributed multiview representation learning, a problem in which $K$ clients each observe a distinct but possibly statistically correlated view. The clients independently extract local representations from their views, which are then used by a central decoder for joint target estimation. One central difficulty is that, since the clients are not allowed to communicate with each other, they must autonomously
Unsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance
Retinal image registration, particularly for color fundus images, is a challenging yet essential task with diverse clinical applications. Existing registration methods for color fundus images typically rely on keypoints and descriptors for alignment; however, a significant limitation is their reliance on labeled data, which is particularly scarce in the medical domain. In this work, we present a novel unsupervised re
Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration
Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even more significant in the medical domain, motivating the use of unsupervised learning. Therefore, in this work, we develop a novel unsupervised descriptor learning method that does not rely on keypoint detection. This enables the resulting descriptor network to be agnostic to the keypoint detec
Computational fluid dynamics (CFD) has been the main workhorse of computational physics, yet its steep learning curve and fragmented, multi-stage workflow create significant barriers to entry. We present Foam-Agent, a multi-agent framework that leverages large language models (LLMs) to automate the end-to-end CFD workflow in OpenFOAM from a single natural-language prompt. Foam-Agent rests on three methodological cont
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (Claude 3.5 Sonnet and DeepSeek v3) against humans who had incentives to persuade, using an interactive, real-time conversational setting. We demonstrate that LLMs persuasive superiority is context-dependent: it depends on whether the persua
Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision
Efficiently adapting large pretrained models is critical under tight compute and memory budgets. While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA achieve efficiency through low-rank updates, their discrete rank constraint limits fine-grained parameter control and confines adaptations to low-dimensional subspaces. We propose Wavelet Fine-Tuning (WaveFT), which learns sparse updates in the wavelet domain
Accelerated Markov Chain Monte Carlo Algorithms on Discrete States
We propose a class of discrete state sampling algorithms based on Nesterov's accelerated gradient method, which extends the classical Metropolis-Hastings (MH) algorithm. The evolution of the discrete states probability distribution governed by MH can be interpreted as a gradient descent direction of the Kullback--Leibler (KL) divergence, via a mobility function and a score function. Specifically, this gradient is
Notable events recorded on this day and month across all years.