Record 19052026 · captured 2026-08-25
The world looked up Aaron Rai. 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.
Aaron Rai is an English professional golfer who plays on the PGA Tour and the European Tour. He has won one major championship, the 2026 PGA Championship.
Murder of Dominic Russo and Davion Flanagan
The murder of Dominic Russo and Davion Flanagan occurred during the early morning hours of July 31, 2022, when Mackenzie Shirilla intentionally crashed her vehicle into a brick wall in Strongsville, Ohio, United States, killing two passengers: her boyfriend, D
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
Thomas Kane Roberts was an American voice actor. He was known for his work in animation and video games, most notably the Star Wars franchise, voicing established characters Yoda, Admiral Ackbar, Boba Fett, Qui-Gon Jinn, and C-3PO. Other notable roles include
Karuppu (transl. Black) is a 2026 Indian Tamil-language fantasy action drama film directed by RJ Balaji from a screenplay he co-wrote with Ashwin Ravichandran, Rahul Raj, T. S. Gopi Krishnan and Karan Aravind Kumar. Produced by Dream Warrior Pictures, the film
The Eurovision Song Contest 2026 was the 70th edition of the Eurovision Song Contest. It consisted of two semi-finals on 12 and 14 May and a final on 16 May 2026, held at Wiener Stadthalle in Vienna, Austria, and presented by Victoria Swarovski and Michael Ost
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
Off Campus is an American romantic drama television series created by Louisa Levy for Amazon Prime Video. It is based on the Off-Campus book series by Elle Kennedy. The series premiered on May 13, 2026 and received positive reviews. In February 2026, ahead of
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
Darina Nikolaeva Yotova, professionally known as Dara, is a Bulgarian singer and songwriter. She first rose to prominence in 2015 after reaching the final in the Bulgarian edition of The X Factor. In 2016, she released her debut single "K'vo ne chu", and in 20
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
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
Bill Harkleroad, known professionally as Zoot Horn Rollo, is an American guitarist. He is best known for his work with Captain Beefheart and The Magic Band. In 2003, he was ranked No. 62 in a Rolling Stone magazine list of "the 100 greatest guitarists of all t
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
Antoine Griezmann is a French professional footballer who plays as a forward or attacking midfielder for Major League Soccer club Orlando City. Considered to be one of the best players of his generation, he is primarily known for his versatility, game intellig
Gina Joy Carano is an American actress and mixed martial artist. She competed in Elite Xtreme Combat and Strikeforce from 2006 to 2009, where she compiled a 7–1 record. Her popularity led to her being called the "face of women's MMA", although Carano rejected
Mark Fuhrman was an American law enforcement officer, author, and commentator. As a detective for the Los Angeles Police Department (LAPD), he became known for his role in the Nicole Brown Simpson and Ron Goldman murder investigation and the subsequent prosecu
John Derek Radford is a British convicted serial sex offender, known as the Black Cab Rapist. Worboys was convicted in 2009 for attacks on 12 women, committed between 2007 and 2008. In 2019, he was convicted for attacks on four more women, the earliest of whic
Dutton Ranch is an American television series created by Chad Feehan. The series serves as both a spin-off and sequel to Yellowstone (2018–2024) and is the fifth television series in the Yellowstone franchise. It stars Kelly Reilly and Cole Hauser reprising th
Ronda Jean Rousey is an American actress, retired professional wrestler, former judoka, and former mixed martial artist. She is best known for her tenures in the Ultimate Fighting Championship (UFC) and WWE.
José Mário dos Santos Mourinho Félix is a Portuguese professional football manager and former player who is the head coach of La Liga club Real Madrid. Nicknamed "The Special One", he is one of the most decorated managers of all time. Mourinho has won league c
Ebola, also known as Ebola virus disease (EVD) and Ebola hemorrhagic fever (EHF), is a zoonotic viral hemorrhagic fever in humans and other primates, caused by four of the six known ebolaviruses. Symptoms typically start anywhere between two days and three wee
Vadasseri Damodaran Satheesan is an Indian politician and lawyer who is serving as the 13th Chief Minister of Kerala since May 2026. A member of the Indian National Congress, he has represented Paravur in the Kerala Legislative Assembly since 2001.
Philip Belmont Cameli is an American actor, best known for his roles as Jamie Spano in the Saved by the Bell reboot (2020–2021), Eli in the Netflix film Along for the Ride (2022) and Garrett Graham in the Amazon Prime Video series Off Campus (2026–present).
Ella Bright is an American-British actress and singer. She began acting as a child. For her performance in the CBBC adaptation of Malory Towers (2020–2025), she received Children's BAFTA and Emmy Award nominations. She has since starred in the Prime Video seri
Legends is a British crime thriller television series written and created by Neil Forsyth and produced by his Tannadice Pictures production company. It is a dramatisation of the true story of undercover British customs investigators who infiltrated the drug wo
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
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
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
HONEM: Learning Embedding for Higher Order Networks
Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to
Matérn Gaussian Processes on Graphs
Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties. Although many different Gaussian process models are readily available when the input space is Euclidean, the choice is much more limited for Gaussian processes whose input space is an undirected graph. In this work, we leverage the stochastic partial differentia
REMOD: Relation Extraction for Modeling Online Discourse
The enormous amount of discourse taking place online poses challenges to the functioning of a civil and informed public sphere. Efforts to standardize online discourse data, such as ClaimReview, are making available a wealth of new data about potentially inaccurate claims, reviewed by third-party fact-checkers. These data could help shed light on the nature of online discourse, the role of political elites in amplify
A Machine With Human-Like Memory Systems
Inspired by the cognitive science theory, we explicitly model an agent with both semantic and episodic memory systems, and show that it is better than having just one of the two memory systems. In order to show this, we have designed and released our own challenging environment, "the Room", compatible with OpenAI Gym, where an agent has to properly learn how to encode, store, and retrieve memories to maximize
A Machine with Short-Term, Episodic, and Semantic Memory Systems
Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released our own reinforcement learning agent environment, "the Room", where an agent has to learn how to encode, store, an
Cost-aware Duration Prediction for Software Upgrades in Datacenters
Software upgrades are critical to maintaining server reliability in datacenters. While job duration prediction and scheduling have been extensively studied, the unique challenges posed by software upgrades remain largely under-explored. This paper presents the first in-depth investigation into software upgrade scheduling at datacenter scale. We begin by characterizing various types of upgrades and then frame the sche
Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts
Scholars in the humanities rely heavily on ancient manuscripts to study history, religion, and socio-political structures in the past. Many efforts have been devoted to digitizing these precious manuscripts using OCR technology, but most manuscripts were blemished over the centuries so that an Optical Character Recognition (OCR) program cannot be expected to capture faded graphs and stains on pages. This work present
Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts
We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising oracle can only judge source. DLVA's training algorithm is general: we extend a source code analysis to bytecode without any manual feature engineering, predefined patterns, or expert rules. DLVA's training algorithm is also robust: it
Intuitive Surgical SurgToolLoc and SurgVU Challenges Results: 2022-2025
Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machine learning models and algorithms that will enable them. With these goals in mind we have invited the surgical data science community to participate in a yearly competition hosted through the Medical Imaging Computing and Computer Assisted Interventions (MICCAI) conference
Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
Introduction: Long-term time series forecasting (LTSF) has gained significant attention in recent years. While various specialized designs exist for capturing temporal dependency, recent studies have shown that even a single linear layer can achieve competitive performance. This paper investigates the intrinsic effectiveness of recent LTSF approaches and reveals the critical role of affine mapping. Materials and meth
Online Resource Allocation with Convex-set Machine-Learned Advice
Decision-makers often have access to machine-learned predictions about future demand that can help guide online resource allocation decisions. However, such predictions may be inaccurate. We develop a framework for online resource allocation with potentially unreliable machine-learned advice, where the advice is represented as a convex uncertainty set for the demand vector rather than a single point estimate. We intr
Causal Influences over Social Learning Networks
This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines the dynamics of social learning models and distributed decision-making protocols, and derives expressions that reveal the causal relations between pairs of agents and explain the flow of influence over the network. The results turn out to be dependent on the graph topology and
Optimal Control of Multiclass Fluid Queueing Networks: A Machine Learning Approach
We propose a machine learning approach to the optimal control of multiclass fluid queueing networks (MFQNETs) that provides explicit and insightful control policies. We prove that a piecewise constant optimal policy exists for MFQNET control problems, with segments separated by hyperplanes passing through the origin. We use Optimal Classification Trees with hyperplane splits (OCT-H) to learn an optimal control policy
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features
Complex emotion recognition is a cognitive task that has so far eluded the same excellent performance of other tasks that are at or above the level of human cognition. Emotion recognition through facial expressions is particularly difficult due to the complexity of emotions expressed by the human face. For a machine to approach the same level of performance in complex facial expression recognition as a human, it may
Lightweight CNN-Based DDoS Detection for Resource-Constrained Edge Networks
Distributed Denial of Service (DDoS) attacks remain a persistent threat to the availability of Internet services, edge networks, and cyber-physical infrastructure. Although recent AI-security work has increasingly focused on foundation models, autonomous agents, and adversarial robustness, many operational defense tasks still require low-latency classification close to the network edge, where cloud-scale analysis may
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is the only in vivo method to non-invasively examine the microstructure of the human heart. Current research in DT-CMR aims to improve the understanding of how the cardiac microstructure relates to the macroscopic function of the healthy heart as well as how microstructural dysfunction contributes to disease. To get the final DT-CMR metrics, we need to acquire diff
Single Image Reflection Removal with Patch Reflectance Prior
Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and reflection. Many existing methods rely on specific prior assumptions to resolve the problem. In this paper, we propose a general reflection intensity prior that captures the intensity of the reflection phenomenon and demonstrate its effectiven
Mixup Barcodes: Quantifying Geometric-Topological Interactions between Point Clouds
We combine standard persistent homology with image persistent homology to define a novel way of characterizing shapes and interactions between them. In particular, we introduce: (1) a mixup barcode, which captures geometric-topological interactions (mixup) between two point sets in arbitrary dimension; (2) simple summary statistics, total mixup and total percentage mixup, which quantify the complexity of the interact
A tutorial on learning from preferences and choices with Gaussian Processes
Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics. By understanding individuals' preferences and how they make choices, we can build products that closely match their expectations, paving the way for more efficient and personalised applications across a wide range of domains. The objective of this tutorial is to present a cohesive and comprehensive framew
Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classification of heart sounds.
A Survey on Retrieval-Augmented Text Generation for Large Language Models
Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but possibly incorrect responses by LLMs, thereby enhancing the a
Generalization analysis with deep ReLU networks for metric and similarity learning
While metric and similarity learning has been extensively studied from several theoretical perspectives, a rigorous understanding of its generalization performance is still lacking. In this paper, we investigate the generalization behavior of metric and similarity learning by exploiting the specific structure of the true metric (i.e., the target function). In particular, by deriving the explicit form of the true metr
Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
Preferential Bayesian optimization (PBO) learns latent utilities from pairwise comparisons, but most existing methods assume homoscedastic comparison noise. This is inadequate in human-in-the-loop settings, where a user may compare some designs reliably and others only hesitantly. We propose a heteroscedastic noise model for PBO: before optimization, the user provides a small set of reliable examples, called anchors,
DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning
With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential in decision-making and control. Most of these works utilized DMs to sample directly from the trajectory space, where DMs can be viewed as a combination of dynamics models and policies. In this work, we explore how to decouple DMs' ability as dynamics models in fully of
Learning Spatial-Preserving Hierarchical Representations for Digital Pathology
Whole slide images (WSIs) pose fundamental computational challenges due to their gigapixel resolution and the sparse distribution of informative regions. Existing approaches often treat image patches independently or reshape them in ways that distort spatial context, thereby obscuring the hierarchical pyramid representations intrinsic to WSIs. We introduce Sparse Pyramid Attention Networks (SPAN), a hierarchical fram
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