Record 16062026 · captured 2026-08-25
The world looked up Oliver Tree. 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.
Oliver Tree Nickell was an American singer-songwriter, rapper, and record producer. Born in Santa Cruz, California, Tree signed to Atlantic Records in 2017 after his song "When I'm Down" went viral. He released his debut studio album Ugly Is Beautiful in July
Cape Verde, also referred to in English by its Portuguese name Cabo Verde, and known officially as the Republic of Cabo Verde, is an archipelagic country in the eastern Atlantic Ocean, off the coast of West Africa. It consists of ten volcanic islands with a co
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
UFC Freedom 250 was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on June 14, 2026, on the South Lawn of the White House in Washington, D.C., United States. The event's name is a reference to the 250th anniversary of
Yasin Ayari is a Swedish professional footballer who plays as a midfielder for Premier League club Brighton & Hove Albion and the Sweden national team.
Justin Ray Gaethje is an American professional mixed martial artist. A professional since 2011, he competes in the lightweight division of the Ultimate Fighting Championship (UFC), where he is the current UFC Lightweight Champion. He is the first fighter in UF
Ilia Topuria is a Georgian and Spanish professional mixed martial artist. He currently competes in the Lightweight division of the Ultimate Fighting Championship (UFC), where he is the former UFC Lightweight Champion and Featherweight Champion, becoming the fi
Cape Verde national football team
The Cape Verde national football team, recognised as Cabo Verde by FIFA, represents Cape Verde in men's international football, and is controlled by the Cape Verdean Football Federation. Nicknamed Blue Sharks, the national team played its first match on 19 Apr
Gaspar Prim Díaz, better known as Gaspi, was an Argentine YouTuber and Internet personality.
Joshua Seth Hokit is an American professional mixed martial artist who currently competes in the Heavyweight division of the Ultimate Fighting Championship (UFC). He formerly competed in Bellator MMA. As of July 25, 2026, he is #6 in the Meta UFC heavyweight r
Jalen Marquis Brunson, nicknamed "Captain Clutch", and the "King of New York" is an American professional basketball player for the New York Knicks of the National Basketball Association (NBA). The son of former NBA guard Rick Brunson, he played college basket
Josimar José Évora Dias, commonly known by the nickname Vozinha, is a Cape Verdean professional footballer who plays as a goalkeeper for Liga de Primera club Colo-Colo and the Cape Verde national team.
Curaçao, officially the Country of Curaçao, is a constituent country within the Kingdom of the Netherlands. It is an island country located in the southern Caribbean Sea, specifically the Dutch Caribbean region, about 65 km (40 mi) north of Venezuela and 80 km
2026 Rio de Janeiro mid-air collision
On 14 June 2026, two helicopters collided mid-air in the Recreio dos Bandeirantes neighborhood, in the southwest zone of Rio de Janeiro, Brazil. The victims were American singer-songwriter Oliver Tree, Argentine YouTuber Gaspi, Argentine director and screenwri
.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
Murder of Reagan Simmons-Hancock
On October 9, 2020, 21-year-old Reagan Simmons-Hancock was murdered by 27-year-old Taylor Rene Parker in New Boston, Texas, United States. Parker then cut from Reagan's body her unborn daughter, Braxlynn Sage Hancock, who died the same day. Parker had previous
Romelu Lukaku Bolingoli is a Belgian professional footballer who plays as a striker for Süper Lig club Fenerbahçe and the Belgium national team. Lukaku ranks second for the all-time European men's top goalscorers in international football, with 93 goals.
The FIFA World Cup is an international association football competition among the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The tournament has been held every
Disclosure Day is a 2026 American science fiction thriller film directed and produced by Steven Spielberg from a screenplay by David Koepp, based on a story by Spielberg. The film stars an ensemble cast, including Emily Blunt, Josh O'Connor, Colin Firth, Eve H
Lucas Alejandro Vignale was an Argentine director and screenwriter. He was best known for co-writing and co-directing the 2026 coming-of-age drama film The River Train along with Lorenzo Ferro.
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
Zion Suzuki is a professional footballer who plays as a goalkeeper for Premier League club Aston Villa. Born in the United States, he represents the Japan national team.
Roderic Jean Brind'Amour is a Canadian professional ice hockey coach and former player who is the head coach for the Carolina Hurricanes of the National Hockey League (NHL). Nicknamed "Rod the Bod" and "Mr. Hurricane", Brind'Amour has played or coached for the
The FIFA Men's World Ranking is a ranking system for men's national teams in association football, first introduced in December 1992. The men's teams of the member nations of FIFA, football's world governing body, are ranked based on their game results with th
Jordan Staal is a Canadian professional ice hockey player who is a centre and captain for the Carolina Hurricanes of the National Hockey League (NHL). He is regarded as a premier penalty-killer and skilled two-way forward. In 2007, he became the youngest playe
Ciryl Romain Gane is a French professional mixed martial artist, actor, and former Muay Thai fighter. He currently competes in the Heavyweight division of the Ultimate Fighting Championship (UFC), where he is the current and two-time Interim UFC Heavyweight Ch
Lamine Yamal Nasraoui Ebana, commonly known as Lamine Yamal, is a Spanish professional footballer who plays as a right winger for the La Liga club Barcelona and the Spain national team. He is widely regarded as one of the best players in the world.
Geeta and Sanjay Chopra kidnapping case
The Geeta and Sanjay Chopra kidnapping case was a kidnapping and murder crime in New Delhi in 1978. It involved the kidnapping and subsequent murder of siblings Geeta and Sanjay by Kuljeet Singh and Jasbir Singh. Although the children were kidnapped for ransom
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
The Carolina Hurricanes are a professional ice hockey team based in Raleigh, North Carolina. The Hurricanes compete in the National Hockey League (NHL) as a member of the Metropolitan Division in the Eastern Conference. The team plays its home games at the Len
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation
We introduce HK-LegiCoST, a new three-way parallel corpus of Cantonese-English translations, containing 600+ hours of Cantonese audio, its standard traditional Chinese transcript, and English translation, segmented and aligned at the sentence level. We describe the notable challenges in corpus preparation: segmentation, alignment of long audio recordings, and sentence-level alignment with non-verbatim transcripts. Su
Attention, not scale, drives human-AI alignment in multimodal language prediction
Humans routinely draw on visual context to predict upcoming words. To what extent current vision-language models produce comparable behaviour is unclear. Here we placed five state-of-the-art pretrained systems side-by-side with 600 human participants in a web-based Visual-World Paradigm. On each of 100 six-second movie clips, models and participants received either text only or synchronised video and text and judged
Deep neural networks (DNNs) show great promise for solving partial differential equations (PDEs), but their deep architectures introduce complex, large-scale, non-convex optimization challenges. Nonlinear PDEs, like the viscous Burgers' equation, compound these difficulties due to steep gradients and shock-like solutions. To address this, we propose a two-stage multi-grade deep learning (TS-MGDL) method. In the f
DURENDAL: Graph deep learning framework for temporal heterogeneous networks
Temporal heterogeneous networks (THNs) are evolving networks that characterize many real-world applications such as citation and events networks, recommender systems, and knowledge graphs. Although different Graph Neural Networks (GNNs) have been successfully applied to dynamic graphs, most of them only support homogeneous graphs or suffer from model design heavily influenced by specific THNs prediction tasks. Furthe
It's About Time: Temporal References in Emergent Communication
Emergent communication enables agents to develop bespoke languages that improve communication efficiency. Despite the known importance of temporal structure in natural language, there is no existing evidence of temporal references in emergent communication. This paper addresses this gap, by exploring how agents communicate about temporal relationships. We analyse three potential factors for the emergence of temporal
Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multi-task model that performs well across diverse tasks. Recent research, exemplified by task arithmetic, highlights that this multi-task model can be derived through arithmetic operations on task vectors. Nevertheless, current merging
SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction
In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks. While pharmacological treatment is effective for some epilepsy patients (with ~65M people affected worldwide), one out of three suffer from drug-resistant epilepsy. To alleviate the impact of seizure, predictive systems have been developed that can n
Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. With distinctive advantages across various application scenarios, numerous 3D skeleton based person re-identification (SRID) methods with diverse skeleton modeling and learning paradigms have been proposed in recent years. In this paper, we provide a comprehensi
As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmentation (DA). Conducting DA in private domain requires private protection approaches (i.e. anonymization and perturbation), but those methods cannot provide protection guarantees. Differential privacy (DP) learning methods theoretically boun
Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-s
Design and Scheduling of an AI-based Queueing System
To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay of other jobs. Motivated by applications where prediction models interact with human servers (e.g., content moderation), we consider a large queueing system comprising of many single server queues where the class of a job is estimated using
Speaking Your Language: Spatial Relationships in Interpretable Emergent Communication
Effective communication requires the ability to refer to specific parts of an observation in relation to others. While emergent communication literature shows success in developing various language properties, no research has shown the emergence of such positional references. This paper demonstrates how agents can communicate about spatial relationships within their observations. The results indicate that agents can
MixTeX: Data-Efficient LaTeX OCR via Synthetic Pretraining and Limited Fine-Tuning
LaTeX OCR converts scientific document images into editable LaTeX code. Existing systems rely on large paired datasets, which are costly to collect and limited for low-resource languages. This paper presents MIXTEX, a data-efficient system using synthetic pretraining without real LaTeX sources. Unlike Nougat that depends on arXiv datasets, we generate training data by randomly pairing grammatical Wikipedia text with
Mitigating scalability challenges in LUT-based neural networks via pruning optimisations
Modern deep neural networks heavily rely on a large number of multiply-accumulate operations, which constitute the predominant computational cost. To address this, Look-Up Table (LUT)-based matrix multiplications have emerged as a promising alternative for reducing the computational cost and time of the multiply-accumulate operations in a neural network. However, the LUT-based neural network still faces the scalabili
Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustment
Semi-supervised learning (SSL) algorithms often struggle to perform well when trained on imbalanced data. In such scenarios, the generated pseudo-labels tend to exhibit a bias toward the majority class, and models relying on these pseudo-labels can further amplify this bias. Existing imbalanced SSL algorithms explore pseudo-labeling strategies based on either pseudo-label refinement (PLR) or threshold adjustment (THA
Metacognitive Myopia in Large Language Models
Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups. We propose metacognitive myopia as a cognitive-ecological framework accounting for a conglomerate of established and emerging LLM biases. Our theoretical framework posits that biased samples in the information environment cause f
A Functional Trade-off between Prosodic and Semantic Cues in Conveying Sarcasm
This study investigates the acoustic features of sarcasm and disentangles the interplay between the propensity of an utterance being used sarcastically and the presence of prosodic cues signaling sarcasm. Using a dataset of sarcastic utterances compiled from television shows, we analyze the prosodic features within utterances and key phrases belonging to three distinct sarcasm categories (embedded, propositional, and
Ensuring Fairness with Transparent Auditing of Quantitative Bias in AI Systems
With the rapid advancement of AI, there is a growing trend to integrate AI into decision-making processes. However, AI systems may exhibit biases that lead decision-makers to draw unfair conclusions. Notably, the COMPAS system used in the American justice system to evaluate recidivism was found to favor racial majority groups; specifically, it violates a fairness standard called equalized odds. Various measures have
Best Arm Identification with Minimal Regret
Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret. This variant of the multi-armed bandit problem elegantly amalgamates two of its most ubiquitous objectives: regret minimization and BAI. More precisely, the agent's goal is to identify the best arm with a prescribed confidence level $δ$, while minimizing
Representations from large language models are highly effective at predicting BOLD fMRI responses to language stimuli. However, these representations are largely opaque: it is unclear what features of the language stimulus drive the response in each brain area. We present generative causal testing (GCT), a framework for generating concise explanations of language selectivity in the brain from predictive models and th
Investigating Role of Big Five Personality Traits in Audio-Visual Rapport Estimation
Automatic rapport estimation in social interactions is a central component of affective computing. Recent reports have shown that the estimation performance of rapport in initial interactions can be improved by using the participant's personality traits as the model's input. In this study, we investigate whether this findings applies to interactions between friends by developing rapport estimation models that
Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning
Supervised contrastive learning has achieved remarkable success by leveraging label information; however, determining positive samples in multi-label scenarios remains a critical challenge. In multi-label supervised contrastive learning (MSCL), multi-label relations are not yet fully defined, leading to ambiguity in identifying positive samples and formulating contrastive loss functions to construct the representatio
A Multi-Modal Non-Invasive Deep Learning Framework for Progressive Prediction of Seizures
This paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures through the utilization of a Deep Learning (DL) methodology based on non-invasive multi-modal sensor networks. Epilepsy, a debilitating neurological condition, affects an estimated 65 million individuals globally, with a substantial proportion facing drug-resistant epilepsy despite pharmacological
The rapid proliferation of Deep Neural Networks (DNNs) is driving a surge in model watermarking technologies, as the trained models themselves constitute valuable intellectual property. Existing watermarking approaches primarily focus on modifying model parameters or altering sampling behaviors. However, with the emergence of increasingly large models, improving the efficiency of watermark embedding becomes essential
SimSiam Naming Game: A Unified Approach for Emergent Communication and Representation Learning
Emergent Communication (EmCom) investigates how agents develop symbolic communication through interaction without predefined language. Recent frameworks, such as the Metropolis--Hastings Naming Game (MHNG), formulate EmCom as the learning of shared external representations negotiated through interaction under joint attention, without explicit success or reward feedback. However, MHNG relies on sampling-based updates
Notable events recorded on this day and month across all years.