Record 15062026 · 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
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
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
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
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.
Eric Daniel "Rick" Brunson is an American professional basketball coach and former player who is an assistant coach for the New York Knicks of the National Basketball Association (NBA). He played college basketball for the Temple Owls. He played nine seasons i
The New York Knickerbockers, commonly called the New York Knicks, are an American professional basketball team based in the New York City borough of Manhattan. The Knicks compete in the National Basketball Association (NBA) as a member of the Atlantic Division
Aldon Jacarus Ramon Smith was an American professional football player who was an outside linebacker and defensive end for six seasons in the National Football League (NFL). He played college football for the Missouri Tigers and was selected by the San Francis
Curaçao national football team
The Curaçao national football team represents Curaçao in men's international football. The team is governed by the Federashon Futbòl Kòrsou.
Nestory Irankunda is an Australian professional soccer player who plays as a winger for Primeira Liga club Sporting CP. Born in Tanzania and raised in Adelaide, Australia, he plays for the Australia national team.
Mike Brown (basketball, born 1970)
Michael Burton Brown is an American basketball coach who is the head coach of the New York Knicks of the National Basketball Association (NBA). He was previously the head coach of the Sacramento Kings, Cleveland Cavaliers, and Los Angeles Lakers, as well as an
Victor Wembanyama, nicknamed "Wemby" and "the Alien", is a French professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He was selected first overall by the Spurs in the 2023 NBA draft and is considered one of t
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
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
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
Karl-Anthony Towns Jr., also known by his initials KAT, is a Dominican American professional basketball player for the New York Knicks of the National Basketball Association (NBA). He was named to the Dominican Republic national team as a 16-year-old and playe
Ogugua "OG" Anunoby Jr. is a British professional basketball player for the New York Knicks of the National Basketball Association (NBA). He played college basketball for the Indiana Hoosiers and was selected by the Toronto Raptors in the first round of the 20
.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
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
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
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
Gaspar Prim Díaz, better known as Gaspi, was an Argentine YouTuber and Internet personality.
Dirk Nicolaas "Dick" Advocaat is a Dutch professional football coach and former player who is the manager of the Curaçao national team. A successful player and manager, he is nicknamed "The Little General", a reference to his mentor Rinus Michels.
Joshua Aaron Hart is an American professional basketball player for the New York Knicks of the National Basketball Association (NBA). Across both his college and professional careers, he has logged minutes at shooting guard and both forward positions and is kn
The FIFA World Cup is an international association football competition contested by the senior men's national teams of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every fou
The NBA Finals is the annual championship series for the National Basketball Association (NBA) held at the conclusion of its postseason. All NBA Finals have been played in a best-of-seven format, and are contested between the winners of the Eastern Conference
Nathaniel Christopher "Nene" Brown is a German professional footballer who plays as a left-back for Bundesliga club Bayern Munich and the Germany national team.
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review
As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objec
Equivariant Representation Learning via Class-Pose Decomposition
We introduce a general method for learning representations that are equivariant to symmetries of data. Our central idea is to decompose the latent space into an invariant factor and the symmetry group itself. The components semantically correspond to intrinsic data classes and poses respectively. The learner is trained on a loss encouraging equivariance based on supervision from relative symmetry information. The app
Supervised Contrastive Learning with Hard Negative Samples
Through minimization of an appropriate loss function such as the InfoNCE loss, contrastive learning (CL) learns a useful representation function by pulling positive samples close to each other while pushing negative samples far apart in the embedding space. The positive samples are typically created using "label-preserving" augmentations, i.e., domain-specific transformations of a given datum or anchor. In ab
NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)
In March 2020, Neural Radiance Field (NeRF) revolutionized Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. In August 2023, Gaussian Splatting, a direct competitor to the NeRF-based framework, was proposed, gaining tremen
Prescriptive Process Monitoring is an emerging area within Process Mining that focuses on recommending actions to optimize business outcomes. Most existing works prescribe pre-defined interventions, i.e., sets of actions applied to ongoing process executions to achieve a specific objective or Key Performance Indicator (KPI). In contrast, only a few approaches have explored learning and evaluating optimal behavioral p
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM). Nevertheless, it is important to note that LLMs may contain social prejudices, and therefore, the fairness of recommendations made by RecLLM requires further investigation. To avoid the potential risks of RecLLM, it is imperative to evaluate the fairness of RecLL
For a widely-studied data model and general loss and sample-hardening functions we prove that the losses of Supervised Contrastive Learning (SCL), Hard-SCL (HSCL), and Unsupervised Contrastive Learning (UCL) are minimized by representations that exhibit Neural-Collapse (NC), i.e., the class means form an Equiangular Tight Frame (ETF) and data from the same class are mapped to the same representation. We also prove th
On Rate-Optimal Partitioning Classification from Observable and from Privatised Data
In this paper we revisit the classical method of partitioning classification and prove novel convergence rates under relaxed conditions, both for observable (non-privatised) and for privatised data. We consider the problem of classification in a $d$ dimensional Euclidean space. Previous results on the partitioning classifier worked with the strong density assumption (SDA), which is restrictive, as we demonstrate thro
Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors
The need for uncertainty quantification in anomaly detection systems has become increasingly important. In this context, effectively controlling Type I error rates without inflating Type II error rates in these systems can build trust and reduce costs associated with false discoveries. The field of conformal anomaly detection emerges as a promising approach for providing respective statistical and finite-sample valid
Arbitrary control over multimode wave propagation for machine learning
Controlled multimode wave propagation can enable more space-efficient photonic processors than architectures based on discrete components connected by single-mode waveguides. Instead of defining discrete elements, one can sculpt the continuous substrate of a photonic processor to perform computations through multimode interference in two dimensions. Here we designed and demonstrated a device with a refractive index t
Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
We propose a generalized debiased Lasso estimator based on a stability principle. When a single column of the design matrix is perturbed, the estimator admits a simple update formula that can be computed from the original solution. Under sub-Gaussian designs with well-conditioned covariance, this approximation is asymptotically accurate for all but a vanishing fraction of coordinates in the proportional growth regime
Linking Named Entities in Diderot's Encyclopédie to Wikidata
Diderot's Encyclopédie is a reference work from XVIIIth century in Europe that aimed at collecting the knowledge of its era. Wikipedia has the same ambition with a much greater scope. However, the lack of digital connection between the two encyclopedias may hinder their comparison and the study of how knowledge has evolved. A key element of Wikipedia is Wikidata that backs the articles with a graph of structured
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses. To address this vulnerability, we propose MirrorCheck, a robust and model-agnostic detection framework that operates effectively in both unimodal and multimodal settings. MirrorCheck leverages Text-to-Image (T2I) models to regenerate visual c
Extrinsic Evaluation of Cultural Competence in Large Language Models
Productive interactions between diverse users and language technologies require outputs from the latter to be culturally relevant and sensitive. Prior works have evaluated models' knowledge of cultural norms, values, and artifacts, without considering how this knowledge manifests in downstream applications. In this work, we focus on extrinsic evaluation of cultural competence in two text generation tasks, open-en
On the Geometry and Optimization of Polynomial Convolutional Networks
We study convolutional neural networks with monomial activation functions. Specifically, we prove that their parameterization map is regular and is an isomorphism almost everywhere, up to rescaling the filters. By leveraging on tools from algebraic geometry, we explore the geometric properties of the image in function space of this map - typically referred to as neuromanifold. In particular, we compute the dimension
Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches require extensive manual annotation, which is often impractical for large textual datasets. We present a weakly supervised Natural Language Processing (NLP) pipeline for classifying Italian discharge letters without document-level manual annota
This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian Process Regression (GPR) and Support Vector Regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out-of-sample data is not very promising. By choosing a suitable data-dependent covariance function, we
A Water Efficiency Dataset for African Data Centers
Artificial intelligence (AI) computing and data centers consume large amounts of freshwater, both directly for cooling and indirectly for electricity generation. While most attention has been paid to developed countries such as the U.S., this paper presents the first-of-its-kind dataset that combines nation-level weather and electricity generation data to estimate water usage effectiveness for data centers in 41 Afri
Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and struggling to adapt to unseen normality. In response to this limitation, self-supervised techniques for time series have garnered attention
We theoretically investigate the phenomena of generalization and memorization in diffusion models. Empirical studies suggest that these phenomena are influenced by model complexity and the size of the training dataset. In our experiments, we further observe that the number of noise samples per data sample ($m$) used during Denoising Score Matching (DSM) plays a significant and non-trivial role. We capture these behav
A Unified Theory of Sinusoidal Activation Families for Implicit Neural Representations
Implicit Neural Representations (INRs) model continuous signals with compact neural networks and have become a standard tool in vision, graphics, and signal processing. A central challenge is accurately capturing fine detail without heavy hand-crafted encodings or brittle training heuristics. Across the literature, periodic activations have emerged as a compelling remedy: from SIREN, which uses a single sinusoid with
Entity state tracking is a necessary component of world modeling that requires maintaining coherent representations of entities over time. Previous work has benchmarked entity tracking performance in purely text-based tasks. We introduce MET-Bench, a multimodal entity tracking benchmark designed to evaluate the ability of vision-language models to track entity states across modalities. Using three domains, we assess
ADAPT: An Autonomous Forklift for Construction Site Operation
Efficient material logistics play a critical role in controlling costs and schedules in the construction industry. However, manual material handling remains prone to inefficiencies, delays, and safety risks. Autonomous forklifts offer a promising solution to streamline on-site logistics, reducing reliance on human operators and mitigating labor shortages. This paper presents the development and evaluation of ADAPT (A
Generalized metric depth understanding is critical for precise vision-guided robotics, which current state-of-the-art (SOTA) vision-encoders do not support. To address this, we propose a self-supervised training approach that extends pretrained RGB encoders with a depth adapter to incorporate and align metric depth into a combined latent space without interfering with the pretrained RGB feature extraction. In combina
Revisiting Outage for Edge Inference Systems
One of the key missions of sixth-generation (6G) mobile networks is to deploy large-scale artificial intelligence (AI) models at the network edge to provide remote-inference services for edge devices. The resultant platform, known as edge inference, will support a wide range of Internet-of-Things applications, such as autonomous driving, industrial automation, and augmented reality. Given the mission-critical and tim
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