Record 13052026 · captured 2026-08-25
The world looked up Brandon Clarke. 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.
Brandon Clarke was a Canadian–American professional basketball player who served as a power forward for the Memphis Grizzlies of the National Basketball Association (NBA). He played college basketball for the San Jose State Spartans and the Gonzaga Bulldogs. C
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
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
Orthohantavirus is a genus of viruses which includes all hantaviruses that cause disease in humans. Hantaviruses are naturally found primarily in rodents. In general, each hantavirus is carried by one rodent species and each rodent that carries a hantavirus ca
Sheryl Patrice Underwood is an American comedian, actress, and television host. She first rose to prominence in the comedy world as the first female finalist in 1989's Miller Lite Comedy Search. Underwood was one of the hosts on the CBS Daytime talk show The T
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
Tony Hinchcliffe is an American comedian and podcaster. Since 2013, he has hosted the stand-up comedy podcast Kill Tony, a showcase of professional and amateur comedians who take turns doing one-minute sets. Hinchcliffe is known primarily for roast comedy, hav
Hayden Lesley Panettiere was an American actress and singer. She starred as Claire Bennet on the NBC superhero series Heroes (2006–2010), Kirby Reed in the slasher horror franchise Scream (2011–2023), Juliette Barnes in the ABC/CMT musical drama series Nashvil
Chandrasekaran Joseph Vijay is an Indian politician and former actor who is currently serving as the ninth chief minister of Tamil Nadu since May 2026. He is the founder and president of the political party Tamilaga Vettri Kazhagam (TVK). Prior to entering pol
Eileen Wang is a Chinese-American politician who served as the mayor of Arcadia, California, from February 3 to May 11, 2026, when she resigned from office after pleading guilty to acting as a foreign agent for the People's Republic of China. Wang was elected
.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
Wade Steven Wilson is an American criminal convicted of the 2019 murders of Kristine Melton and Diane Ruiz in Cape Coral, Florida. Due to sharing the name of the Marvel character Wade "Deadpool" Wilson, Wilson has been referred to in the media as the "Deadpool
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
Alex Ikenna Ekubo-Okwaraeke was a Nigerian actor, known for acting in Nollywood. Ekubo was the first runner-up at the 2010 Mr Nigeria contest and later starred in Weekend Getaway. In 2020, he was recognised among the "Most Influential People of African Descent
Survival of the Sickest (song)
"Survival of the Sickest" is a song by American rock band Saliva. It was released in June 2004 as the first single off their fourth album of the same name (2004). The song received positive reviews from critics. "Survival of the Sickest" peaked at numbers 6 an
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
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
John Christian Devavaram Prabhakar is an Indian politician and a Member of the Legislative Assembly of Tamil Nadu representing the Thousand Lights Assembly constituency, as a member of the TVK. He is the Speaker of the Tamil Nadu Legislative Assembly.
Mortal Kombat II is a 2026 American martial arts high fantasy film based on the video-game series created by Ed Boon and John Tobias. It is the sequel to Mortal Kombat (2021) and is the fourth installment in the Mortal Kombat film series. Directed by Simon McQ
Chelsea Joy Handler is an American stand-up comedian, actress, writer, television host, and producer. She hosted the late-night talk show Chelsea Lately on the E! network from 2007 to 2014 and released a documentary series, Chelsea Does, on Netflix in January
MV Hondius hantavirus outbreak
In April 2026, an outbreak of hantavirus infection caused by the Andes virus was identified on the Dutch cruise ship MV Hondius. There were ten confirmed cases and two suspected cases directly linked to the outbreak as of 22 May. There have been three deaths,
Jason Paul Collins was an American professional basketball player who was a center for 13 seasons in the National Basketball Association (NBA). He played college basketball for the Stanford Cardinal, earning third-team All-American honors in 2001. Collins was
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
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
Wesley Paul William Streeting is a British politician who has served as Secretary of State for Defence since 2026. He previously served as Secretary of State for Health and Social Care from 2024 until his resignation in 2026. A member of the Labour Party, he h
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
Clayton Holmes Aiken is an American singer, television personality, actor and political activist. Aiken finished second place on the second season of American Idol in 2003, and his debut album, Measure of a Man, went multi-platinum. He released four more album
Remarkably Bright Creatures (film)
Remarkably Bright Creatures is a 2026 American drama film directed by Olivia Newman, who co-wrote the film with screenwriter John Whittington. It is an adaptation on the 2022 novel of the same name by Shelby Van Pelt. The film stars Sally Field, Lewis Pullman,
Sir Keir Rodney Starmer is a British politician and former lawyer who served as Prime Minister of the United Kingdom from 2024 to 2026. He served as Leader of the Labour Party from 2020 to 2026 and as Leader of the Opposition from 2020 to 2024. He has been Mem
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Enriching and Controlling Global Semantics for Text Summarization
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to ca
Stochastic tensor space feature theory with applications to robust machine learning
In this paper we develop a Multilevel Orthogonal Subspace (MOS) Karhunen-Loeve feature theory based on stochastic tensor spaces, for the construction of robust machine learning features. Training data are treated as instances of a random field within a relevant Bochner space. Our key observation is that separate machine learning classes can reside predominantly in mostly distinct subspaces. Using the Karhunen-Loeve e
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions
Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization. This might cause harm to model's predictions in numerous cases. To overcome the aforementioned issues, we propose Multimodal Contrastive Le
PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
Point cloud learning is receiving increasing attention. However, most existing point cloud models lack the practical ability to deal with the unavoidable presence of unknown objects. This paper primarily discusses point cloud learning in open-set settings, where we train the model without data from unknown classes and identify them during the inference stage. In essence, we propose a novel Point Cut-and-Mix mechanism
Natural Language Processing in the Legal Domain
We summarize the current state of the field of NLP & Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a nearly complete corpus of nearly one thousand NLP & Law related papers published between 2013-2024. Our analysis highlights several major trends. Namely, we document an increasing number of papers written, tasks undertaken, and lan
The power and limitations of learning quantum dynamics incoherently
Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share quantum information, less attention has been paid to whether the dynamics of quantum systems can be learned without the system and target directly interacting. Such incoherent frameworks are practically appealing since they open up methods of
DiFaReli++: Diffusion Face Relighting with Consistent Cast Shadows
We introduce a novel approach to single-view face relighting in the wild, addressing challenges such as global illumination and cast shadows. A common scheme in recent methods involves intrinsically decomposing an input image into 3D shape, albedo, and lighting, then recomposing it with the target lighting. However, estimating these components is error-prone and requires many training examples with ground-truth light
Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews. Previous studies commonly employ fully-connected neural networks (FCNNs) as the final score predictor and pairwise loss as the training objective. However, FCNNs have been shown to perform inefficient splitting for re
netFound: Principled Design for Network Foundation Models
Network foundation models promise reusable representations for diverse traffic analysis tasks, but recent diagnostic works have revealed fundamental problems: models exploit dataset shortcuts rather than learning genuine traffic patterns, produce collapsed embedding spaces, and fail to capture the exogenous network conditions that shape real-world behavior. We translate these diagnostic insights into four concrete de
Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the relations between video moments and the text query. However, naive attention might not be able to appropriately capture such relations, resulting in ineffective distributions where target video moments are difficult to separate from the remai
Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization. With a growing number of tasks and limited training data, such full fine-tuning approach leads to costly model storage and unstable training. To overcome these shortcomings, we introduce lightweight adapters to the pre-traine
Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data
COVID-19 has affected more than 223 countries worldwide and in the Post-COVID Era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID
Interactive Mars Image Content-Based Search with Interpretable Machine Learning
The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user engagement demands an interpretable content classification system to support scientific discovery and individual curiosity. In this paper, we leverage a prototype-based architecture to enable users to understand and validate the evidence used
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by heterogeneous (non-IID) client data. Conventional pruning methods often treat data heterogeneity as a problem to be mitigated. In this work, we introduce a paradigm shift: we reframe client diversity as a feature to be harnessed. We propos
Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive the world around us. There has been a lot of interest in creating video-language understanding systems with human-like senses since a video-language pair can mimic both our linguistic medium and visual environment with temporal dynamics. In
Sparsity-Constraint Optimization via Splicing Iteration
Sparsity-constrained optimization underlies many problems in signal processing, statistics, and machine learning. State-of-the-art hard-thresholding (HT) algorithms rely on an appropriately selected continuous step-size parameter to ensure convergence. In this paper, we propose a naturally convergent iterative algorithm, SCOPE (Sparsity-Constrained Optimization via sPlicing itEration). The algorithm is capable of opt
Towards Shutdownable Agents via Stochastic Choice
The POST-Agents Proposal (PAP) is an idea for ensuring that advanced artificial agents never resist shutdown. A key part of the PAP is using a novel `Discounted Reward for Same-Length Trajectories (DReST)' reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be `USEFUL'), and (2) choose stochastically between different trajectory-lengths (be `NEUTRAL' abou
Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Models
Accurate and reliable energy forecasting is essential for power grid operators who strive to minimize extreme forecasting errors that pose significant operational challenges and incur high intra-day trading costs. Incorporating planning information -- such as anticipated user behavior, scheduled events or timetables -- provides substantial contextual information to enhance forecast accuracy and reduce the occurrence
Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models
This paper introduces Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of $g$ priors that allow for differential shrinkage for various (data-selected) blocks of parameters while fully accounting for the predictors' correlation structure, providing a bridge between the literatures on model selection and continuou
Cameras can be used to perceive the environment around the vehicle, while affordable radar sensors are popular in autonomous driving systems as they can withstand adverse weather conditions unlike cameras. However, radar point clouds are sparser with low azimuth and elevation resolution that lack semantic and structural information of the scenes, resulting in generally lower radar detection performance. In this work,
Understanding the capabilities of text-to-image (T2I) models in harmful content generation is essential to safety and compliance. However, human red-teaming is costly and inconsistent, driving the need for automatic tools that simulate realistic misuse attempts. Existing methods either require white-box access, fail to generalize across defenses, or produce uninterpretable adversarial tokens, while generating fluent
How far can bias go? Tracing bias from pretraining data to alignment
As LLMs are increasingly integrated into user-facing applications, addressing biases that perpetuate societal inequalities is crucial. While much work has gone into measuring or mitigating biases in these models, fewer studies have investigated their origins. Therefore, this study examines the correlation between gender-occupation bias in pre-training data and their manifestation in LLMs, focusing on the Dolma datase
BEExformer: A Fast Inferencing Binarized Transformer with Early Exits
Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and processing requirements hinder deployment on constrained resources. To enhance efficiency, binarization and Early Exit (EE) have proved to be effective solutions. However, binarization may lead to performance loss as reduced precision affects gradient estimation and parameter
Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inverse problems, sensitivity analysis, and probabilistic forward predictions, which would otherwise be computationally infeasible. During training, surrogate parameters are fitted such that the surrogate reproduces the simulation model's outputs as closely as possible. However,
Modality-Inconsistent Continual Learning of Multimodal Large Language Models
In this paper, we introduce Modality-Inconsistent Continual Learning (MICL), a new continual learning scenario for Multimodal Large Language Models (MLLMs) that involves tasks with inconsistent modalities (image, audio, or video) and varying task types (captioning or question-answering). Unlike existing vision-only or modality-incremental settings, MICL combines modality and task type shifts, both of which drive cata
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