Record 14052026 · 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
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
Donald Richard Gibb was an American actor, best known for his roles as the hulking, dimwitted fraternity brother Frederick “Ogre” Palowaski in several installments of the Revenge of the Nerds film series, as Kumite fighter Ray Jackson in Bloodsport, and as Les
The fifth and final season of the American satirical superhero television series The Boys, the first series in the franchise based on the comic book series of the same name created by Garth Ennis and Darick Robertson, was developed for television by Eric Kripk
The Punisher: One Last Kill is an American television special directed by Reinaldo Marcus Green and written by Jon Bernthal and Green for the streaming service Disney+, based on Marvel Comics featuring the character Punisher. It is the third Special Presentati
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 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
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
Dileep Raj was an Indian actor, television director and producer known for his work in Kannada television and cinema. He appeared in 24 films and produced many television serials under his banner DR Creations.
José Fernandes André Cavaco Miglietti, known as Zeca, was a Mozambican footballer who played mainly as a defender, either in the center or in the left.
State of South Carolina v. Richard Alexander Murdaugh was the trial of former American lawyer Alex Murdaugh for the murders of his wife, Maggie, and their 22-year-old son, Paul, on June 7, 2021. The trial in the fourteenth circuit of the South Carolina Circuit
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
The Boys is an American satirical superhero streaming television series developed by Eric Kripke for Amazon Prime Video. Based on the comic book series of the same name by Garth Ennis and Darick Robertson, it follows the eponymous team of vigilantes as they co
.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
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
Milton Teagle "Richard" Simmons was an American fitness instructor and television personality. He was a promoter of weight-loss programs, most prominently through his television show, The Richard Simmons Show and later the Sweatin' to the Oldies line of aerobi
Origami is the art and technique of folding paper. It also refers to the two- and three-dimensional forms created in the process. The use of the term has been extended in modern times to include other materials such as metal, textiles, and it is also used in t
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
The Boys is an American satirical superhero television series developed by showrunner Eric Kripke that premiered on July 26, 2019, on the streaming service Amazon Prime Video. Kripke also serves as an executive producer, alongside Evan Goldberg, Neal H. Moritz
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
Michelle Christine Trachtenberg was an American actress. After beginning her career in commercials at age three, she made her television debut in her first credited role on the Nickelodeon series The Adventures of Pete & Pete (1994–1996) and her feature film d
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
Mulayam Singh Yadav also known "Neta ji" was an Indian politician, schoolmaster, lecturer and a socialist figure and the founder of the Samajwadi Party. Over the course of his political career spanning more than six decades, he served for three terms as the Ch
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
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
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
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
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
ReAct! An Interactive Tool for Hybrid Planning in Robotics
We present ReAct!, an interactive tool for high-level reasoning for cognitive robotic applications. ReAct! enables robotic researchers to describe robots' actions and change in dynamic domains, without having to know about the syntactic and semantic details of the underlying formalism in advance, and solve planning problems using state-of-the-art automated reasoners, without having to learn about their input/outp
Training VAEs Under Structured Residuals
Variational auto-encoders (VAEs) are a popular and powerful deep generative model. Previous works on VAEs have assumed a factorized likelihood model, whereby the output uncertainty of each pixel is assumed to be independent. This approximation is clearly limited as demonstrated by observing a residual image from a VAE reconstruction, which often possess a high level of structure. This paper demonstrates a novel schem
Physics-driven Fire Modeling from Multi-view Images
Fire effects are widely used in various computer graphics applications such as visual effects and video games. Modeling the shape and appearance of fire phenomenon is challenging as the underlying effects are driven by complex laws of physics. State-of-the-art fire modeling techniques rely on sophisticated physical simulations which require intensive parameter tuning, or use simplifications which produce physically i
The GAN that Warped: Semantic Attribute Editing with Unpaired Data
Deep neural networks have recently been used to edit images with great success, in particular for faces. However, they are often limited to only being able to work at a restricted range of resolutions. Many methods are so flexible that face edits can often result in an unwanted loss of identity. This work proposes to learn how to perform semantic image edits through the application of smooth warp fields. Previous app
A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach
We study robot construction problems where multiple autonomous robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by other blocks and stability of the structure at all times. We propose a formal hybrid planning framework to solve a wide range of robot construction prob
Image Aesthetics Assessment using Multi Channel Convolutional Neural Networks
Image Aesthetics Assessment is one of the emerging domains in research. The domain deals with classification of images into categories depending on the basis of how pleasant they are for the users to watch. In this article, the focus is on categorizing the images in high quality and low quality image. Deep convolutional neural networks are used to classify the images. Instead of using just the raw image as input, dif
Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach
For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills,
Predicting the outcomes of robotic actions, often referred to as learning a world model, in complex environments remains a fundamental challenge in robotics. Existing approaches primarily rely on visual observations and action inputs to generate video-based predictions, frequently overlooking the critical role of tactile feedback in understanding physical interactions. In this work, we investigate the integration of
R-C-P Method: An Autonomous Volume Calculation Method Using Image Processing and Machine Vision
Machine vision and image processing are often used with sensors for situation awareness in autonomous systems, from industrial robots to self-driving cars. The 3D depth sensors, such as LiDAR (Light Detection and Ranging), Radar, are great invention for autonomous systems. Due to the complexity of the setup, LiDAR may not be suitable for some operational environments, for example, a space environment. This study was
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
The COVID-19 pandemic has exerted a profound impact on the global economy and continues to exact a significant toll on human lives. The COVID-19 case growth rate stands as a key epidemiological parameter to estimate and monitor for effective detection and containment of the resurgence of outbreaks. A fundamental challenge in growth rate estimation and hence outbreak detection is balancing the accuracy-speed tradeoff,
Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces
Machine learning models with inputs in a Euclidean space $\mathbb{R}^d$, when implemented on digital computers, generalize, and their generalization gap converges to $0$ at a rate of $c/N^{1/2}$ concerning the sample size $N$. However, the constant $c>0$ obtained through classical methods can be large in terms of the ambient dimension $d$ and machine precision, posing a challenge when $N$ is small to realistically
Understanding Catastrophic Forgetting In LoRA via Mean-Field Attention Dynamics
Low-Rank Adaptation (LoRA) is the dominant parameter-efficient fine-tuning method due to its favorable compute-performance trade-off, yet it suffers from catastrophic forgetting. We study forgetting through a tractable _mean-field self-attention_ toy model, where tokens evolve as an interacting particle system and LoRA acts as a low-rank perturbation. Using tools from partial differential equations and dynamical syst
Compact 3D Gaussian Splatting For Dense Visual SLAM
Recent work has shown that 3D Gaussian-based SLAM enables high-quality reconstruction, accurate pose estimation, and real-time rendering of scenes. However, these approaches are built on a tremendous number of redundant 3D Gaussian ellipsoids, leading to high memory and storage costs, and slow training speed. To address the limitation, we propose a compact 3D Gaussian Splatting SLAM system that reduces the number and
ChatSR: Multimodal Large Language Models for Scientific Formula Discovery
Current multimodal large language models (MLLMs) are mainly focused on the understanding and processing of perceptual modalities such as images and videos, while their capability for scientific data understanding remains insufficient. To this end, we propose ChatSR, a novel multimodal large language model tailored for scientific data understanding. ChatSR treats scientific data as a new modality analogous to visual c
Generative Modeling by Minimizing the Wasserstein-2 Loss
This paper develops a generative model by minimizing the second-order Wasserstein loss (the $W_2$ loss) through a distribution-dependent ordinary differential equation (ODE), whose dynamics involves the Kantorovich potential associated with the true data distribution and a current estimate of it. A main result shows that the time-marginal laws of the ODE form a gradient flow for the $W_2$ loss, which converges expone
Clustering in pure-attention hardmax transformers and its role in sentiment analysis
Transformers are extremely successful machine learning models whose mathematical properties remain poorly understood. Here, we rigorously characterize the behavior of transformers with hardmax self-attention and normalization sublayers as the number of layers tends to infinity. By viewing such transformers as discrete-time dynamical systems describing the evolution of points in a Euclidean space, and thanks to a geom
Ensemble Transport Filter via Optimized Maximum Mean Discrepancy
In this paper, we present a new ensemble-based filter method by reconstructing the analysis step of the particle filter through a transport map, which directly transports prior particles to posterior particles. The transport map is constructed through an optimization problem described by the Maximum Mean Discrepancy loss function, which matches the expectation information of the approximated posterior and reference p
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation
Multi-sensor ML models for EO aim to enhance prediction accuracy by integrating data from various sources. However, the presence of missing data poses a significant challenge, particularly in non-persistent sensors that can be affected by external factors. Existing literature has explored strategies like temporal dropout and sensor-invariant models to address the generalization to missing data issues. Inspired by the
Task Prompt Vectors: Effective Initialization through Multi-Task Soft-Prompt Transfer
Prompt tuning is an efficient solution for training large language models (LLMs). However, current soft-prompt-based methods often sacrifice multi-task modularity, requiring the training process to be fully or partially repeated for each newly added task. While recent work on task vectors applied arithmetic operations on full model weights to achieve the desired multi-task performance, a similar approach for soft-pro
AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions
AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancement of Generative AI (or GAI), the technology ecosystem behind the design, development, adoption, and deployment of AI systems has drastically changed, broadening the scope of AI Safety to address impacts on public safety and national securit
Convergent Differential Privacy Analysis for General Federated Learning
The powerful cooperation of federated learning (FL) and differential privacy~(DP) provides a promising paradigm for the large-scale private clients. However, existing analyses in FL-DP mostly rely on the composition theorem and cannot tightly quantify the privacy leakage challenges, which is tight for a few communication rounds but yields an arbitrarily loose and divergent bound eventually. This also implies a counte
BEAVER: An Enterprise Benchmark for Text-to-SQL
Existing text-to-SQL benchmarks have largely been constructed from public databases with well-structured schemas and simplistic question-SQL pairs. While large language models (LLMs) excel on these settings, their efficacy in complex private enterprise environments, characterized by intricate schemas, domain knowledge, and analytical user queries involving sophisticated structures and functions, remains unproven. To
Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit
Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the availability of big datasets. One effective strategy to mitigate the issue of insufficient data is to first harness information from other data sources possessing certain similarities in the study design stage, and then employ the multi-task or meta learning framework in the
Causal Fine-Tuning under Latent Confounded Shift
Adapting to latent confounded shift remains a core challenge in modern AI. This setting is driven by hidden variables that induce spurious correlations between inputs and outputs during training, leading models to rely on non-causal shortcuts. For example, a model may learn to treat metadata (e.g., data source like "Amazon") as a proxy for positive sentiment, causing failure when the source becomes predominan
Repurposing Image Diffusion Models for Training-Free Music Style Transfer on Mel-spectrograms
Music style transfer blends source structure with reference style to enable personalized music creation. However, existing zero-shot methods often struggle to capture fine-grained audio nuances, relying on coarse text descriptions or requiring expensive task-specific training. We propose Stylus, a training-free framework that repurposes pretrained image diffusion models for music style transfer in the Mel-spectrogram
Notable events recorded on this day and month across all years.