Record 14072026 · captured 2026-08-25
The world looked up Sam Neill. 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.
Sir Nigel John Dermot "Sam" Neill was a New Zealand actor. Known as a leading man in film and television, he received nominations for three Primetime Emmy Awards and two Golden Globe Awards. He was appointed an Officer of the Order of the British Empire in the
Lindsey Olin Graham was an American politician, attorney, and military officer who represented South Carolina in the United States Senate from 2003 until his death in 2026. A member of the Republican Party, he represented South Carolina's 3rd congressional dis
Darline Lettie Graham Nordone is an American politician and public administrator serving as the junior United States senator from South Carolina, a seat she has held since July 14, 2026. A member of the Republican Party, she was appointed to fill the vacancy c
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
Jude Victor William Bellingham is an English professional footballer who plays as a midfielder for La Liga club Real Madrid and the England national team. Regarded as one of the best players in the world, he is known for his athleticism and ball-winning abilit
Ann Noreen Widdecombe was a British politician and media personality. As a member of the Conservative Party, she was the member of Parliament (MP) for Maidstone and The Weald, previously Maidstone, from 1987 to 2010. She joined the Brexit Party – later Reform
Jannik Sinner is an Italian professional tennis player. He is currently ranked world No.1 by Association of Tennis Professionals (ATP), and was the year-end No. 1 in 2024. Sinner has won 30 ATP Tour-level singles titles, including five majors, ten Masters and
Addison Mitchell McConnell III is an American politician and attorney who has been a United States senator from Kentucky since 1985 and has been Kentucky's senior U.S. senator since 1999. A member of the Republican Party, McConnell is in his seventh Senate ter
Erling Braut Haaland is a Norwegian professional footballer who plays as a striker for Premier League club Manchester City and the Norway national team. Regarded as one of the best players in the world and the greatest Norwegian player of all time, he is known
Youri Marion A. Tielemans is a Belgian professional footballer who plays as a midfielder for Premier League club Manchester United and captains the Belgium national team.
Sam Neill was a New Zealand actor whose career in film and television spanned nearly five decades. He made his screen debut in the New Zealand television film The City of No (1971). He subsequently appeared in the short film The Water Cycle (1972) and the tele
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
Joshua Steven Grisetti was an American actor, director, educator, and author who worked in theater, television, and film.
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
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
Digger is an upcoming satirical black comedy film directed by Alejandro G. Iñárritu, who co-wrote the film with Sabina Berman, Alexander Dinelaris Jr., and Nicolás Giacobone, from a story by Iñárritu and Berman. It is a co-production of the United States, Unit
Conor Anthony McGregor is an Irish professional mixed martial artist. He is a former Ultimate Fighting Championship (UFC) Featherweight and Lightweight Champion, becoming the first fighter to hold UFC championships in two weight classes simultaneously. He is a
Lionel Andrés "Leo" Messi is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer (MLS) club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players in history, Messi has set
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
Sistla Janaki was an Indian playback singer and occasional music composer. Fondly known as Janaki Amma and described as the "Nightingale of South India", Janaki is considered one of the greatest and most influential singers in the history of Indian music. She
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
On 13 January 2012, the seven-year-old Costa Cruises vessel Costa Concordia was on the first leg of a cruise around the Mediterranean Sea when the cruise ship deviated from her planned route at Isola del Giglio, Italy in order to perform a sail-by salute, and
Kylian Mbappé Lottin is a French professional footballer who plays as a forward for La Liga club Real Madrid and captains the France national team. Widely regarded as one of the best players in the world and one of the greatest French players of all time, he i
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 third season of the American fantasy drama television series House of the Dragon premiered on HBO on June 21, 2026, in the United States and concluded on August 9, 2026. It consists of eight episodes, each of approximately one hour. The season covers the e
Moana is a 2026 American adventure film and a live-action adaptation of Disney Animation's 2016 film. As the overall third installment and the first live-action film in the Moana franchise, the film was directed by Thomas Kail and written by Jared Bush and Dan
Laura Margaret Tingle is an Australian journalist and author. She is the ABC's global affairs editor and was formerly the chief political correspondent of the Australian Broadcasting Corporation's 7.30 current affairs television program and previously the poli
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.
Evil Dead Burn is a 2026 American supernatural horror film written and directed by Sébastien Vaniček, who co-wrote it with Florent Bernard, and produced by Rob Tapert and series creator Sam Raimi. It serves as a sequel to Evil Dead Rise (2023) and is the sixth
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Improved Answer Selection with Pre-Trained Word Embeddings
This paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into traditional information retrieval (IR) systems allows for the capture of semantic relatedness between questions and answers. Empirical results on three publicly available data sets show significant g
Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards
We consider the problem of scheduling in multi-class, parallel-server queuing systems with uncertain rewards from job-server assignments. In this scenario, jobs incur holding costs while awaiting completion, and job-server assignments yield observable stochastic rewards with unknown mean values. The mean rewards for job-server assignments are assumed to follow a bilinear model with respect to features that characteri
Research on Cross-media Science and Technology Information Data Retrieval
Since the era of big data, the Internet has been flooded with all kinds of information. Browsing information through the Internet has become an integral part of people's daily life. Unlike news data and social data on the Internet, cross-media science and technology information data has different characteristics. This data has become an important basis for researchers and scholars to track current hot spots and e
Research on Intellectual Property Resource Profile and Evolution Law
In the era of big data, intellectual property-oriented scientific and technological resources show the trend of large data scale, high information density, and low value density, which brings severe challenges to the effective use of intellectual property resources, and the demand for mining hidden information in intellectual property is increasing. This makes intellectual property-oriented science and technology res
Profiling and Evolution of Intellectual Property
In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding. However, the increase in the number and category of information data will also increase the cost of information acquisition. For technology-based enterprises or users, in addition to general papers, patents, and other resources, policies related to technology or the devel
Unmatched uncertainty mitigation through neural network supported model predictive control
This paper presents a deep learning based model predictive control (MPC) algorithm for systems with unmatched and bounded state-action dependent uncertainties of unknown structure. We utilize a deep neural network (DNN) as an oracle in the underlying optimization problem of learning based MPC (LBMPC) to estimate unmatched uncertainties. Generally, non-parametric oracles such as DNN are considered difficult to employ
Interventions Against Machine-Assisted Statistical Discrimination
I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans. When beliefs are verifiable, interventions against statistical discrimination can move beyond simple belief-free designs, like affirmative action and blinding, to more sophisticated belief-contingent ones. I analyze a belief-contingent intervention, common identity, and show that it can
New Advances in Body Composition Assessment with ShapedNet: A Single Image Deep Regression Approach
We introduce a novel technique called ShapedNet to enhance body composition assessment. This method employs a deep neural network capable of estimating Body Fat Percentage (BFP), performing individual identification, and enabling localization using a single photograph. The accuracy of ShapedNet is validated through comprehensive comparisons against the gold standard method, Dual-Energy X-ray Absorptiometry (DXA), uti
Federated Topic Model and Model Pruning Based on Variational Autoencoder
Topic modeling has emerged as a valuable tool for discovering patterns and topics within large collections of documents. However, when cross-analysis involves multiple parties, data privacy becomes a critical concern. Federated topic modeling has been developed to address this issue, allowing multiple parties to jointly train models while protecting privacy. However, there are communication and performance challenges
Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting
We propose a novel self-supervised framework for retargeting non-parameterized 3D garments onto 3D human avatars of arbitrary shapes and poses, enabling 3D virtual try-on (VTON). Existing self-supervised 3D retargeting methods only support parametric and canonical garments, which can only be draped over parametric body, e.g. SMPL. To facilitate the non-parametric garments and body, we propose a novel method that intr
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
Retrieval-augmented generation (RAG) has rapidly advanced the language model field, particularly in question-answering (QA) systems. By integrating external documents during the response generation phase, RAG significantly enhances the accuracy and reliability of language models. This method elevates the quality of responses and reduces the frequency of hallucinations, where the model generates incorrect or misleadin
Randomized Confidence Bounds for Stochastic Partial Monitoring
The partial monitoring (PM) framework provides a theoretical formulation of sequential learning problems with incomplete feedback. On each round, a learning agent plays an action while the environment simultaneously chooses an outcome. The agent then observes a feedback signal that is only partially informative about the (unobserved) outcome. The agent leverages the received feedback signals to select actions that mi
The sim-to-real gap, which represents the disparity between training and testing environments, poses a significant challenge in reinforcement learning (RL). A promising approach to addressing this challenge is distributionally robust RL, often framed as a robust Markov decision process (RMDP). In this framework, the objective is to find a robust policy that achieves good performance under the worst-case scenario amon
Deep learning-based malware detection systems are vulnerable to adversarial EXEmples - carefully-crafted malicious programs that evade detection with minimal perturbation. As such, the community is dedicating effort to develop mechanisms to defend against adversarial EXEmples. However, current randomized smoothing-based defenses are still vulnerable to attacks that inject blocks of adversarial content. In this paper,
Neural Active Learning Meets the Partial Monitoring Framework
We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously
Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need
The introduction of 5G technology has revolutionized communications, enabling unprecedented capacity, connectivity, and ultra-fast, reliable communications. However, this leap has led to a substantial increase in energy consumption, presenting a critical challenge for network sustainability. Accurate energy consumption modeling is essential for developing energy-efficient strategies, enabling operators to optimize re
PanDORA: Casual HDR Radiance Acquisition of Indoor Scenes for Image-based Lighting
Most novel view synthesis methods -- including Neural Radiance Fields (NeRF) -- struggle to capture the high dynamic range (HDR) radiance required for realistic image-based lighting (IBL). This limitation stems from a reliance on low dynamic range (LDR) imagery, which fails to capture the intensity of light sources found in indoor environments. While exposure bracketing can recover this range, it is often too slow fo
In this work, we consider the non-invasive medical imaging modality of Electrical Impedance Tomography (EIT), where the goal is to recover the conductivity in a medium from boundary current-to-voltage measurements, i.e., the Neumann-to-Dirichlet (N--t--D) operator. We formulate this inverse problem as an operator-learning task, where the aim is to approximate the implicitly defined map from N--t--D operators to admis
Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models
The imitation of cursive handwriting is mainly limited to generating handwritten words or lines. Multiple synthetic outputs must be stitched together to create paragraphs or whole pages, whereby consistency and layout information are lost. To close this gap, we propose a method for imitating handwriting at the paragraph level that also works for unseen writing styles. Therefore, we introduce a modified latent diffusi
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
In the zero-shot policy transfer (ZSPT) setting for contextual Markov decision processes (CMDP), agents train on a fixed, finite set of contexts and must generalize to new ones. Recent work has demonstrated that training on additional states, even if they are irrelevant for solving the current context, can improve generalization to unseen contexts. In this paper, we demonstrate that training on these states can indee
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment. However, existing unsupervised skill discovery methods often learn entangled skills where one skill variable simultaneously influences many entities in the environment, making downstream skill chaining extremely challenging. We propose Disentangled Unsupervised Skill Discovery (DUSDi), a
On Occlusions in Video Action Detection: Benchmark Datasets And Training Recipes
This paper explores the impact of occlusions in video action detection. We facilitate this study by introducing five new benchmark datasets namely O-UCF and O-JHMDB consisting of synthetically controlled static/dynamic occlusions, OVIS-UCF and OVIS-JHMDB consisting of occlusions with realistic motions and Real-OUCF for occlusions in realistic-world scenarios. We formally confirm an intuitive expectation: existing mod
Asynchronous Perception Machine For Efficient Test-Time-Training
In this work, we propose Asynchronous Perception Machine (APM), a computationally-efficient architecture for test-time-training (TTT). APM can process patches of an image one at a time in any order asymmetrically and still encode semantic-awareness in the net. We demonstrate APM's ability to recognize out-of-distribution images without dataset-specific pre-training, augmentation or any-pretext task. APM offers co
Big data approach to Kazhdan-Lusztig polynomials
We investigate the structure of Kazhdan-Lusztig polynomials of the symmetric group by leveraging computational approaches from big data, including exploratory and topological data analysis, applied to the polynomials for symmetric groups of up to 11 strands.
SparseLGS: Sparse View Language Embedded Gaussian Splatting
Recently, several studies have combined Gaussian Splatting to obtain scene representations with language embeddings for open-vocabulary 3D scene understanding. While these methods perform well, they essentially require very dense multi-view inputs, limiting their applicability in real-world scenarios. In this work, we propose SparseLGS to address the challenge of 3D scene understanding with pose-free and sparse view
Notable events recorded on this day and month across all years.