Record 15072026 · 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.
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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
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.
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
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
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
Yandex LLC is a Russian technology company that provides Internet-related products and services including a web browser, search engine, cloud computing, web mapping, online food ordering, streaming media, online shopping, and a ridesharing company.
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
Mikel Oyarzabal Ugarte is a Spanish professional footballer who plays as a forward for La Liga club Real Sociedad, which he captains, and the Spain national team.
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
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
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
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
Bastille Day is the common name given in English-speaking countries to the national day of France, which is celebrated on 14 July each year. It is referred to, both legally and commonly, as le 14 juillet in French, though la fête nationale is also used in the
Jordan Alexander Walker is an American professional baseball right fielder for the St. Louis Cardinals of Major League Baseball (MLB). The Cardinals selected him in the first round of the 2020 MLB draft. He made his MLB debut in 2023 and was named an All-Star
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
The Spain national football team have represented Spain in men's international football competition since 1920, and are governed by the Royal Spanish Football Federation. They are the reigning World and European champions, having won the most recent FIFA World
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
Didier Claude Deschamps, popularly known as Dédé, is a French professional football manager and former player. He played as a defensive midfielder for several clubs, in France, Italy, England and Spain, namely Marseille, Juventus, Chelsea and Valencia, as well
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
Michael Akpovie Olise is a professional footballer who plays as a winger or attacking midfielder for Bundesliga club Bayern Munich and the France national team. Widely regarded as one of the best players in the world, he is known for his creative playmaking, t
Sonam Wangchuk is an Indian engineer, educator, and activist. He is the founding director of the Students' Educational and Cultural Movement of Ladakh (SECMOL), which was founded in 1988 by a group of students who struggled with the public education system. He
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
Marc Cucurella Saseta is a Spanish professional footballer who plays as a left-back or left wing-back for La Liga club Real Madrid and the Spain national team. He is considered to be one of the best left-backs in the world.
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
The France–Spain football rivalry is one of the biggest and most heated association football rivalries in Europe, between France and Spain, two of the most successful national teams in the world as well as neighbours in Europe.
Pedro Antonio Porro Sauceda is a Spanish professional footballer who plays as a right-back for Premier League club Tottenham Hotspur and the Spain national team.
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
The France national football team represents France in men's international football. It is overseen by the French Football Federation, the governing body for football in France. It is a member of UEFA in Europe and FIFA in global competitions. The team's colou
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learning (ML) has shown its applicability to various complex problems and is frequently used in the dependability domain, both for supporting systems design and verification activities. However, using ML is complex and highly dependent on the problem in hand, increasing the probabilit
Diversity-Enriched Option-Critic
Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The option-critic framework has been demonstrated to learn temporally extended actions, represented as options, end-to-end in a model-free setting. However, feasibility of option-critic remains limited due to two major challenges, multiple options adopting very similar behavior, or a
Various machine-learning models, including deep neural network models, have already been developed to predict deleteriousness of missense (non-synonymous) mutations. Potential improvements to the current state of the art, however, may still benefit from a fresh look at the biological problem using more sophisticated self-adaptive machine-learning approaches. Recent advances in the natural language processing field sh
Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
Training logistic regression over encrypted data has emerged as a prominent approach to addressing security concerns in recent years. In this paper, we introduce an efficient gradient variant, termed the \textit{quadratic gradient}, which is specifically designed for privacy-preserving logistic regression while remaining equally effective in plaintext optimization. By incorporating this quadratic gradient, we enhance
Diffusion models have proven to be a flexible and effective framework for modelling probability distributions on finite-dimensional spaces. However, many physical modelling problems such as time series are naturally described over function spaces. In this work we apply diffusion models to such stochastic processes. To do so we consider a spectral representation of the data, obtained using a kernel, thereby dissociati
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice. The objective is to find conformations maximizing H-H contacts. It is known that even in this redu
Classification of integers based on residue classes via modern deep learning algorithms
Judging whether an integer can be divided by prime numbers such as 2 or 3 may appear trivial to human beings, but can be less straightforward for computers. Here, we tested multiple deep learning architectures and feature engineering approaches on classifying integers based on their residues when divided by small prime numbers. We found that the ability of classification critically depends on the feature space. We al
Data as Voters: Core Set Selection Using Approval-Based Multi-Winner Voting
We present a novel approach to the core set/instance selection problem in machine learning. Our approach is based on recent results on (proportional) representation in approval-based multi-winner elections. In our model, instances play a double role as voters and candidates. The approval set of each instance in the training set (acting as a voter) is defined from the concept of local set, which already exists in the
We hypothesize that large language models (LLMs) based on the transformer architecture can enable automated detection of clinical phenotype terms, including terms not documented in the HPO. In this study, we developed two types of models: PhenoBCBERT, a BERT-based model, utilizing Bio+Clinical BERT as its pre-trained model, and PhenoGPT, a GPT-based model that can be initialized from diverse GPT models, including ope
Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing
This paper investigates the strategic decision-making capabilities of three Large Language Models (LLMs): GPT-3.5, GPT-4, and LLaMa-2, within the framework of game theory. Utilizing four canonical two-player games -- Prisoner's Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight -- we explore how these models navigate social dilemmas, situations where players can either cooperate for a collective benefit or
The term "Reversal Curse" refers to the scenario where auto-regressive decoder large language models (LLMs), such as ChatGPT, trained on "A is B" fail to learn "B is A," assuming that B and A are distinct and can be uniquely identified from each other, demonstrating a basic failure of logical deduction. This raises a red flag in the use of GPT models for certain general tasks such as construct
Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically proving the dominance of local SGD in settings with reasonable data heterogeneity has been difficult, creating a significant gap between theory and practice. In this paper, we provide new lower bounds for local SGD under existing first-orde
Conformal Recursive Feature Elimination
Unlike traditional statistical methods, Conformal Prediction (CP) allows for the determination of valid and accurate confidence levels associated with individual predictions based only on exchangeability of the data. We here introduce a new feature selection method that takes advantage of the CP framework. Our proposal, named Conformal Recursive Feature Elimination (CRFE), identifies and recursively removes features
Active Exploration via Autoregressive Generation of Missing Data
We pose uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence model, an area experiencing rapid innovation. Our approach rests on viewing uncertainty as arising from missing future outcomes that could be revealed through action choices, rather than from unobservable latent parameters of the environment. This reformulation aligns na
VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis
While Large Multimodal Models (LMMs) excel in general multimodal tasks, they lack the domain-specific knowledge for industrial vibration signal analysis. This paper introduces VSLLaVA, a comprehensive pipeline that utilizes expert knowledge-guided instruction tuning and evaluation to create an end-to-end LMM for signal analysis. To achieve this, we construct a novel Signal-Question-Answer (SQA) dataset using an exper
Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e., In-Distribution (InD), or from an unknown out distribution. Recent researches have leveraged Diffusion Models (DMs) for OoD detection due to their powerful distribution modeling capability. Given an input image, an InD-pretrained DM produces a corresponding InD-aligned
A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devices
Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices. Yet, there is still limited understanding of how different object detection models behave across heterogeneous edge devices and under varying scene complexity. In this paper, we benchmark YOLOv8 (Nano, Small, Medium), EfficientDet Lite (Lite0, Li
Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias
This paper investigates the challenges associated with bias, toxicity, unreliability, and lack of robustness in large language models (LLMs) such as ChatGPT. It emphasizes that these issues primarily stem from the quality and diversity of data on which LLMs are trained, rather than the model architectures themselves. As LLMs are increasingly integrated into various real-world applications, their potential to negative
Growing a Tail: Increasing Output Diversity in Large Language Models
How diverse are the outputs of large language models when diversity is desired? We examine the diversity of responses of several language models to questions with multiple possible answers, comparing them with human responses. Our findings suggest that models' responses are highly concentrated, reflecting narrow, mainstream outputs, in comparison to humans, whose responses exhibit a much longer-tail. We examine t
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports high-dimensional conditional control over twelve physicochemical and structural properties from partial specifications, constructs molecules via symmetry-aware fragment attachments, and conditions each generation step on force-field-relaxed three-dimensional geometry of the partial
Image Matching Filtering and Refinement by Planes and Beyond
This paper provides a consistent and extensive evaluation of state-of-the-art filtering and refinement methods on common image matching pipelines. Unlike previous comparisons, the designed benchmark also takes into account the more general, real, and practical cases where camera intrinsics are unavailable. Moreover, a novel and effective strategy combining non-deep traditional computer vision approaches based on plan
Reinforcement learning (RL) has demonstrated remarkable capabilities in training agents to solve complex tasks autonomously, such as mobile robots, UAVs/UGVs, and game-playing agents). However, scaling RL to master multiple tasks simultaneously (i.e., so-called multi-task RL) remains a significant challenge. Such a multi-task RL capability especially is important for agents to adapt to changes in real-world operation
LVMark: Robust Watermark for Latent Video Diffusion Models
Rapid advancements in video diffusion models have enabled the creation of realistic videos, raising concerns about unauthorized use and driving the demand for techniques to protect model ownership. Existing watermarking methods suffer from two key limitations: they overlook temporal consistency due to conventional watermark decoders and degrade the visual quality of the generated videos. To address these issues, we i
Modeling Story Expectations: A Generative Framework using LLMs
Consumers' engagement with stories is shaped by their expectations about what will happen next, yet modeling these forward-looking beliefs over unstructured narrative content has remained challenging. We develop a framework that uses large language models to approximate consumers' story expectations. Our method generates multiple imagined story continuations from a pre-trained LLM and extracts interpretable,
Toward Metaphor-Fluid Conversation Design for Voice User Interfaces
Metaphors play a critical role in shaping user experiences with Voice User Interfaces (VUIs), yet existing designs often rely on static, human-centric metaphors that fail to adapt to diverse contexts and user needs. This paper introduces Metaphor-Fluid Design, a novel approach that dynamically adjusts metaphorical representations based on conversational use-contexts. We compare this approach to a Default VUI, which c
Notable events recorded on this day and month across all years.