Record 23062026 · captured 2026-08-25
The world looked up Andy Burnham. 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.
Andrew Murray Burnham is a British politician who has served as Prime Minister of the United Kingdom and Leader of the Labour Party since July 2026. He has been Member of Parliament (MP) for Makerfield in Greater Manchester since June 2026, and was Mayor of Gr
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
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
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
List of FIFA World Cup top goalscorers
Players have scored more than 3,000 goals in the 23 men's FIFA World Cup tournaments, the goal record includes own goals scored, but not counting penalty shoot-outs. Since the first goal, by French player Lucien Laurent in 1930, nearly 1,300 footballers have s
Cape Verde, also referred to in English by its Portuguese name Cabo Verde, and known officially as the Republic of Cabo Verde, is an archipelagic country in the eastern Atlantic Ocean, off the coast of West Africa. It consists of ten volcanic islands with a co
Clive Jay Davis was an American record executive, A&R executive, record producer and lawyer. He won four Grammy Awards and was inducted into the Rock and Roll Hall of Fame as a non-performer in 2000. From 1967 to 1973, Davis was the president of Columbia Recor
Alan Greenspan was an American economist who served as the 13th chair of the Federal Reserve from 1987 to 2006. He worked as a private adviser and provided consulting for firms through his company, Greenspan Associates LLC.
Andrea Louise Mitchell is an American television journalist, anchor and commentator for NBC News, based in Washington, D.C.
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 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
Amelia Dimoldenberg is an English comedian, writer, and presenter. She is the creator and host of the web series Chicken Shop Date, in which she interviews celebrities in fried chicken restaurants while subjecting them to her sarcastic, deadpan, and awkward se
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
Toy Story 5 is a 2026 American animated comedy-drama film produced by Pixar Animation Studios for Walt Disney Pictures. Directed by Andrew Stanton and written by Stanton and Kenna Harris, it is the fifth main installment in the Toy Story film series and the se
I Will Find You is an American crime drama miniseries made for Netflix, adapted from the 2023 novel of the same name by Harlan Coben, who served as executive producer. The miniseries stars Sam Worthington, Britt Lower, Milo Ventimiglia, and Erin Richards. It p
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
Wyndham Robert Clark is an American professional golfer who plays on the PGA Tour. He has won two major championships, the 2023 and 2026 U.S. Opens.
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
Mohamed Salah Hamed Mahrous Ghaly is an Egyptian professional footballer who plays as a right winger for Süper Lig club Trabzonspor and captains the Egypt national team. He is widely regarded as one of the best players of his generation and one of the greatest
House of the Dragon is an American fantasy drama television series created by George R. R. Martin and Ryan Condal for HBO. A prequel to Game of Thrones (2011–2019), it is the second television series in Martin's A Song of Ice and Fire franchise. Based on parts
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.
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
Shakira Isabel Mebarak Ripoll, known mononymously as Shakira, is a Colombian singer-songwriter, dancer, and record producer. Referred to as the "Queen of Latin Music", she has had a significant impact on the musical landscape of Latin America and has been cred
List of prime ministers of the United Kingdom
The prime minister of the United Kingdom is the principal minister of the Crown of His Majesty's Government, and the head of the British Cabinet.
Victoria Starmer, styled Lady Starmer, is a British occupational health administrator and former solicitor. She is married to Keir Starmer, who served as Prime Minister of the United Kingdom from 2024 to 2026.
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
Marie-France van Heel is a marketing executive. She is married to Andy Burnham, the prime minister of the United Kingdom and the leader of the Labour Party.
Prime Minister of the United Kingdom
The prime minister of the United Kingdom is the head of government of the United Kingdom. The prime minister advises the sovereign on the exercise of much of the royal prerogative, chairs the Cabinet, and selects its ministers. Modern prime ministers hold offi
Voicemails for Isabelle is a 2026 American romantic comedy-drama film written and directed by Leah McKendrick and starring Zoey Deutch and Nick Robinson. The plot follows Jill who, to cope with the loss of her sister Isabelle, begins leaving voicemails on Isab
Cocktail 2 is a 2026 Indian Hindi-language romantic comedy drama film directed by Homi Adajania, co-written by Luv Ranjan and Tarun Jain, and produced by Dinesh Vijan, Luv Ranjan and Ankur Garg under Maddock Films and Luv Films. It is a spiritual sequel to Coc
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection
With a rapidly increasing number of devices connected to the internet, big data has been applied to various domains of human life. Nevertheless, it has also opened new venues for breaching users' privacy. Hence it is highly required to develop techniques that enable data owners to privatize their data while keeping it useful for intended applications. Existing methods, however, do not offer enough flexibility for
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
In this paper, we present a deep learning-based (DL-based) algorithm, as a purely mathematical platform, for providing intuitive understanding of the properties of electromagnetic (EM) wave-matter interaction in nanostructures. This approach is based on using the dimensionality reduction (DR) technique to significantly reduce the dimensionality of a generic EM wave-matter interaction problem without imposing signific
Here, we present a new approach based on manifold learning for knowledge discovery and inverse design with minimal complexity in photonic nanostructures. Our approach builds on studying sub-manifolds of responses of a class of nanostructures with different design complexities in the latent space to obtain valuable insight about the physics of device operation to guide a more intelligent design. In contrast to the cur
Incorporating Domain Knowledge into Deep Neural Networks
We present a survey of ways in which domain-knowledge has been included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain
We study the problem of finding approximate first-order stationary points in optimization problems of the form $\min_{x \in X} \max_{y \in Y} f(x,y)$, where the sets $X,Y$ are convex and $Y$ is compact. The objective function $f$ is smooth, but assumed neither convex in $x$ nor concave in $y$. Our approach relies upon replacing the function $f(x,\cdot)$ with its $k$th order Taylor approximation (in $y$) and finding a
CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment
We present Curve Distillation, CuDi, for efficient and controllable exposure adjustment without the requirement of paired or unpaired data during training. Our method inherits the zero-reference learning and curve-based framework from an effective low-light image enhancement method, Zero-DCE, with further speed up in its inference speed, reduction in its model size, and extension to controllable exposure adjustment.
Detection and Evaluation of Clusters within Sequential Data
Sequential data is ubiquitous -- it is routinely gathered to gain insights into complex processes such as behavioral, biological, or physical processes. Challengingly, such data not only has dependencies within the observed sequences, but the observations are also often high-dimensional, sparse, and noisy. These are all difficulties that obscure the inner workings of the complex process under study. One solution is t
MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis
Conventional methods for human motion synthesis are either deterministic or struggle with the trade-off between motion diversity and motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can generate long, temporally plausible, and semantically accurate motions based on a range of conditioning c
Explanations for Automatic Speech Recognition
We address quality assessment for neural network based ASR by providing explanations that help increase our understanding of the system and ultimately help build trust in the system. Compared to simple classification labels, explaining transcriptions is more challenging as judging their correctness is not straightforward and transcriptions as a variable-length sequence is not handled by existing interpretable machine
A criterion for Artificial General Intelligence: hypothetic-deductive reasoning, tested on ChatGPT
We argue that a key reasoning skill that any advanced AI, say GPT-4, should master in order to qualify as 'thinking machine', or AGI, is hypothetic-deductive reasoning. Problem-solving or question-answering can quite generally be construed as involving two steps: hypothesizing that a certain set of hypotheses T applies to the problem or question at hand, and deducing the solution or answer from T - hence the
Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading
Machine learning (ML) and deep learning (DL) techniques have gained significant attention as reduced order models (ROMs) to computationally expensive structural analysis methods, such as finite element analysis (FEA). Graph neural network (GNN) is a particular type of neural network which processes data that can be represented as graphs. This allows for efficient representation of complex geometries that can change d
Universal Time-Series Representation Learning: A Survey
Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies. Learning representations by extracting and inferring valuable information from these time series is crucial for understanding the complex dynamics of particular phenomena and enabling informed decisions. With the learned representations, we can perform numerous downstream
This paper introduces a general methodology through which a population of autonomous agents can converge on a linguistic convention that enables them to refer to arbitrary entities in their environment. The linguistic convention emerges in a decentralised manner through local communicative interactions between pairs of agents drawn from the population. The emergent convention consists of associations between symbolic
Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE
Understanding the inner workings of neural networks, including transformers, remains one of the most challenging puzzles in machine learning. This study introduces a novel approach by applying the principles of gauge symmetries, a key concept in physics, to neural network architectures. By regarding model functions as physical observables, we find that parametric redundancies of various machine learning models can be
A successful negotiation requires a range of capabilities, including comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, strategic reasoning, and effective communication, making it challenging for automated systems. Despite the remarkable performance of LLMs in various NLP tasks, there is no systematic evaluation of their capabilities in negotiation. Such an eval
Wisdom of Committee: Diverse Distillation from Large Foundation Models and Domain Experts
Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality. For example, in our experiments, distilling from a 76M-parameter language model to a 2M-parameter recommender closes less than 40% of the performance gap between the undistilled student and the teacher. We show that introducing domain-specific experts -- which share th
NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning
Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This paper presents NeuPAN: a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: 1)
Neural network representation of quantum systems
It has been proposed that random wide neural networks near Gaussian process are quantum field theories around Gaussian fixed points. In this paper, we provide a novel map with which a wide class of quantum mechanical systems can be cast into the form of a neural network with a statistical summation over network parameters. Our simple idea is to use the universal approximation theorem of neural networks to generate ar
Transcribing Bengali Text with Regional Dialects to IPA using District Guided Tokens
Accurate transcription of Bengali text to the International Phonetic Alphabet (IPA) is a challenging task due to the complex phonology of the language and context-dependent sound changes. This challenge is even more for regional Bengali dialects due to unavailability of standardized spelling conventions for these dialects, presence of local and foreign words popular in those regions and phonological diversity across
Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things
The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intelligent, seamlessly interconnected ecosystems. However, achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sens
A polarity-aware multi-relational model for the signed interaction prediction in biological networks
Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing. While deep graph models have demonstrated success in modeling complex biological systems, existing approaches often fail to distinguish between positive and negative interactions, limiting their utility for precise pharmacological predictions. In this study, we propose a novel deep gra
ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software
Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others. In practice, reproducibility has been the dimension most often neglected. This has limited what can be automatically extracted from historical bug datasets, and has reduced their utility for downstream security resear
Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model
One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving. Although physiological signals have been extensively used in driving-related research to assess cognitive load, only a few studies have specifically focused on high cognitive load scenarios. Most existing studies tend to examine moderate or low levels of cognitive load In this
Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep
We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention and sleep. We investigate the use of LLMs to estimate attention states, sleep stages, and sleep quality and generate sleep improvement suggestions and adaptive guided imagery scripts based on electroencephalogram (EEG) and physical activity data (e.g., waveforms, power spectro
Measuring Human Contribution in AI-Assisted Content Generation
With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI
Notable events recorded on this day and month across all years.