Record 05062026 · captured 2026-08-25
The world looked up Marjane Satrapi. 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.
Marjane Satrapi was an Iranian and French comic book author, film director, and children's book author. Her best-known works include the graphic novel Persepolis and its film adaptation; the graphic novel Chicken with Plums; Woman, Life, Freedom; and the Marie
On 3 December 2025, Henry Nowak, an 18‑year‑old university student, was murdered in Southampton, Hampshire, England, by 23‑year‑old Vickrum Singh Digwa. Police bodycam footage showing officers arresting Nowak as he lay dying from stab wounds sparked public out
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
Backrooms is a 2026 American science fiction psychological horror film directed and co-scored by Kane Parsons, and written by Will Soodik. It is based on Parsons's web series which was inspired by the "Backrooms" creepypasta. In the film, Clark, a furniture st
Jalen Marquis Brunson, nicknamed "Captain Clutch", and the "King of New York" is an American professional basketball player for the New York Knicks of the National Basketball Association (NBA). The son of former NBA guard Rick Brunson, he played college basket
Victor Wembanyama, nicknamed "Wemby" and "the Alien", is a French professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He was selected first overall by the Spurs in the 2023 NBA draft and is considered one of t
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
Maja Ewa Chwalińska is a Polish professional tennis player. She has career-high WTA rankings of No. 21 in singles, achieved on 8 June 2026 and No. 91 in doubles, reached on 9 June 2025. Chwalińska's best result is reaching the final of the 2026 French Open, th
Peddi is a 2026 Indian Telugu-language sports action drama film written and directed by Buchi Babu Sana. Produced by Venkata Satish Kilaru under Vriddhi Cinemas and co-produced by Ishan Saksena under IVY Entertainment and presented by Mythri Movie Makers and S
Masters of the Universe (2026 film)
Masters of the Universe is a 2026 American sword-and-sorcery film based on the media franchise by Mattel. It is the second live-action film adaptation, the 1987 film was the first. It was directed by Travis Knight and written by Chris Butler, Aaron Nee, Adam N
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
Rachel Jane Nickell was a British woman who was stabbed to death on Wimbledon Common in southwest London on 15 July 1992. The initial police investigation of the crime resulted in the arrest in controversial circumstances of an innocent man, who was acquitted.
Diana Maximovna Shnaider is a Russian professional tennis player. She has a career-high singles ranking by the WTA of No. 11, achieved on 5 May 2025, and a best doubles ranking of No. 8, reached on 16 June 2025.
Mirra Aleksandrovna Andreeva is a Russian professional tennis player. She has been ranked by the WTA as high as world No. 5 in singles, achieved in July 2025, and No. 12 in doubles, achieved in September 2025. Andreeva has won six WTA Tour–level singles titles
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
A gallus was a eunuch priest of the Phrygian goddess Cybele and her consort Attis, whose worship was incorporated into the state religious practices of ancient Rome.
Scary Movie is a 2026 American parody film directed by Michael Tiddes and written by Marlon Wayans, Shawn Wayans, Keenen Ivory Wayans, Craig Wayans, and Rick Alvarez. It is the sixth installment in the Scary Movie film series and has been referred to as the sp
Quisqualic acid is an agonist of the AMPA, kainate, and group I metabotropic glutamate receptors. It is one of the most potent AMPA receptor agonists known. It causes excitotoxicity and is used in neuroscience to selectively destroy neurons in the brain or spi
Andoni Iraola Sagarna is a Spanish professional football manager and former player who is the head coach of Premier League club Liverpool.
The Backrooms is a fictional location invented in a 2019 thread on the imageboard website 4chan. The Backrooms are usually portrayed as an impossibly large extradimensional complex of empty rooms, accessed by exiting reality. They are one of the best-known exa
Robert Lee "Peabo" Bryson was an American singer and songwriter. After collaborating with singers Luther Vandross and Cissy Houston on his debut album Peabo (1976), he signed to Capitol Records and released the 1978 albums Reaching for the Sky and Crosswinds,
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
The New York Knickerbockers, commonly called the New York Knicks, are an American professional basketball team based in the New York City borough of Manhattan. The Knicks compete in the National Basketball Association (NBA) as a member of the Atlantic Division
Robert Clive Napper is an English serial killer and rapist. He has been convicted of two murders, one manslaughter, two rapes and two attempted rapes. He was sentenced to indefinite detention at Broadmoor Hospital on 18 December 2008 for the manslaughter of Ra
Karl-Anthony Towns Jr., also known by his initials KAT, is a Dominican American professional basketball player for the New York Knicks of the National Basketball Association (NBA). He was named to the Dominican Republic national team as a 16-year-old and playe
Spencer William Pratt is an American reality television personality. In 2007, he began dating Heidi Montag, a primary cast member of the reality television series The Hills and came to prominence after being cast in the series. A feud between them and Montag's
Æthelred II, known as Æthelred the Unready, was King of the English from March 978 to December 1013 and again from February 1014 until his death. The epithet "Unready" or "Unræd" is a pun on his name in Old English, Æðel (noble) and ræd (counsel). He was the s
Dylan Robert Harper is an American professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He played college basketball for the Rutgers Scarlet Knights, and was drafted second overall in the 2025 NBA draft by the
Anna Kay Faris is an American actress, comedian, producer, writer, podcaster, and author. She rose to prominence for her comedic roles, particularly the lead part of Cindy Campbell in the Scary Movie films (2000–present). Her films as a leading actress have gr
Spider-Noir is an American superhero series developed by Oren Uziel for MGM+ and Prime Video. Based on Marvel Comics featuring the character Spider-Man Noir, the series follows an aging private investigator and superhero in 1930s New York City who grapples wit
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
The thermodynamics of human reaction times
I present a new approach for the interpretation of reaction time (RT) data from behavioral experiments. From a physical perspective, the entropy of the RT distribution provides a model-free estimate of the amount of processing performed by the cognitive system. In this way, the focus is shifted from the conventional interpretation of individual RTs being either long or short, into their distribution being more or les
Analytical Determination of Fractal Structure in Stochastic Time Series
Current methods for determining whether a time series exhibits fractal structure (FS) rely on subjective assessments on estimators of the Hurst exponent (H). Here, I introduce the Bayesian Assessment of Scaling, an analytical framework for drawing objective and accurate inferences on the FS of time series. The technique exploits the scaling property of the diffusion associated to a time series. The resulting criterio
From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogonal dimensions of valence, arousal, and dominance (VAD). These essential dimensions have become the cornerstone of sentiment analysis across many fields. By re-examining first types and then tokens for the English language, and through the use of automatically annotated h
In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new influence approximation called "lazy influence" to filter and score data while preserving privacy. To do this, each participant uses their own data to estim
Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare
The success of many healthcare programs depends on participants' adherence. We consider the problem of scheduling interventions in low resource settings (e.g., placing timely support calls from health workers) to increase adherence and/or engagement. Past works have successfully developed several classes of Restless Multi-armed Bandit (RMAB) based solutions for this problem. Nevertheless, all past RMAB approaches
Semi-Offline Reinforcement Learning for Optimized Text Generation
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time cost, and offline methods efficiently obtain reward signals by sacrificing exploration capability. We propose semi-offline RL, a novel paradigm that smoothly transits from offline to online settings, balances exploration capability and traini
Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data
Different distribution shifts require different interventions, and algorithms must be grounded in the specific shifts they address. However, methodological development for robust algorithms typically relies on structural assumptions that lack empirical validation. Advocating for an empirically grounded data-driven approach to algorithm development, we build an empirical testbed comprising natural shifts across 8 tabu
Can Language Models Learn to Listen?
We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Given an input transcription of the speaker's words with their timestamps, our approach autoregressively predicts a response of a listener: a sequence of listener facial gestures, quantized using a VQ-VAE. Since gesture is a language component, we propose treating the
Enhancing cardiovascular risk prediction through AI-enabled calcium-omics
Background. Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease. Objective. To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE predicti
Uncovering Model Processing Strategies with Non-Negative Per-Example Fisher Factorization
We introduce NPEFF (Non-Negative Per-Example Fisher Factorization), an interpretability method that aims to uncover strategies used by a model to generate its predictions. NPEFF decomposes per-example Fisher matrices using a novel decomposition algorithm that learns a set of components represented by learned rank-1 positive semi-definite matrices. Through a combination of human evaluation and automated analysis, we d
Exploration via linearly perturbed loss minimisation
We introduce exploration via linear loss perturbations (EVILL), a randomised exploration method for structured stochastic bandit problems that works by solving for the minimiser of a linearly perturbed regularised negative log-likelihood function. We show that, for the case of generalised linear bandits, EVILL reduces to perturbed history exploration (PHE), a method where exploration is done by training on randomly p
Only a small fraction of patients with chronic kidney disease (CKD) progress to dialysis, creating severe class imbalance that limits the performance of machine learning models for early dialysis prediction. This challenge is compounded by the binary structure of electronic health record (EHR) data, for which most existing augmentation methods were not designed. We propose Binary Gaussian Copula Synthesis (BGCS), a t
Know Yourself Better: Diverse Object-Related Features Improve Open Set Recognition
Open set recognition (OSR) is a critical aspect of machine learning, addressing the challenge of detecting novel classes during inference. Within the realm of deep learning, neural classifiers trained on a closed set of data typically struggle to identify novel classes, leading to erroneous predictions. To address this issue, various heuristic methods have been proposed, allowing models to express uncertainty by stat
Recently a million of biological neurons (BNN) has turned out better from modern RL methods in playing Pong~\cite{RL}, reminding they are still qualitatively superior e.g. in learning, flexibility and robustness - suggesting to try to improve current artificial e.g. MLP/KAN for better agreement with biological. There is proposed extension of KAN approach to neurons containing model of local joint distribution: $ρ(\ma
GridPE: A Grid Cell-Inspired Unified Position Embedding for Arbitrary-Dimensional Spaces
Understanding spatial relationships across all dimensions is fundamental for intelligent systems. However, existing positional embeddings, such as Rotary Positional Embedding (RoPE), lack theoretical guarantees for high-dimensional spatiotemporal tasks like video understanding and robotic navigation. Inspired by the hexagonal periodic coding of grid cells in mammalian spatial cognition, we propose GridPE -- a novel p
Separation Power of Equivariant Neural Networks
The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy for its expressivity. Indeed, knowing the separation power of a family of models is a necessary condition to obtain fine-grained universality results. In this paper, we analyze the separation power of equivariant neural networks, such as convolutional and permutation-invariant ne
What Makes Two Language Models Think Alike?
Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether two models share a similar representational geometry, but they cannot explain why. Here, we propose a new, simple, approach to address this question. This approach maps neural activity in each model layer onto a set of interpretable linguistic features and quantifies how mu
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. The advent of large language models (LLMs), shows their impressive capability of textual understanding through large-scale pretraining, which implies the great potential of extractive snippet generation. In this paper, we systematically investigated two indispens
FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting
Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns. Accurate forecasting is critical for monitoring these phenomena and supporting mitigation strategies. While recent data-driven models for time-series forecasting, including CNNs, RNNs, and attention-based
Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
Learning conditional distributions $π^*(\cdot|x)$ is a central problem in machine learning, which is typically approached via supervised methods with paired data $(x,y) \sim π^*$. However, acquiring paired data samples is often challenging, especially in problems such as domain translation. This necessitates the development of $\textit{semi-supervised}$ models that utilize both limited paired data and additional unpa
Comprehensive Monitoring of Air Pollution Hotspots Using Sparse Sensor Networks
Urban air pollution hotspots pose significant health risks, yet their detection and analysis remain limited by the sparsity of public sensor networks. This paper addresses this challenge by combining predictive modeling and mechanistic approaches to comprehensively monitor pollution hotspots. We enhanced New Delhi's existing sensor network with 28 low-cost sensors, collecting PM2.5 data over 30 months from May 1,
Decomposition Polyhedra of Piecewise Linear Functions
In this paper we contribute to the frequently studied question of how to decompose a continuous piecewise linear (CPWL) function into a difference of two convex CPWL functions. Every CPWL function has infinitely many such decompositions, but for applications in optimization and neural network theory, it is crucial to find decompositions with as few linear pieces as possible. This is a highly challenging problem, as w
On Efficient Variants of Segment Anything Model: A Survey
The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive performance comes with significant computational and resource demands, making it challenging to deploy in resource-limited environments such as edge devices. To address this, a variety of SAM variants have been proposed to enhance efficiency wh
ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
Autonomous web agents solve complex browsing tasks, yet existing benchmarks measure only whether an agent finishes a task, ignoring whether it does so safely or in a way enterprises can trust. To integrate these agents into critical workflows, safety and trustworthiness (ST) are prerequisite conditions for adoption. We introduce \textbf{\textsc{ST-WebAgentBench}}, a configurable and easily extensible suite for evalua
Channel-Wise Mixed-Precision Quantization for Large Language Models
Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes. Weight-only quantization presents a promising solution to reduce the memory footprint of LLMs. However, existing approaches primarily focus on integer-bit quantization, limiti
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