Record 07092026 · captured 2026-09-08
The world looked up Killing of the Clancy children. 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.
Killing of the Clancy children
On January 24, 2023, Lindsay Clancy fatally strangled her three children—five-year-old Cora, three-year-old Dawson, and eight-month-old Callan—at the family's home in Duxbury, Massachusetts, United States. She then attempted suicide by cutting her wrists and n
2026 Saxony-Anhalt state election
The 2026 Saxony-Anhalt state election was held on 6 September 2026 to elect the 9th Landtag of Saxony-Anhalt, two weeks before the state elections in Berlin and Mecklenburg-Vorpommern. It resulted in the highest vote share for the far-right in Germany since 19
Andrew Williams was an American musician and professional wrestler. He is best known as the rhythm guitarist of the metalcore band Every Time I Die.
Mirzapur: The Movie is a 2026 Indian Hindi-language action crime thriller film directed by Gurmeet Singh and written by Puneet Krishna. Produced by Ritesh Sidhwani and Farhan Akhtar under Excel Entertainment, the film stars Pankaj Tripathi, Ali Fazal, Divyennd
Iva Jovic is an American professional tennis player. She has a career-high WTA singles ranking of No. 14 achieved on August 24, 2026, and a best doubles ranking of No. 64 reached on August 24, 2026. She has won one WTA Tour title, at the 2025 Guadalajara Open.
Hanuman Ansh is a 2026 Indian Hindi-language biographical devotional drama film written, directed and produced by Dr Vishal Chaturvedi under his banner, Swambhu Media Network. The film is first instalment of the film trilogy based on his book Divine Detour: Th
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
Katie Taylor is an Irish former professional boxer and footballer. She is the former three-time undisputed champion, having previously been the undisputed and undefeated lineal world lightweight champion from 2019 to 2024. She then vacated her titles and went
Gopalaswamy Doraiswamy Naidu was an Indian innovator, inventor, industrialist, and educator. He redesigned and transformed imported technologies into practical and affordable innovations for India. He is widely regarded as a versatile genius, his contributions
Toxic: A Fairy Tale for Grown-Ups is a 2026 Indian psychological thriller gangster film directed by Geetu Mohandas and jointly produced by Venkat K. Narayana and Yash through KVN Productions and Monster Mind Creations respectively. It stars Yash, Kiara Advani,
The Gentlemen (2024 TV series)
The Gentlemen is an action comedy television series created by Guy Ritchie for Netflix and is a spin-off of Ritchie's 2019 film. The series stars Theo James in the lead role and premiered on March 7, 2024. In August 2024, the series was renewed for a second se
Ainsley Cory Maitland-Niles is an English professional footballer who plays as a right-back for Premier League club Everton.
Spider-Man: Brand New Day is a 2026 American superhero film based on the Marvel Comics character Spider-Man. Produced by Columbia Pictures, Marvel Studios, and Pascal Pictures, and distributed by Sony Pictures Releasing, it is the 38th film in the Marvel Cinem
Lane Monte Kiffin is an American football coach who is the head coach for the LSU Tigers. He previously served as the head coach of the Tennessee Volunteers in 2009, the USC Trojans from 2010 to 2013, the Florida Atlantic Owls from 2017 to 2019, and the Ole Mi
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
Coyote vs. Acme is a 2026 American comedy film directed by Dave Green and written by Samy Burch, who developed the story with James Gunn and Jeremy Slater. Loosely based on the 1990 The New Yorker magazine article "Coyote v. Acme" by Ian Frazier, it follows Wi
Neem Karoli Baba also known as Neeb Karori Baba and by his followers as Maharaj-ji, was a Hindu guru and devotee of the Hindu deity Hanuman.
Gloria Marie Steinem was an American journalist and social movement activist who emerged as a nationally recognized leader of second-wave feminism in the United States in the late 1960s and early 1970s, and who remained particularly prominent in the 1980s and
Alternative for Germany is a far-right, right-wing populist, national conservative, and in parts völkisch nationalist political party in Germany. It has 151 members of the Bundestag and 15 members of the European Parliament. It is the largest opposition party
Frances Tiafoe Jr. is an American professional tennis player, nicknamed "Big Foe" or simply "Foe". He has a career-high singles ranking of world No. 10, achieved in June 2023, and a best doubles ranking of No. 160, reached in November 2021. Tiafoe has won four
.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
2027 AFC U-20 Asian Cup qualification
The 2027 AFC U-20 Asian Cup qualification was an international men's under-20 football competition organized by the Asian Football Confederation (AFC). The tournament was held from 31 August to 6 September 2026 to determine the participating teams for the 2027
Alexandra Maniego Eala is a Filipino professional tennis player. On August 24, 2026, she achieved a career-high singles ranking of world No. 18, the highest ever for a Filipino and Southeast Asian in WTA Tour history. She is the first Filipino to break into th
Dolly Rebecca Parton Dean was an American singer-songwriter, actress, and businesswoman. Dubbed the "Queen of Country", Parton was one of the most successful country music performers in history. She was also known for her cultural influence and philanthropy, p
The Whisper Man is a 2026 American crime thriller film directed by James Ashcroft and written by Ben Jacoby and Chase Palmer. It is based on the novel of the same name by Alex North and stars Robert De Niro, Michelle Monaghan, and Adam Scott.
Buddy is a 2026 American independent supernatural comedy horror film directed by Casper Kelly and written by Kelly and Jamie King. The film stars Cristin Milioti, Delaney Quinn, Patton Oswalt, Clint Howard, Michael Shannon, Topher Grace, and Keegan-Michael Key
The Equal Earth map projection is an equal-area pseudocylindrical global map projection invented by Bojan Šavrič, Bernhard Jenny, and Tom Patterson in 2018. It is inspired by the widely used Robinson projection, but unlike the Robinson projection, it retains t
Saxony-Anhalt is a landlocked state of Germany, bordering the states of Brandenburg, Saxony, Thuringia and Lower Saxony. It covers an area of 20,555 square kilometres (7,936 sq mi) and has a population of about 2.14 million inhabitants, making it the 8th-large
Theodore Peter James Kinnaird Taptiklis is an English actor and producer. He gained recognition for playing Tobias Eaton in The Divergent Series (2014–2016). He has starred in the horror films Underworld: Awakening (2012) and Underworld: Blood Wars (2016), the
Mayday is a 2026 American action comedy film written and directed by John Francis Daley and Jonathan Goldstein, and produced by Apple Original Films, Skydance Media, and Maximum Effort. It stars Ryan Reynolds, Kenneth Branagh, Marcin Dorociński, Maria Bakalova
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?
Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted tree, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 2.08 for classification and 1.39 for regression. This outperformance strengthens with class granularity and persists across subsamples and after transaction costs. Spanning tests show t
We find experimentally that when artificial neural networks are connected in parallel and trained together, they display the following properties. (i) When the parallel-connected neural network (PNN) is optimized, each sub-network in the connection is not optimized. (ii) The contribution of an inferior sub-network to the whole PNN can be on par with that of the superior sub-network. (iii) The PNN can output the corre
Quality-diversity in dissimilarity spaces
The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.
A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
We introduce a neural-preconditioned iterative solver for Poisson equations with mixed boundary conditions. Typical Poisson discretizations yield large, ill-conditioned linear systems. Iterative solvers can be effective for these problems, but only when equipped with powerful preconditioners. Unfortunately, effective preconditioners like multigrid require costly setup phases that must be re-executed every time domain
LLMs have become increasingly capable at accomplishing a range of specialized-tasks and can be utilized to expand equitable access to medical knowledge. Most medical LLMs have involved extensive fine-tuning, leveraging specialized medical data and significant, thus costly, amounts of computational power. Many of the top performing LLMs are proprietary and their access is limited to very few research groups. However,
Measuring proximity to standard planes during fetal brain ultrasound scanning
This paper presents a pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use, demonstrating the feasibility of continuous, real-time proximity feedback for navigation to the standard planes (SPs) in the fetal brain. We propose a semi-supervised segmentation model that uses labeled SPs and unlabeled slices from 3D US volumes (non-SPs), achieving 0.93 mean Intersection over Union (mIoU)
Momentum-based gradient descent methods for Lie groups
Polyak's Heavy Ball (PHB; Polyak, 1964), a.k.a. Classical Momentum, and Nesterov's Accelerated Gradient (NAG; Nesterov, 1983) are well-established momentum-descent methods for optimization. Although the latter generally outperforms the former, primarily, generalizations of PHB-like methods to nonlinear spaces have not been sufficiently explored in the literature. In this paper, we propose a generalization of
A Survey on Semantic Modeling for Building Energy Management
Building Energy Management (BEM) is central to reducing energy use and CO2 emissions in the building sector. Although IoT technologies now provide extensive operational data, heterogeneous data models, device descriptions, and contextual representations continue to limit semantic interoperability, limiting the development of generalisable, autonomous, context-aware BEM applications. Ontologies address this challenge
An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders
Can pretrained models generalize to new datasets without any retraining? We deploy pretrained image models on datasets they were not trained for, and investigate whether their embeddings form meaningful clusters. Our suite of benchmarking experiments uses encoders pretrained solely on ImageNet-1k with either supervised or self-supervised training techniques, deployed on image datasets that were not seen during traini
Procedural Content Generation via Generative Artificial Intelligence
The attempt to utilize machine learning in procedural content generation (PCG) has been made in the past. In this survey paper, we investigate how generative artificial intelligence (AI), which saw a significant increase in interest in the mid-2010s, is being used for PCG. We review applications of generative AI for the creation of various types of content, including terrains, items, and even storylines. While genera
Small Molecule Optimization with Large Language Models
Molecular optimization, the process of designing molecules with desirable properties, represents a critical challenge in drug discovery. Recent advancements in large language models (LLMs) have opened new opportunities for their integration with traditional molecular optimization algorithms to improve performance. In this work, we propose Molecular Language Model powered Evolutionary Algorithm (Mol-E), an evolutionar
Multilingual Models for Check-Worthy Social Media Posts Detection
This work presents an extensive study of transformer-based NLP models application for detection of social media posts that contain verifiable factual claims and harmful claims. The study covers various activities, including dataset collection, dataset pre-processing, architecture selection, setup of settings, model training (fine-tuning), model testing, and implementation. The study includes a comprehensive analysis
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Operator learning, the approximation of mappings between infinite-dimensional function spaces using machine learning, has gained increasing research attention in recent years. Operator approximations can serve as efficient surrogate models for problems in computational science and engineering, complementing traditional methods. However, despite their empirical success, our understanding of the underlying mathematical
Explainable Clustering of Mixture Models
The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with $K$ leaves can approximate a given clustering. The performance of the tree is measured via the \textit{price of explainability}, defined as the ratio between the clustering cost of the tree (where every leaf is a cluster) and the optimal cost. Several recent works have given wors
Hyperedge Anomaly Detection with Hypergraph Neural Network
Hypergraph is a data structure that enables us to model higher-order associations among data entities. Conventional graph-structured data can represent pairwise relationships only, whereas hypergraph enables us to associate any number of entities, which is essential in many real-life applications. Hypergraph learning algorithms have been well-studied for numerous problem settings, such as node classification, link pr
Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points
Security vulnerabilities in software can have severe consequences; however, manual vulnerability detection is costly and does not scale, especially as agentic coding frameworks increase the rate of code production. Over the last decade, a large body of research has applied machine learning machine learning to automate vulnerability detection (ML4AVD), yet self-reported performance on the most popular datasets shows n
DeltaGNN: Graph Neural Network with Information Flow Control
Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations in the message passing process. When applied to semi-supervised node classification, the message-passing enables GNNs to understand short-range spatial interactions, but also causes them to suffer from over-smoothing and over-squashing. These challenges hinder model expre
We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and ma
TSMini: A Simple Yet Highly Effective Trajectory Similarity Learning Model
Trajectory similarity is fundamental to many spatio-temporal data mining applications. Recent studies propose deep learning models to approximate conventional trajectory similarity measures, exploiting their fast inference time once trained. Although efficient inference has been reported, challenges remain in similarity approximation accuracy due to difficulties in trajectory granularity modeling and in exploiting si
FSPGD: Rethinking Black-box Attacks on Semantic Segmentation
Black-box adversarial attacks on semantic segmentation remain a challenging problem, particularly in the black-box transfer attack setting where perturbations crafted on a surrogate model are expected to mislead unseen target models. Existing methods typically operate only on output logits and thus fail to account for the spatial structure and class-wise feature relationships that are crucial for dense prediction. To
Graph Foundation Models for Recommendation: A Comprehensive Survey
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role in improving ranking accuracy. Among these, graph neural networks (GNNs) excel at extracting higher-order structural information, while large language models (LLMs) are designed to process and comprehend natural language, making both approac
How far can we go with ImageNet for Text-to-Image generation?
Recent text-to-image (T2I) generation models have achieved remarkable sucess by training on billion-scale datasets, following a `bigger is better' paradigm that prioritizes data quantity over availability (closed vs open source) and reproducibility (data decay vs established collections). We challenge this established paradigm by demonstrating that one can achieve capabilities of models trained on massive web-scr
The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent particularly challenging applications for this task due to the complexity of the strongly coupled physics involved and the extremely harsh and hostile environments, especially for new technologies such as Generation-IV reactors. Data-driven
Collecting real-world vehicle accident videos for autonomous driving research is challenging due to their rarity and complexity. While existing driving video generation methods may produce visually realistic videos, they often fail to deliver physically realistic simulations because they lack the capability to generate accurate post-collision trajectories. In this paper, we introduce AccidentSim, a novel framework th
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitioning from fast and intuitive thinking (System 1) to slow and deep reasoning (System 2). While System 2 reasoning improves task accuracy, it often incurs substantial computational costs due to its slow thinking nature and inefficient or unnecessary reasoning behaviors. In contrast,
Origins publishing files written for machines rather than people. Measured against a frozen cohort, so a change in the number means a change in adoption.
| Signal | Web head Tranco top 1,000 n=1,000 | Web tail sampled to rank 100k n=1,000 | AI-native model & dev platforms n=110 |
|---|---|---|---|
| Any agent-facing signal | 9.5% (95) | 3.7% (37) | 75.45% (83) |
| llms.txt (apex domain) | 7.4% (74) | 3% (30) | 60% (66) |
| llms.txt (docs subdomain) | 3.2% (32) | 0.9% (9) | 44.55% (49) |
| .well-known/mcp.json | 0.6% (6) | 0.2% (2) | 7.27% (8) |
| .well-known/agents.txt | 0.3% (3) | 0.1% (1) | 0% (0) |
| ai.txt | 0.2% (2) | 0.1% (1) | 0% (0) |
| ai-plugin.json (deprecated) | 0.7% (7) | 0.1% (1) | 0.91% (1) |
Notable events recorded on this day and month across all years.