Record 11062026 · captured 2026-08-25
The world looked up Murder of Austin Metcalf. 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.
On April 2, 2025, Austin Metcalf, a 17-year-old student at Memorial High School, was murdered by Karmelo Anthony, a Centennial High School student of the same age, while attending a school track meet in Frisco, Texas, United States. Anthony stabbed Metcalf aft
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
Chinnasaamy Periyamaya, better known by his stage name Bharathiraja, was an Indian film director, producer, screenwriter and actor who worked mainly in the Tamil film industry. Making his debut in 1977 with 16 Vayathinile, he was known for realistic and sensit
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
Disclosure Day is a 2026 American science fiction thriller film directed and produced by Steven Spielberg from a screenplay by David Koepp, based on a story by Spielberg. The film stars an ensemble cast, including Emily Blunt, Josh O'Connor, Colin Firth, Eve H
2026 Peruvian general election
General elections were held in Peru from 12 to 13 April 2026 to elect the president, vice presidents, and the Congress of the Republic of Peru. As no presidential candidate achieved a majority of votes in the first round, a runoff election was held on 7 June.
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
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
.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
Graham Cunningham Platner is an American oyster farmer, Marine Corps veteran, and politician. Platner was the Democratic nominee in the 2026 US Senate election in Maine until he ended his campaign in July.
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
The Social Reckoning is an upcoming American biographical drama thriller film written, co-produced, and directed by Aaron Sorkin. A companion piece to The Social Network (2010), it dramatizes the 2021 Facebook leak by the whistleblower Frances Haugen and Jeff
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
Bypass Road is a 2019 Indian Hindi-language thriller film directed by Naman Nitin Mukesh and written by Neil Nitin Mukesh. The film features Neil Nitin Mukesh, Adah Sharma and Shama Sikander in lead roles. The film is produced by Neil Nitin Mukesh in associati
St. James's Hall was a concert hall in London that opened on 25 March 1858, designed by architect and artist Owen Jones, who had decorated the interior of the Crystal Palace. It was situated between the Quadrant in Regent Street and Piccadilly, and Vine Street
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
Manoj Kumar Bharathiraja was an Indian actor, film director, producer and playback singer who worked in Tamil cinema. He was the son of director Bharathiraja and made his acting debut with his father's Taj Mahal in 1999.
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
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.
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
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
Danielle Fabiola "Inde" Navarrette is an American actress and former online streamer. She began her acting career as a teenager with roles in short films, before landing roles in the Netflix drama series 13 Reasons Why (2020) and The CW's superhero drama serie
Whalefall is an upcoming American survival thriller film directed and produced by Brian Duffield, based on the 2023 novel by Daniel Kraus, who co-wrote the script with Duffield. The film follows a scuba diver who, while searching for the remains of his father,
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
Carmelo Kyam Anthony is an American former professional basketball player. Nicknamed "Melo", Anthony played 19 seasons in the National Basketball Association (NBA) and is a ten-time NBA All-Star and six-time All-NBA Team member. He played college basketball fo
Teach You a Lesson (Korean: 참교육) is a 2026 South Korean action school drama television series written by Lee Nam-kyu, Kim Da-hee, and Moon Jong-ho, directed by Hong Jong-chan, and starring Kim Mu-yeol, Lee Sung-min, Jin Ki-joo, and Pyo Ji-hoon. Based on the Na
Jeffrey Preston Bezos is an American businessman, and the founder, executive chairman, and former president and CEO of Amazon, the world's largest e-commerce and cloud computing company. According to the Bloomberg Billionaires Index and Forbes, he was the worl
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
Widow's Bay is an American comedy horror television series created by Katie Dippold for Apple TV, and starring Matthew Rhys, Kate O'Flynn, Kevin Carroll, Dale Dickey, Kingston Rumi Southwick, and Stephen Root. The series is set in the fictional New England isl
Colman Jason Domingo is an American actor, playwright and director. Prominent on both screen and stage since the 2010s, Domingo has received various accolades, including a Primetime Emmy Award, and nominations for two Academy Awards and two Tony Awards. Time m
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
LSTM based IoT Device Identification
While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market. In this environment, IoT device identification methods provide a preventive security measure as an important factor in identifying these devices and detecting the vulnerabilities they suffer from. In this study, we present an end-to-end
Causal Clothes-Invariant Feature Learning for Cloth-Changing Person Re-ID
In cloth-changing person re-identification (CCReID), it is critical to learn clothes-invariant feature, which can provide discriminative ID features that remain robust against clothing changes. However, a spurious correlation currently limits existing ReID methods from effectively extracting these clothing-invariant features. This spurious correlation arises from clothing ownership: clothing is rarely shared across d
Automatic speech recognition systems based on deep learning are mainly trained under empirical risk minimization (ERM). Since ERM utilizes the averaged performance on the data samples regardless of a group such as healthy or dysarthric speakers, ASR systems are unaware of the performance disparities across the groups. This results in biased ASR systems whose performance differences among groups are severe. In this st
Improving Generalization and Data Efficiency with Diffusion in Offline Multi-agent RL
We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL). Different from existing algorithms that rely mainly on conservatism in policy design, DOM2 enhances policy expressiveness and diversity based on diffusion model. Specifically, we incorporate a diffusion model into the policy network and propose a trajectory-based data-reweighting scheme in training. Th
On the Trade-off between the Number of Nodes and the Number of Trees in a Random Forest
In this paper, we focus on the prediction phase of a random forest and study the problem of representing a bag of decision trees using a smaller bag of decision trees, where we only consider binary decision problems on the binary domain and simple decision trees in which an internal node is limited to querying the Boolean value of a single variable. As a main result, we show that the majority function of $n$ variable
Reliable predictions of critical phenomena, such as weather, wildfires and epidemics often rely on models described by Partial Differential Equations (PDEs). However, simulations that capture the full range of spatio-temporal scales described by such PDEs are often prohibitively expensive. Consequently, coarse-grained simulations are usually deployed that adopt various heuristics and empirical closure terms to accoun
Benchmarking Cross-Domain Audio-Visual Deception Detection
Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior. Conventional contact-based techniques, like polygraph devices, rely on physiological signals to determine the authenticity of an individual's statements. Nevertheless, recent developments in automated deception detection have demonstrated that multimodal features derived from both
Guiding Frame-Level CTC Alignments Using Self-knowledge Distillation
Transformer encoder with connectionist temporal classification (CTC) framework is widely used for automatic speech recognition (ASR). However, knowledge distillation (KD) for ASR displays a problem of disagreement between teacher-student models in frame-level alignment which ultimately hinders it from improving the student model's performance. In order to resolve this problem, this paper introduces a self-knowled
FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
Large Language Models (LLMs) have provided a new pathway for Named Entity Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting methods avoid the need for training, conserve substantial computational resources, and rely on minimal annotated data. Previous studies have achieved comparable performance to fully supervised BERT-based fine-tuning approaches on general NER benchmarks. However, none of t
We introduce a generative learning framework to model high-dimensional parametric systems using gradient guidance and virtual observations. We consider systems described by Partial Differential Equations (PDEs) discretized with structured or unstructured grids. The framework integrates multi-level information to generate high fidelity time sequences of the system dynamics. We demonstrate the effectiveness and versati
BioMamba: Domain-Adaptive Biomedical Language Models
Background. Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-based models, this trade-off has not been systematically studied across biomedical literature and clinical text. Methods. We developed BioMamba, a family of biomedical Mamba2 models at five scales obtained by continued pretraining of released public Mamba2 checkpoints on a
Fluidic injection offers a promising solution to improve the performance of the overexpanded single expansion ramp nozzles (SERNs) during vehicle acceleration. However, determining the injection parameters that yield the best overall performance across multiple nozzle operating conditions remains a challenge. The gradient-based optimization method requires gradients of injection parameters at each design point, which
Temporal2Seq: A Unified Framework for Temporal Video Understanding Tasks
With the development of video understanding, there is a proliferation of tasks for clip-level temporal video analysis, including temporal action detection (TAD), temporal action segmentation (TAS), and generic event boundary detection (GEBD). While task-specific video understanding models have exhibited outstanding performance in each task, there remains a dearth of a unified framework capable of simultaneously addre
Neuron-based Personality Trait Induction in Large Language Models
Large language models (LLMs) have become increasingly proficient at simulating various personality traits, an important capability for supporting related applications (e.g., role-playing). To further improve this capacity, in this paper, we present a neuron-based approach for personality trait induction in LLMs, with three major technical contributions. First, we construct PersonalityBench, a large-scale dataset for
Program Evaluation with Remotely Sensed Outcomes
We study causal inference in experiments and quasi-experiments, where the economic outcome is imperfectly measured by a remotely sensed variable. The remotely sensed variable is low-cost, scalable, and predictive of the economic outcome in observational data; examples include satellite imagery and mobile phone activity. We model the remotely sensed variable as post-outcome: variation in the economic outcome causes va
Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption
The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management. Accurately predicting DER adoption is critical for proactive infrastructure planning, but the inherent uncertainty and spatial disparity of DER growth complicate traditional forecasting approaches. Moreover, the hierarchical structure of distribution grids demands that predictio
Artificial Intelligence (AI) is reshaping many societal domains, raising critical questions about its risks, benefits, and the potential misalignment between public and academic perspectives. This study examines how the general public (N=1110) -- individuals who interact with or are impacted by AI technologies -- and academic AI experts (N=119) -- those elites shaping AI development -- perceive AI's capabilities
STEAM: Squeeze and Transform Enhanced Attention Module
Channel and spatial attention mechanisms introduced in earlier work enhance the representational capabilities of deep convolutional neural networks (CNNs) but often increase parameter and computational costs. While recent approaches focus solely on efficient feature context modeling for channel attention, we aim to model both channel and spatial attention comprehensively with minimal parameters and reduced computatio
Demonstrating Data-to-Knowledge Pipelines for Connecting Production Sites in the World Wide Lab
The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digita
Online optimisation for dynamic electrical impedance tomography
Online optimisation studies the convergence of optimisation methods as the data embedded in the problem changes. Based on this idea, we propose a primal dual online method for nonlinear time-discrete inverse problems. We analyse the method through regret theory and demonstrate its performance in real-time monitoring of moving bodies in a fluid with Electrical Impedance Tomography (EIT). To do so, we also prove the se
As artificial intelligence (AI) continues to advance, understanding public perceptions -- including biases, risks, and benefits -- is essential for guiding research priorities and AI alignment, shaping public discourse, and informing policy. This exploratory study investigates cultural differences in mental models of AI using 71 imaginaries of AI's potential futures. Drawing on cross-cultural convenience samples
Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling
Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities. In these scenarios, schedulers must make real-time decisions to satisfy both delay and resource constraints without prior knowledge of system d
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows
Modeling and simulation of complex fluid flows with dynamics that span multiple spatio-temporal scales is a fundamental challenge in many scientific and engineering domains. Full-scale resolving simulations for systems such as highly turbulent flows are not feasible in the foreseeable future, and reduced-order models must capture dynamics that involve interactions across scales. In the present work, we propose a nove
Application of Tabular Transformer Architectures for Operating System Fingerprinting
Operating System (OS) fingerprinting is essential for network management and cybersecurity, enabling accurate device identification based on network traffic analysis. Traditional rule-based tools such as Nmap and p0f face challenges in dynamic environments due to frequent OS updates and obfuscation techniques. While Machine Learning (ML) approaches have been explored, Deep Learning (DL) models, particularly Transform
FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling. The lack of samples leads to difficulty simulating their spread with physical models and limited scientific understanding of PFAS transport in surface waters. Yet, rich geospati
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