Record 20052026 · captured 2026-08-25
The world looked up Murder of Dominic Russo and Davion Flanagan. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
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Murder of Dominic Russo and Davion Flanagan
The murder of Dominic Russo and Davion Flanagan occurred during the early morning hours of July 31, 2022, when Mackenzie Shirilla intentionally crashed her vehicle into a brick wall in Strongsville, Ohio, United States, killing two passengers: her boyfriend, D
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
Mark Fuhrman was an American law enforcement officer, author, and commentator. As a detective for the Los Angeles Police Department (LAPD), he became known for his role in the Nicole Brown Simpson and Ron Goldman murder investigation and the subsequent prosecu
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
Thomas Kane Roberts was an American voice actor. He was known for his work in animation and video games, most notably the Star Wars franchise, voicing established characters Yoda, Admiral Ackbar, Boba Fett, Qui-Gon Jinn, and C-3PO. Other notable roles include
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
Judith Marcia Blume is an American writer of children's, young adult, and adult fiction. She began writing in 1959 and has published more than 26 novels. Among her best-known works are Superfudge (1980), Are You There God? It's Me, Margaret. (1970), Tales of a
Karuppu (transl. Black) is a 2026 Indian Tamil-language fantasy action drama film directed by RJ Balaji from a screenplay he co-wrote with Ashwin Ravichandran, Rahul Raj, T. S. Gopi Krishnan and Karan Aravind Kumar. Produced by Dream Warrior Pictures, the film
Enzo Maresca is an Italian professional football manager and former player who is currently the manager of Premier League club Manchester City.
Off Campus is an American romantic drama television series created by Louisa Levy for Amazon Prime Video. It is based on the Off-Campus book series by Elle Kennedy. The series premiered on May 13, 2026 and received positive reviews. In February 2026, ahead of
2026 Islamic Center of San Diego shooting
On May 18, 2026, a shooting occurred at the Islamic Center of San Diego (ICSD), the largest mosque in San Diego, California, United States. Two teenage gunmen, identified as 17-year-old Cain Lee Clark and 18-year-old Caleb Liam Vazquez, fatally shot a security
Josep "Pep" Guardiola Sala is a Spanish football manager and former player from Catalonia. He is the global ambassador of the City Football Group and was most recently the manager of Premier League club Manchester City. Widely regarded as one of the greatest f
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
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
Prorenoic acid, or prorenoate, is a synthetic steroidal antimineralocorticoid which was never marketed.
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
The Arsenal Football Club is a professional football club based in Islington, North London, England. They compete in the Premier League, the top tier of English football. Domestically, Arsenal have won 14 league titles, a record 14 FA Cups, 2 League Cups, 18 F
Thomas Harold Massie is an American politician and engineer. A member of the Republican Party, Massie has been the United States representative for Kentucky's 4th congressional district since 2012. The district is dominated by the Kentucky side of the Cincinna
Ready or Not Here I Come (Can't Hide from Love)
"Ready or Not Here I Come (Can't Hide from Love)" is a single from American R&B vocal group The Delfonics. The song was produced by Thom Bell and released on October 22, 1968 by Philly Groove Records. The song peaked at number 35 on the U.S. pop chart, and num
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
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
Neymar da Silva Santos Júnior, known mononymously as Neymar, is a Brazilian professional footballer who plays as an attacking midfielder or a forward for Campeonato Brasileiro Série A club Santos. A goalscorer and playmaker, he is known for his dribbling, tech
Ebola, also known as Ebola virus disease (EVD) and Ebola hemorrhagic fever (EHF), is a zoonotic viral hemorrhagic fever in humans and other primates, caused by four of the six known ebolaviruses. Symptoms typically start anywhere between two days and three wee
The Eurovision Song Contest 2026 was the 70th edition of the Eurovision Song Contest. It consisted of two semi-finals on 12 and 14 May and a final on 16 May 2026, held at Wiener Stadthalle in Vienna, Austria, and presented by Victoria Swarovski and Michael Ost
The 2026 FIFA World Cup was an international football tournament held in Canada, Mexico, and the United States, from June 11 to July 19, 2026. The 48 national teams involved in the tournament were required to register a squad of up to 26 players, including thr
Star Wars: The Mandalorian and Grogu is a 2026 American science fiction film directed by Jon Favreau, who co-wrote the film with Dave Filoni and Noah Kloor. Produced by Lucasfilm and Fairview Entertainment, and distributed by Walt Disney Studios Motion Picture
The fifth and final season of the American satirical superhero television series The Boys, the first series in the franchise based on the comic book series of the same name created by Garth Ennis and Darick Robertson, was developed for television by Eric Kripk
Shaivonte Aician Gilgeous-Alexander, also known by his initials SGA, is a Canadian professional basketball player for the Oklahoma City Thunder of the National Basketball Association (NBA). He is a four-time NBA All-Star, a four-time All-NBA First Team member,
Clash in Italy was a 2026 professional wrestling pay-per-view (PPV) and livestreaming event produced by the American company WWE. The event took place on Sunday, May 31, 2026, at the Inalpi Arena in Turin, Italy, and was held for wrestlers from the promotion's
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").
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Audio-based automatic mating success prediction of giant pandas
Giant pandas, stereotyped as silent animals, make significantly more vocal sounds during breeding season, suggesting that sounds are essential for coordinating their reproduction and expression of mating preference. Previous biological studies have also proven that giant panda sounds are correlated with mating results and reproduction. This paper makes the first attempt to devise an automatic method for predicting ma
Adapted Center and Scale Prediction: More Stable and More Accurate
Pedestrian detection benefits from deep learning technology and gains rapid development in recent years. Most of detectors follow general object detection frame, i.e. default boxes and two-stage process. Recently, anchor-free and one-stage detectors have been introduced into this area. However, their accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of anchor-free detectors and the accuracy o
In recent years, artificial neural networks have developed into a powerful tool for addressing a multitude of problems for which classical solution approaches reach their limits. However, it is still unclear why gradient descent optimization algorithms with random initialization, such as the well-known batch gradient descent, are able to achieve zero training loss in many situations, even though the objective functio
Deep Neural Network for Musical Instrument Recognition using MFCCs
The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the musical domain. Musical instrument recognition is the task of instrument identification by virtue of its audio. This audio, also termed as the sound vibrations are leveraged by the model to match with the instrument classes. In this paper, we use an artificial neural network (
Traditional randomized A/B experiments assign arms with uniform random (UR) probability, such as 50/50 assignment to two versions of a website to discover whether one version engages users more. To more quickly and automatically use data to benefit users, multi-armed bandit algorithms such as Thompson Sampling (TS) have been advocated. While TS is interpretable and incorporates the randomization key to statistical in
Federated Learning with Nonvacuous Generalisation Bounds
We introduce a novel strategy to train randomised predictors in federated learning, where each node of the network aims at preserving its privacy by releasing a local predictor but keeping secret its training dataset with respect to the other nodes. We then build a global randomised predictor which inherits the properties of the local private predictors in the sense of a PAC-Bayesian generalisation bound. We consider
What Machine Learning Can Do for Focusing Aerogel Detectors
Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to
Not All Learnable Distribution Classes are Privately Learnable
We give an example of a class of distributions that is learnable up to constant error in total variation distance with a finite number of samples, but not learnable under $(\varepsilon, δ)$-differential privacy with the same target error. This weakly refutes a conjecture of Ashtiani.
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that
3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion
Point cloud completion aims to generate a complete and high-fidelity point cloud from an initially incomplete and low-quality input. A prevalent strategy involves leveraging Transformer-based models to encode global features and facilitate the reconstruction process. However, the adoption of pooling operations to obtain global feature representations often results in the loss of local details within the point cloud.
Multilayer Correlation Clustering
We establish Multilayer Correlation Clustering, a novel generalization of Correlation Clustering to the multilayer setting. In this model, we are given a series of inputs of Correlation Clustering (called layers) over the common set $V$ of $n$ elements. The goal is to find a clustering of $V$ that minimizes the $\ell_p$-norm ($p\geq 1$) of the multilayer-disagreements vector, which is defined as the vector (with dime
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still suffer from several key issues, such as hallucination problems, knowledge update issues, and lacking domain-specific expertise. The appearance of retrieval-augmented generation (RAG), which leverages an external knowledge base to augment L
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems
Many real-world decision problems require solving, again and again, combinatorial optimization instances drawn from a common distribution. A recent line of structured learning methods exploits this regularity by learning policies that pair a statistical model with a tractable combinatorial oracle, instead of solving each instance independently. Training such policies is notoriously difficult, however: the resulting e
Sharper Bounds for Chebyshev Moment Matching, with Applications
We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. This problem arises broadly across algorithms, statistics, and machine learning. By leveraging a global decay bound on the coefficients in the Chebyshev expansion of any Lipschitz function, we sharpen prior work, proving that accurate recovery in the Wasserstein distance is possibl
Fine-grained image classification has witnessed significant advancements with the advent of deep learning and computer vision technologies. However, the scarcity of detailed annotations remains a major challenge, especially in scenarios where obtaining high-quality labeled data is costly or time-consuming. To address this limitation, we introduce Precision-Enhanced Pseudo-Labeling(PEPL) approach specifically designed
TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation
In this paper, we introduce TextBoost, an efficient one-shot personalization approach for text-to-image diffusion models. Traditional personalization methods typically involve fine-tuning extensive portions of the model, leading to substantial storage requirements and slow convergence. In contrast, we propose selectively fine-tuning only the text encoder, significantly improving computational and storage efficiency.
Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models
Aligned Large Language Models (LLMs) have attracted significant attention for their safety, particularly in the context of jailbreak attacks that attempt to bypass guardrails via adversarial prompts. Among existing approaches, the Greedy Coordinate Gradient (GCG) attack pioneered automated jailbreaks through discrete token optimization; however, its low sample efficiency limits practical applicability. In particular,
Entry-level guide to the use of large language models for medical research
Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, m
A Survey of Large Language Models for Arabic Language and its Dialects
This survey offers a comprehensive overview of Large Language Models (LLMs) designed for Arabic language and its dialects. It covers key architectures, including encoder-only, decoder-only, and encoder-decoder models, along with the datasets used for pre-training, spanning Classical Arabic, Modern Standard Arabic, and Dialectal Arabic. The study also explores monolingual, bilingual, and multilingual LLMs, analyzing t
Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection
Selective parameter activation provided by Mixture-of-Expert (MoE) models have made them a popular choice in modern foundational models. However, MoEs face a fundamental tension when employed for serving. Batching, critical for performance in serving, forces the activation of all experts, thereby negating MoEs' benefits and exacerbating memory bandwidth bottlenecks. Existing work on efficient MoE inference are un
Hard-Label Black-Box Attacks on 3D Point Clouds
With the maturity of depth sensors in various 3D safety-critical applications, 3D point cloud models have been shown to be vulnerable to adversarial attacks. Almost all existing 3D attackers simply follow the white-box or black-box setting to iteratively update coordinate perturbations based on back-propagated or estimated gradients. However, these methods are hard to deploy in real-world scenarios (no model details
RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields
Robot manipulation policies, while central to the promise of physical AI, are highly vulnerable in the presence of external variations in the real world. Diagnosing these vulnerabilities is hindered by two key challenges: (i) the relevant variations to test against are often unknown, and (ii) direct testing in the real world is costly and unsafe. We introduce a framework that tackles both issues by learning a separat
Motion-2-To-3: Leveraging 2D Motion Data for 3D Motion Generations
Text-driven human motion synthesis has showcased its potential for revolutionizing motion design in the movie and game industry. Existing methods often rely on 3D motion capture data, which requires special setups, resulting in high costs for data acquisition, ultimately limiting the diversity and scope of human motion. In contrast, 2D human videos offer a vast and accessible source of motion data, covering a wider r
Visual-Spatial Systems has become increasingly essential in concrete crack inspection. However, existing methods often lacks adaptability to diverse scenarios, exhibits limited robustness in image-based approaches, and struggles with curved or complex geometries. To address these limitations, an innovative framework for two-dimensional (2D) crack detection, three-dimensional (3D) reconstruction, and 3D automatic crac
Given an unnormalized probability density $π\propto\mathrm{e}^{-V}$, estimating its normalizing constant $Z=\int_{\mathbb{R}^d}\mathrm{e}^{-V(x)}\mathrm{d}x$ or free energy $F=-\log Z$ is a crucial problem in Bayesian statistics, statistical mechanics, and machine learning. It is challenging especially in high dimensions or when $π$ is multimodal. To mitigate the high variance of conventional importance sampling esti
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