Record 17042026 · captured 2026-08-25
The world looked up Justin Fairfax. 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.
Justin Edward Fairfax was an American lawyer and politician who served as the 41st lieutenant governor of Virginia from 2018 to 2022. A member of the Democratic Party, he was the second African American to be elected to statewide office in Virginia, after Doug
Alexander Manninger was an Austrian footballer who played as a goalkeeper. He played internationally for the Austria national team on 33 occasions, including at UEFA Euro 2008, and represented football clubs in Italy, Germany, Austria and England.
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
List of highest-grossing Indian films
This ranking lists the highest-grossing Indian films produced by Indian cinema, based on conservative global box office estimates as reported by organisations classified as green by Wikipedia. The figures are not adjusted for inflation. However, there is no of
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
Castalius is a butterfly genus in the family Lycaenidae. They are commonly known as Pierrots. This name is also often used for the very closely related genus Tarucus.
Food and Drug Administration (Philippines)
The Food and Drug Administration (FDA) of the Philippines, formerly the Bureau of Food and Drugs, is a health regulatory agency under the Department of Health. It was created in 1963 by Republic Act No. 3720, amended in 1987 by Executive Order 175 otherwise kn
Sierras de Cazorla, Segura y Las Villas Natural Park
Sierras de Cazorla, Segura y Las Villas Natural Park is a natural park in the eastern and northeastern part of the province of Jaén, Spain, established in 1986. With an area of 2,099.2 square kilometres (810.5 sq mi), it is the largest protected area in Spain
Michael Akpovie Olise is a professional footballer who plays as a winger or attacking midfielder for Bundesliga club Bayern Munich and the France national team. Widely regarded as one of the best players in the world, he is known for his creative playmaking, t
Judith Eva Barsi was an American child actress. She began her career in television, making appearances in commercials and television series, as well as the 1987 film Jaws: The Revenge. She also provided the voices of Ducky in The Land Before Time and Anne-Mari
Lee Cronin's The Mummy is a 2026 supernatural horror film written and directed by Lee Cronin. A reimagining of The Mummy franchise based around the Nasmaranian, an ancient Egyptian demon that possesses victims with exorcism themes, the film stars Jack Reynor,
Eric Michael Swalwell is an American former politician who served as a U.S. representative from California from 2013 to 2026. A member of the Democratic Party, Swalwell previously served on the city council for Dublin, California from 2010 to 2013.
Street Fighter is an upcoming American action comedy film directed by Kitao Sakurai and written by Sakurai and T. J. Fixman. It is the third live-action feature-length film based on the Street Fighter video game series by Capcom. It features an ensemble cast i
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
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
Survivor 50: In the Hands of the Fans
Survivor 50: In the Hands of the Fans is the 50th season of the American competitive reality television series Survivor. It premiered on February 25, 2026, on CBS in the United States, and it is the eighteenth consecutive season to be filmed in the Mamanuca Is
Avatar Aang: The Last Airbender
Avatar Aang: The Last Airbender is a 2026 American animated fantasy action-adventure film directed by Lauren Montgomery from a screenplay by Tim Hedrick and Christopher Yost, based on a story by Bryan Konietzko, Michael Dante DiMartino, Hedrick, and Kenneth Li
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
Focker-in-Law is an upcoming American comedy film written for the screen and directed by John Hamburg from a story by Hamburg, Ilana Glazer, and Austen Earl. It is the fourth installment in the Fockers film series and the sequel to Little Fockers (2010). Rober
Braden Eric Peters, better known as Clavicular or Clav, is an American livestreamer, internet personality, and influencer. He became known in 2025 on TikTok and Kick for his usage of incelosphere slang, for his "looksmaxxing" content, incorporating controversi
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
The Boys is an American satirical superhero streaming television series developed by Eric Kripke for Amazon Prime Video. Based on the comic book series of the same name by Garth Ennis and Darick Robertson, it follows the eponymous team of vigilantes as they co
Ruby Rose Langenheim is an Australian actress, television presenter, and model. She gained prominence for her role in season three of the Netflix series Orange Is the New Black (2015–2016) and for portraying Kate Kane / Batwoman in the Arrowverse television fr
WrestleMania 42, also promoted as WrestleMania Vegas, was a 2026 professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 42nd annual WrestleMania and took place as a two-night event on Saturday, April 18 and Sunday, April
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
Samrat Choudhary is an Indian politician who is serving as the 24th Chief Minister of Bihar since 15 April 2026. He has been a member of the Bihar Legislative Assembly representing Tarapur Assembly constituency since 2025 and previously served as deputy chief
Erica G. Schwartz is an American health official who is Director of the Centers for Disease Control and Prevention (CDC). She previously served as Deputy Surgeon General from January 2019 to April 2021, with the rank of rear admiral in the U.S. Public Health S
Beef is an American comedy drama anthology television series created by Lee Sung Jin for Netflix. Season 1 stars Steven Yeun and Ali Wong as Danny Cho and Amy Lau, two strangers whose involvement in a road rage incident escalates into a prolonged feud. Appeari
2026 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. The election saw the defeat
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements
We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token
Help! Need Advice on Identifying Advice
Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increa
How people talk about each other: Modeling Generalized Intergroup Bias and Emotion
Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group. While this has led to progress recognizing and mitigating negative bias, and having a clear notion of the targeted group is necessary, it is not always practical. In this work we extrapolate to a broader notion of bias, rooted in social science and psychology literature. We move towards predicti
RECALL: Rehearsal-free Continual Learning for Object Classification
Convolutional neural networks show remarkable results in classification but struggle with learning new things on the fly. We present a novel rehearsal-free approach, where a deep neural network is continually learning new unseen object categories without saving any data of prior sequences. Our approach is called RECALL, as the network recalls categories by calculating logits for old categories before training new one
Universal hidden monotonic trend estimation with contrastive learning
In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach related to the Mann-Kendall test, a standard monotonic trend detection method and call it contrastive trend estimation (CTE). We show that the CTE method identifies any hidden trend underlying temporal data while avoiding the standard assumptions used for monotonic trend ide
An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning
In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay value when learning a new task. Prior research has typically relied on either a single adaptive centroid or multiple fixed centroids to represent each class in the latent space. However, such methods struggle when class data streams are inheren
Competitive plasticity to reduce the energetic costs of learning
The brain is not only constrained by energy needed to fuel computation, but it is also constrained by energy needed to form memories. Experiments have shown that learning simple conditioning tasks already carries a significant metabolic cost. Yet, learning a task like MNIST to 95% accuracy appears to require at least 10^{8} synaptic updates. Therefore the brain has likely evolved to be able to learn using as little e
Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias
While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts -- thus connecting this new framing of bias to language out
Using deep learning to construct stochastic local search SAT solvers with performance bounds
The Boolean Satisfiability problem (SAT), as the prototypical $\mathsf{NP}$-complete problem, is crucial in both theoretical computer science and practical applications. To address this problem, stochastic local search (SLS) algorithms, which iteratively and randomly update candidate assignments, present an important and theoretically well-studied class of solvers. Recent theoretical advancements have identified cond
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
We present Lil-Bevo, our submission to the BabyLM Challenge. We pretrained our masked language models with three ingredients: an initial pretraining with music data, training on shorter sequences before training on longer ones, and masking specific tokens to target some of the BLiMP subtasks. Overall, our baseline models performed above chance, but far below the performance levels of larger LLMs trained on more data.
Towards Adaptive, Learning-Based Security in Decentralized Applications
Web3 systems expose a fundamentally different security landscape from centralized platforms, characterized by composability, pseudonymous identities, decentralized governance, and rapidly evolving attack strategies that span social, application, and protocol layers. Existing security mechanisms, such as static smart contract analysis, blacklist-based phishing detection, and network-level mitigation, operate in isolat
Pretraining language models is still a challenge for many researchers due to its substantial computational costs. As such, there is growing interest in developing more affordable pretraining methods. One notable advancement in this area is the Cramming technique (Geiping and Goldstein, 2022), which enables the pretraining of BERT-style language models using just one GPU in a single day. Building on this innovative ap
High Probability Guarantees for Random Reshuffling
We consider the stochastic gradient method with random reshuffling ($\mathsf{RR}$) for tackling smooth nonconvex optimization problems. $\mathsf{RR}$ finds broad applications in practice, notably in training neural networks. In this work, we provide high probability complexity guarantees for this method. First, we establish a high probability ergodic sample complexity result (without taking expectation) for finding a
This research project presents the implementation of a Deep Q-Learning Network (DQN) for a self-driving car on a 2-dimensional (2D) custom track, with the objective of enhancing the DQN network's performance. It encompasses the development of a custom driving environment using Pygame on a track surrounding the University of Memphis map, as well as the design and implementation of the DQN model. The algorithm util
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
Hand Gesture Recognition (HGR) enables intuitive human-computer interactions in various real-world contexts. However, existing frameworks often struggle to meet the real-time requirements essential for practical HGR applications. This study introduces a robust, skeleton-based framework for dynamic HGR that simplifies the recognition of dynamic hand gestures into a static image classification task, effectively reducin
Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training
Adversarial training has proven effective in improving the robustness of deep neural networks against adversarial attacks. However, this enhanced robustness often comes at the cost of a substantial drop in accuracy on clean data. In this paper, we address this limitation by introducing Tangent Direction Guided Adversarial Training (TART), a novel method that enhances clean accuracy by exploiting the geometry of the d
Edge-preserving noise for diffusion models
Classical diffusion models typically rely on isotropic Gaussian noise, treating all regions uniformly and overlooking structural information important for high-quality generation. We introduce an edge-preserving diffusion process that generalizes isotropic models via a hybrid noise scheme with an edge-aware scheduler that smoothly transitions from edge-preserving to isotropic noise. This enables the model to capture
Respiratory motion complicates accurate irradiation of thoraco-abdominal tumors during radiotherapy, as treatment-system latency entails target-location uncertainties. This work addresses frame forecasting in chest and liver cine MRI to compensate for such delays. We investigate RNNs trained with online learning algorithms, enabling adaptation to changing respiratory patterns via on-the-fly parameter updates, and tra
Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging
Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and non-destructive testing in material science, and ultrasound computed tomography in medicine. Current approaches for solving CWI problems can be divided into two catego
Sampling Transferable Graph Neural Networks with Limited Graph Information
Graph neural networks (GNNs) achieve strong performance on graph learning tasks, but training on large-scale networks remains computationally challenging. Transferability results show that GNNs with fixed weights can generalize from smaller graphs to larger ones drawn from the same family, motivating the use of sampled subgraphs to boost training efficiency. Yet most existing sampling strategies rely on reliable acce
In Context Learning and Reasoning for Symbolic Regression with Large Language Models
Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we explore the potential of LLMs to perform symbolic regression -- a machine-learning method for finding simple and accurate equations from datasets. We prompt GPT-4 and GPT-4o models to suggest expressions from data, which are then optimized and
Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach
Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundamental challenges: (i) non-stationarity caused by the evolving lower-level policy during training, which destabilizes higher-level learning, and (ii) the generation of infeasible subgoals that lower-level policies cannot achieve. To address the
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation
The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-encoders and diffusion models. However, these generative models for molecular design remain constrained by exposure bias, error accumulation, and suboptimal handling of activity cliffs. Here, we introduce DiffGap, a diffusion-based framework that integrates adaptive samplin
Audio-driven portrait animation has made significant advances with diffusion-based models, improving video quality and lipsync accuracy. However, the increasing complexity of these models has led to inefficiencies in training and inference, as well as constraints on video length and inter-frame continuity. In this paper, we propose JoyVASA, a diffusion-based method for generating facial dynamics and head motion in au
Query pipeline optimization for cancer patient question answering systems
Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external information and grounding responses in retrieved knowledge. However, query pipeline optimization for cancer patient question-answering (CPQA) systems requires separately optimizing multiple components with domain-specific considerations. We propose a novel three-aspect op
Notable events recorded on this day and month across all years.