Record 19012026 · captured 2026-08-25
The world looked up Jarrett Stidham. 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.
Jarrett Ryan Stidham is an American professional football quarterback for the Denver Broncos of the National Football League (NFL). He played college football for the Auburn Tigers following a stint with the Baylor Bears. Stidham was selected by the New Englan
The Rip is a 2026 American action thriller film written and directed by Joe Carnahan, who developed the story with Michael McGrale. The film stars Matt Damon and Ben Affleck as police officers in the Miami-Dade Police Department narcotics unit. It also stars S
Bo Chapman Nix is an American professional football quarterback for the Denver Broncos of the National Football League (NFL). He played his first three seasons of college football for the Auburn Tigers, winning SEC Freshman of the Year in 2019. During his last
Drake Lee Maye is an American professional football quarterback for the New England Patriots of the National Football League (NFL). He played college football for the North Carolina Tar Heels, winning the Shaun Alexander Award and ACC Football Player of the Ye
Bigg Boss Kannada 12 is a reality show and the twelfth season of the Indian Kannada-language reality television series Bigg Boss produced by Banijay. The show began on 28 September 2025, on Colors Kannada and JioHotstar, with Kichcha Sudeep as a host for the t
The Africa Cup of Nations, commonly abbreviated as AFCON in English and CAN in French, and currently known as the TotalEnergies Africa Cup of Nations for sponsorship reasons, is the main biennial international men's association football competition in Africa.
Greenland is an autonomous territory of the Kingdom of Denmark and is the largest of the kingdom's three constituent parts by land area, the others being Denmark proper and the Faroe Islands. Citizens of Greenland are citizens of Denmark. They are thus citizen
28 Years Later: The Bone Temple
28 Years Later: The Bone Temple is a 2026 post-apocalyptic horror film directed by Nia DaCosta and written by Alex Garland. It is the direct sequel to 28 Years Later (2025) and the fourth instalment in the 28 Days Later film series. It stars Ralph Fiennes, Jac
Coleridge Bernard "C. J." Stroud IV is an American professional football quarterback for the Houston Texans of the National Football League (NFL). Stroud played college football for the Ohio State Buckeyes, where he holds several school records, including most
Samuel Richard Darnold is an American professional football quarterback for the Seattle Seahawks of the National Football League (NFL). He played college football for the USC Trojans, becoming the first freshman to win the Archie Griffin Award.
Joshua Patrick Allen is an American professional football quarterback for the Buffalo Bills of the National Football League (NFL). He is regarded as one of the greatest dual-threat quarterbacks of all time and is the NFL leader in quarterback rushing touchdown
Bigg Boss (Tamil TV series) season 9
Bigg Boss 9 is the ninth season of the Indian Tamil-language reality television series Bigg Boss, produced by Banijay. Vijay Sethupathi is returning as a host for the second time in a row. The season premiered on 5 October 2025 on Star Vijay and JioHotstar. Un
Agatha Christie's Seven Dials is a British miniseries based on the 1929 novel The Seven Dials Mystery by Agatha Christie. Dramatised by Chris Chibnall and directed by Chris Sweeney, the series stars Mia McKenna-Bruce, Edward Bluemel, Iain Glen, Martin Freeman,
Melanie Lyn McGuire is an American former nurse who was convicted of the murder of her husband. He was murdered on April 28th, 2004, in what media dubbed the "Suitcase Murder". She was sentenced to life in prison, on July 19, 2007 and is serving her sentence a
Kevin Stefanski is an American professional football coach and former player who is the head coach for the Atlanta Falcons of the National Football League (NFL). He previously served as the head coach of the Cleveland Browns from 2020 to 2025. He played colleg
Michael George Vrabel is an American professional football coach and former linebacker who is the head coach for the New England Patriots of the National Football League (NFL). Vrabel previously played in the NFL for 14 seasons, most notably with the Patriots.
Hailee Steinfeld is an American actress and singer. She had her breakthrough with the western film True Grit (2010), which earned her various accolades, including nominations for an Academy Award, a BAFTA Award, a Critics' Choice Movie Award and an Actor Award
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
Brahim Abdelkader Díaz, also known mononymously as Brahim, is a professional footballer who plays as an attacking midfielder or winger for La Liga club Real Madrid. Born in Spain, he plays for the Morocco national team. A versatile playmaker, he is known for h
2026 Brihanmumbai Municipal Corporation election
The 2026 Brihanmumbai Municipal Corporation election took place on 15 January 2026 to elect members to the Brihanmumbai Municipal Corporation of Greater Mumbai, the second largest city in India. Voting lasted one day and the results were released on 16 January
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
His & Hers is an American mystery thriller limited series starring Tessa Thompson, Jon Bernthal, Pablo Schreiber, Marin Ireland, Sunita Mani, Rebecca Rittenhouse, Chris Bauer, Poppy Liu and Crystal Fox. It is an adaptation of the 2020 novel of the same name by
Avatar: Fire and Ash is a 2025 American epic science fiction film directed by James Cameron from a screenplay he co-wrote with Rick Jaffa and Amanda Silver. Produced by Lightstorm Entertainment, it is the third installment in the Avatar film series and the seq
James Cornelison is an American singer who sings "The Star-Spangled Banner" and "O Canada" at the beginning of home games for the Chicago Blackhawks, accompanied by the organist Frank Pellico/Carrie Marcotte. Cornelison, a dramatic tenor, started singing the a
Heated Rivalry is a Canadian sports romance television series created, written, and directed by Jacob Tierney for Crave. Based on the Game Changers book series by Rachel Reid, the show takes its title from the 2019 second installment. It stars Hudson Williams
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
Gregory Kent Bovino is a United States Border Patrol officer who served as the commander-at-large of the Border Patrol from October 2025 to January 2026.
"XXX" is a song by American rapper Kendrick Lamar, from his fourth studio album Damn, released on April 14, 2017. The eleventh track on the album, the song was written by Lamar, Mike Will Made It, DJ Dahi, Mark Spears a.k.a. Sounwave, Anthony Tiffith, Bono, th
Michael Macdonald is an American professional football coach who is the head coach for the Seattle Seahawks of the National Football League (NFL). He began his career with the Baltimore Ravens in 2014, serving as a defensive assistant. In 2021, Macdonald left
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Robustness is a fundamental pillar of Machine Learning (ML) classifiers, substantially determining their reliability. Methods for assessing classifier robustness are therefore essential. In this work, we address the challenge of evaluating corruption robustness in a way that allows comparability and interpretability on a given dataset. We propose a test data augmentation method that uses a robustness distance $ε$ der
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
Offline reinforcement learning (RL) provides a promising solution to learning an agent fully relying on a data-driven paradigm. However, constrained by the limited quality of the offline dataset, its performance is often sub-optimal. Therefore, it is desired to further finetune the agent via extra online interactions before deployment. Unfortunately, offline-to-online RL can be challenging due to two main challenges:
Feature Propagation on Knowledge Graphs using Cellular Sheaves
Many inference tasks on knowledge graphs, including relation prediction, operate on knowledge graph embeddings -- vector representations of the vertices (entities) and edges (relations) that preserve task-relevant structure encoded within the underlying combinatorial object. Such knowledge graph embeddings can be modeled as an approximate global section of a cellular sheaf, an algebraic structure over the graph. Usin
ecoBLE: A Low-Computation Energy Consumption Prediction Framework for Bluetooth Low Energy
Bluetooth Low Energy (BLE) is a de-facto technology for Internet of Things (IoT) applications, promising very low energy consumption. However, this low energy consumption accounts only for the radio part, and it overlooks the energy consumption of other hardware and software components. Monitoring and predicting the energy consumption of IoT nodes after deployment can substantially aid in ensuring low energy consumpt
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding
Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct
In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed to address visual grounding in AVs.Our Context-Aware Visual Grounding (CAVG) model is an advanced system that integrates five core encoders-Text, Image, Context, and Cross
This paper develops a framework to predict toxic trades that a broker receives from her clients. Toxic trades are predicted with a novel online learning Bayesian method which we call the projection-based unification of last-layer and subspace estimation (PULSE). PULSE is a fast and statistically-efficient Bayesian procedure for online training of neural networks. We employ a proprietary dataset of foreign exchange tr
Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks
We analyze the frequency spectrum of Quantum Neural Networks (QNNs) using Minkowski sums, which yields a compact algebraic description and permits explicit computation. Using this description, we prove several maximality results for broad classes of QNN architectures. Under some mild technical conditions we establish a bijection between classes of models with the same area $A:=R\cdot L$ that preserves the frequency s
High-Dimensional Tail Index Regression
Motivated by the empirical observation of power-law distributions in the credits (e.g., ``likes'') of viral posts in social media, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we propose a regularized estimator, establish its consistency, and derive its convergence rate. Second, we debias the regularized estimator to fac
Apriori Knowledge in an Era of Computational Opacity: The Role of AI in Mathematical Discovery
Can we acquire apriori knowledge of mathematical facts from the outputs of computer programs? People like Burge have argued (correctly in our opinion) that, for example, Appel and Haken acquired apriori knowledge of the Four Color Theorem from their computer program insofar as their program simply automated human forms of mathematical reasoning. However, unlike such programs, we argue that the opacity of modern LLMs
LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this paper, we address these challenges by proposin
Towards Explainable Traffic Flow Prediction with Large Language Models
Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results. Achieving both accuracy and explainability in traffic pre
Value Improved Actor Critic Algorithms
To learn approximately optimal acting policies for decision problems, modern Actor Critic algorithms rely on deep Neural Networks (DNNs) to parameterize the acting policy and greedification operators to iteratively improve it. The reliance on DNNs suggests an improvement that is gradient based, which is per step much less greedy than the improvement possible by greedier operators such as the greedy update used by Q-l
Complex conjugate matrix equations (CCME) are important in computation and antilinear systems. Existing research mainly focuses on the time-invariant version, while studies on the time-variant version and its solution using artificial neural networks are still lacking. This paper introduces zeroing neural dynamics (ZND) to solve the earliest time-variant CCME. Firstly, the vectorization and Kronecker product in the c
Kolmogorov-Arnold networks (KANs) represent data features by learning the activation functions and demonstrate superior accuracy with fewer parameters, FLOPs, GPU memory usage (Memory), shorter training time (TraT), and testing time (TesT) when handling low-dimensional data. However, when applied to high-dimensional data, which contains significant redundant information, the current activation mechanism of KANs leads
Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
Graph anomaly detection (GAD) is widely applied in many areas, such as financial fraud detection and social spammer detection. Anomalous nodes in the graph not only impact their own communities but also create a ripple effect on neighbors throughout the graph structure. Detecting anomalous nodes in complex graphs has been a challenging task. While existing GAD methods assume all labels are correct, real-world scenari
HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction
Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced signi
In the field of Image-Text Retrieval (ITR), recent advancements have leveraged large-scale Vision-Language Pretraining (VLP) for Fine-Grained (FG) instance-level retrieval, achieving high accuracy at the cost of increased computational complexity. For Coarse-Grained (CG) category-level retrieval, prominent approaches employ Cross-Modal Hashing (CMH) to prioritise efficiency, albeit at the cost of retrieval performanc
Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency compared to model-free RL by utilizing a virtual environment model. However, it is challenging to obtain sufficiently accurate representations of the environmental dynamics due to uncertainties in complex systems and environments. An inaccurate environment model may degrade the sample efficiency and performance of model-based R
Automating Traffic Model Enhancement with AI Research Agent
Developing efficient traffic models is crucial for optimizing modern transportation systems. However, current modeling approaches remain labor-intensive and prone to human errors due to their dependence on manual workflows. These processes typically involve extensive literature reviews, formula tuning, and iterative testing, which often lead to inefficiencies. To address this, we propose TR-Agent, an AI-powered frame
Better Language Models Exhibit Higher Visual Alignment
How well do text-only large language models (LLMs) align with the visual world? We present a systematic evaluation of this question by incorporating frozen representations of various language models into a discriminative vision-language framework and measuring zero-shot generalization to novel concepts. We find that decoder-based models exhibit stronger visual alignment than encoders, even when controlling for model
Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior SDRTV-to-HDRTV Conversion
The rise of HDR-WCG display devices has highlighted the need to convert SDRTV to HDRTV, as most video sources are still in SDR. Existing methods primarily focus on designing neural networks to learn a single-style mapping from SDRTV to HDRTV. However, the limited information in SDRTV and the diversity of styles in real-world conversions render this process an ill-posed problem, thereby constraining the performance an
V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception
Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged; however, these datasets primarily focus on came
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions, they frequently overlook detailed car-following behaviors
FOF-X: Towards Real-time Detailed Human Reconstruction from a Single Image
We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality results is a persistent challenge, mainly due to the high computational demands of existing 3D representations. To address this, we propose Fourier Occupancy Field (FOF), an efficient 3D representation by learning the Fourier series. The core of FOF is to factorize a 3D occupa
Notable events recorded on this day and month across all years.