Record 15012026 · captured 2026-08-25
The world looked up Scott Adams. 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.
Scott Raymond Adams was an American cartoonist, author, and commentator. He is best known as the creator of the Dilbert comic strip and nonfiction works of business, self-improvement, commentary, and satire.
Timothy Busfield is an American actor and director. He played Arnold Poindexter in the first two Revenge of the Nerds films, Elliot Weston on the television series Thirtysomething, Mark in Field of Dreams, and Danny Concannon on the television series The West
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.
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
Michael Pettaway Tomlin is an American former professional football coach. He is known for having served as the head coach for the Pittsburgh Steelers of the National Football League (NFL) from 2007 to 2025. During his 19 seasons as head coach, Tomlin never fi
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
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
The 2025–2026 Iranian protests were a series of nationwide demonstrations against the government of Iran that began on 28 December 2025 amid a deepening economic crisis. The unrest followed a sharp depreciation of the Iranian rial, rising inflation, and widesp
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
Melissa Ellen Gilbert is an American actress. Gilbert began her career as a child actress in the late 1960s, appearing in numerous commercials and guest-starring roles on television. From 1974 to 1983, she starred as Laura Ingalls Wilder, the second-oldest dau
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
On January 7, 2026, Renée Nicole Macklin Good, a 37-year-old American woman, was fatally shot by United States Immigration and Customs Enforcement (ICE) agent Jonathan Ross in Minneapolis, Minnesota, during Operation Metro Surge. Good was in her car stopped si
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
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
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
Makar(a) Sankrānti also referred to as Uttarāyana, Makara, or simply Sankrānti, is a Hindu observance and a mid-winter harvest festival in India and Nepal. The Sun God (Surya) is worshipped in Makar Sankranti. It is typically celebrated on 14 January annually.
The RajaSaab is a 2026 Indian Telugu-language fantasy horror comedy film written and directed by Maruthi, and produced by People Media Factory and IVY Entertainment. The film stars Prabhas, alongside Sanjay Dutt, Nidhhi Agerwal, Malavika Mohanan, Riddhi Kumar
Connor Storrie is an American actor. He is best known for his breakout role as Ilya Rozanov in the sports romance series Heated Rivalry (2025–present). He hosted an episode of Saturday Night Live in 2026, for which he received a Primetime Emmy Award nomination
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
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
Hudson Williams is a Canadian actor. He rose to prominence for his breakout role as Shane Hollander in the Crave original television series, Heated Rivalry (2025–present), for which he won the Canadian Screen Award for Best Leading Performance in a Drama Serie
Venezuela, officially the Bolivarian Republic of Venezuela, is a country on the northern coast of South America, consisting of a continental landmass and various islands and islets in the Caribbean Sea. It comprises an area of 912,050 km2 (352,140 sq mi), with
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
Iran, officially the Islamic Republic of Iran, and historically known as Persia, is a country in West Asia. It borders Iraq to the west, Turkey, Azerbaijan, and Armenia to the northwest, the Caspian Sea to the north, Turkmenistan to the northeast, Afghanistan
Marty Supreme is a 2025 American sports comedy-drama film directed by Josh Safdie, who co-wrote it with Ronald Bronstein. Set in the 1950s, it stars Timothée Chalamet as table tennis player Marty Mauser and follows his quest to become world champion. Gwyneth P
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Stephen J. Bisciotti is an American business executive and the current majority owner of the Baltimore Ravens of the National Football League (NFL). He founded Aerotek, the largest privately owned staffing and recruiting company in the U.S. based in Hanover, M
Liam James Rosenior is an English professional football manager and former player who is the head coach of Ligue 1 club Paris FC.
1000 Blank White Cards is a party card game played with cards in which the deck is created as part of the game. Though it has been played by adults in organized groups worldwide, 1000 Blank White Cards is also described as well-suited for children in Hoyle's R
John Edward Forté was an American rapper and record producer. He is best known for being a member of the musical collective Refugee Camp All-Stars. He also worked as a producer on The Score (1996), which received critical acclaim and earned Forté a Grammy Awar
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior
An open problem in autonomous driving research is modeling human driving behavior, which is needed for the planning component of the autonomy stack, safety validation through traffic simulation, and causal inference for generating explanations for autonomous driving. Modeling human driving behavior is challenging because it is stochastic, high-dimensional, and involves interaction between multiple agents. This proble
Reinforcement Learning with Exogenous States and Rewards
Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal. This paper formalizes exogenous state variables and rewards and shows that if the reward function decomposes additively into endogenous and exogenous components, the MDP can be decomposed into an exogenous Markov Reward Process (based on the exogenous reward) and an endogenous Markov Decis
Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction
In personalized machine learning, the aim of personalization is to train a model that caters to a specific individual or group of individuals by optimizing one or more performance metrics and adhering to specific constraints. In this paper, we discuss the need for personalization in affective computing and present the first survey of existing approaches for personalization in affective computing. Our review spans tra
LaCon: Late-Constraint Diffusion for Steerable Guided Image Synthesis
Diffusion models have demonstrated impressive abilities in generating photo-realistic and creative images. To offer more controllability for the generation process, existing studies, termed as early-constraint methods in this paper, leverage extra conditions and incorporate them into pre-trained diffusion models. Particularly, some of them adopt condition-specific modules to handle conditions separately, where they s
Positional Embedding-Aware Activations
We present a neural network architecture designed to naturally learn a positional embedding and overcome the spectral bias towards lower frequencies faced by conventional activation functions. Our proposed architecture, SPDER, is a simple MLP that uses an activation function composed of a sinusoidal multiplied by a sublinear function, called the damping function. The sinusoidal enables the network to automatically le
Kernel Limit for a Class of Recurrent Neural Networks Trained on Ergodic Data Sequences
Mathematical methods are developed to characterize the asymptotics of recurrent neural networks (RNN) as the number of hidden units, data samples in the sequence, hidden state updates, and training steps simultaneously grow to infinity. In the case of an RNN with a simplified weight matrix, we prove the convergence of the RNN to the solution of an infinite-dimensional ODE coupled with the fixed point of a random alge
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the recent wave of transformative AI. Nevertheless, such advance comes with an intensifying concern about the misuse of this generative technology, especially for producing copyrighted or NSFW (i.e. not safe for work) images. Although efforts ha
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
This work aims to adapt large-scale pre-trained vision-language models, such as contrastive language-image pretraining (CLIP), to enhance the performance of object reidentification (Re-ID) across various supervision settings. Although prompt learning has enabled a recent work named CLIP-ReID to achieve promising performance, the underlying mechanisms and the necessity of prompt learning remain unclear due to the abse
Soft Contrastive Learning for Time Series
Contrastive learning has shown to be effective to learn representations from time series in a self-supervised way. However, contrasting similar time series instances or values from adjacent timestamps within a time series leads to ignore their inherent correlations, which results in deteriorating the quality of learned representations. To address this issue, we propose SoftCLT, a simple yet effective soft contrastive
Lens: A Knowledge-Guided Foundation Model for Network Traffic
Network traffic refers to the amount of data being sent and received over the Internet or any system that connects computers. Analyzing network traffic is vital for security and management, yet remains challenging due to the heterogeneity of plain-text packet headers and encrypted payloads. To capture the latent semantics of traffic, recent studies have adopted Transformer-based pretraining techniques to learn networ
Template-Based Probes Are Imperfect Lenses for Counterfactual Bias Evaluation in LLMs
Bias in large language models (LLMs) has many forms, from overt discrimination to implicit stereotypes. Counterfactual bias evaluation is a widely used approach to quantifying bias and often relies on template-based probes that explicitly state group membership. It aims to measure whether the outcome of a task performed by an LLM is invariant to a change in group membership. In this work, we find that template-based
Human-in-the-Loop Segmentation of Multi-species Coral Imagery
Marine surveys by robotic underwater and surface vehicles result in substantial quantities of coral reef imagery, however labeling these images is expensive and time-consuming for domain experts. Point label propagation is a technique that uses existing images labeled with sparse points to create augmented ground truth data, which can be used to train a semantic segmentation model. In this work, we show that recent a
Swap distance minimization beyond entropy minimization in word order variation
Consider a linguistic structure formed by $n$ elements, for instance, subject, direct object and verb ($n=3$) or subject, direct object, indirect object and verb ($n=4$). We investigate whether the frequency of the $n!$ possible orders is constrained by two principles. First, entropy minimization, a principle that has been suggested to shape natural communication systems at distinct levels of organization. Second, sw
Objective: Clinical trials are essential for advancing pharmaceutical interventions, but they face a bottleneck in selecting eligible participants. Although leveraging electronic health records (EHR) for recruitment has gained popularity, the complex nature of unstructured medical texts presents challenges in efficiently identifying participants. Natural Language Processing (NLP) techniques have emerged as a solution
This paper presents Global Benchmark Database (GBD), a comprehensive suite of tools for provisioning and sustainably maintaining benchmark instances and their metadata. The availability of benchmark metadata is essential for many tasks in empirical research, e.g., for the data-driven compilation of benchmarks, the domain-specific analysis of runtime experiments, or the instance-specific selection of solvers. In this
In recent years considerable research in LiDAR semantic segmentation was conducted, introducing several new state of the art models. However, most research focuses on single-scan point clouds, limiting performance especially in long distance outdoor scenarios, by omitting time-sequential information. Moreover, varying-density and occlusions constitute significant challenges in single-scan approaches. In this paper we
Boosting Adversarial Transferability with Low-Cost Optimization via Maximin Expected Flatness
Transfer-based attacks craft adversarial examples on white-box surrogate models and directly deploy them against black-box target models, offering model-agnostic and query-free threat scenarios. While flatness-enhanced methods have recently emerged to improve transferability by enhancing the loss surface flatness of adversarial examples, their divergent flatness definitions and heuristic attack designs suffer from un
A Low Rank Neural Representation of Entropy Solutions
We construct a new representation of entropy solutions to nonlinear scalar conservation laws with a smooth convex flux function in a single spatial dimension. The representation is a generalization of the method of characteristics and posseses a compositional form. While it is a nonlinear representation, the embedded dynamics of the solution in the time variable is linear. This representation is then discretized as a
Temporal Knowledge Graph Question Answering: A Survey
Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a growing interest in Temporal Knowledge Graph Question Answering (TKGQA), an emerging task to answer temporal questions. However, this field grapples with ambiguities in defining temporal questions and lacks a systematic categorization of ex
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. Identifying mild syndrome with small pathological regions serves as an ominous warning and is fundamental in the early diagnosis of diseases. While deep learning algorithms, particularly convolutional neural networks (CNNs), have shown promise in segmenting medica
Large Language Models (LLMs) have emerged as a new information channel. Meanwhile, one critical but under-explored question is: Is it possible to bypass the safety alignment and inject harmful information into LLMs stealthily? In this paper, we propose to reformulate knowledge editing as a new type of safety threat for LLMs, namely Editing Attack, and conduct a systematic investigation with a newly constructed datase
Stock Price Responses to Firm-Level News in Supply Chain Networks
This study examines how positive and negative news about firms is associated with stock prices and whether these associations extend to firms' suppliers and clients connected through supply chain relationships, using large samples of publicly listed firms worldwide and in Japan. News sentiment is measured using FinBERT, a natural language processing model fine-tuned for financial texts, and supply chain links are
Differentially Private Bilevel Optimization
We present differentially private (DP) algorithms for bilevel optimization, a problem class that received significant attention lately in various machine learning applications. These are the first algorithms for such problems under standard DP constraints, and are also the first to avoid Hessian computations which are prohibitive in large-scale settings. Under the well-studied setting in which the upper-level is not
Naturally situated conversations encapsulate the social norms inherent to their context, reflecting both the relationships between interlocutors and the underlying communicative intent. In this paper, we propose a novel, multi-step framework for generating dialogues that automatically uncovers social norms from rich, context-laden interactions through a process of self-assessment and norm discovery, rather than relyi
FastLRNR and Sparse Physics Informed Backpropagation
We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR). The approach exploits the low rank structure within LRNR and constructs a reduced neural network approximation that is much smaller in size. We call the smaller network FastLRNR. We show that backpropagation
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