Record 13012026 · captured 2026-08-25
The world looked up Donald Trump. 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.
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.
The 83rd Golden Globes was an awards ceremony that honored excellence in film and American television productions of 2025. The winners were revealed during the live telecast, airing on CBS and streaming on Paramount+ on January 11, 2026, at the Beverly Hilton.
Hamnet is a 2025 historical drama film directed by Chloé Zhao, who co-wrote the screenplay with Maggie O'Farrell, based on the 2020 novel by O'Farrell. The film dramatises the family life of William Shakespeare and his wife Agnes Hathaway as they cope with the
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
Álvaro Arbeloa Coca is a Spanish professional football manager and former footballer who is the head coach of Premier League club Fulham. He predominantly played as a right-back, and occasionally on the left side.
Jessie Buckley is an Irish actress and singer. Her accolades include an Academy Award, two BAFTAs, an Actor Award, a Golden Globe Award, a Critics' Choice Award and a Laurence Olivier Award.
Teyana Me Shay Jacqueline Taylor is an American singer, songwriter, actress, dancer, choreographer, and music video director. Her accolades include a Golden Globe Award, two Critics Choice Awards, and an NAACP Image Award, along with nominations for an Academy
Mary Rose Byrne is an Australian actress. Renowned for her versatility across screen and stage, she is particularly recognised for her leading roles in blockbuster comedies, independent dramas, and horror films. Her accolades include a Golden Globe Award, Nati
Timothée Hal Chalamet is an American and French actor. Known for his work in a diverse range of blockbusters and independent films, he is the recipient of numerous accolades including an Actor Award, a Golden Globe Award, and two Critics' Choice Awards, in add
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
Nicole Rene Glaser is an American stand-up comedian and actress. She has had five television stand-up specials, hosted numerous award shows, and performed at numerous televised roasts, gaining significant popularity for her set on The Roast of Tom Brady. Previ
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
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
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
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
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
Paul Thomas Anderson, also known by his initials PTA, is an American filmmaker. Often described as one of the preeminent filmmakers of his generation, he is the recipient of numerous accolades, including three Academy Awards, three Golden Globe Awards and four
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 Golden Globes are American awards presented for excellence in international film and television. It is an annual award with an award ceremony held since 1944 to honor artists, professionals, and their work. The ceremony is normally held every January, and
Robert Hall Weir was an American musician and songwriter best known as a founding member of the Grateful Dead. After the group disbanded in 1995, he performed with the Other Ones, later known as the Dead, together with other former members of the Grateful Dead
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
Stellan John Skarsgård is a Swedish actor. He is known for his collaborations with director Lars von Trier, appearing in Breaking the Waves (1996), Dancer in the Dark (2000), Dogville (2003), Melancholia (2011), and Nymphomaniac (2013). Skarsgård's early Engli
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
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
The president of Venezuela, officially known as the president of the Bolivarian Republic of Venezuela, is the executive head of state and head of government of Venezuela. The president leads the National Executive of the Venezuelan government and is the comman
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
Owen Patrick Cooper is an English actor. He is best known for his debut role as a teenage murder suspect in the Netflix miniseries Adolescence (2025). For his performance, he became the youngest male actor to win an Actor Award and Primetime Emmy Award, as wel
Noah Strausser Speer Wyle is an American actor, television director, producer and writer. He is best known for portraying Dr. John Carter in the NBC medical drama ER (1994–2005) and Dr. Michael "Robby" Robinavitch in the HBO Max medical drama The Pitt (2025–pr
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
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Effective Blind Source Separation Based on the Adam Algorithm
In this paper, we derive a modified InfoMax algorithm for the solution of Blind Signal Separation (BSS) problems by using advanced stochastic methods. The proposed approach is based on a novel stochastic optimization approach known as the Adaptive Moment Estimation (Adam) algorithm. The proposed BSS solution can benefit from the excellent properties of the Adam approach. In order to derive the new learning rule, the
Survey on Publicly Available Sinhala Natural Language Processing Tools and Research
Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks
In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest. The assessment of neural networks by means of uncertainties is a common ansatz to prevent safety issues. As in applications like automated driving, video streams of images are available, we present a time-dynamic approach to investigating uncertainties and assessing the prediction quality of neur
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
Instance segmentation with neural networks is an essential task in environment perception. In many works, it has been observed that neural networks can predict false positive instances with high confidence values and true positives with low ones. Thus, it is important to accurately model the uncertainties of neural networks in order to prevent safety issues and foster interpretability. In applications such as automat
Accumulation of Sub-Sampling Matrices with Applications to Statistical Computation
With appropriately chosen sampling probabilities, sampling-based random projection can be used to implement large-scale statistical methods, substantially reducing computational cost while maintaining low statistical error. However, computing optimal sampling probabilities is often itself expensive, and in practice one typically resorts to suboptimal schemes. This generally leads to increased time and space costs, as
False Negative Reduction in Video Instance Segmentation using Uncertainty Estimates
Instance segmentation of images is an important tool for automated scene understanding. Neural networks are usually trained to optimize their overall performance in terms of accuracy. Meanwhile, in applications such as automated driving, an overlooked pedestrian seems more harmful than a falsely detected one. In this work, we present a false negative detection method for image sequences based on inconsistencies in ti
Approximating Persistent Homology for Large Datasets
Persistent homology is an important methodology in topological data analysis which adapts theory from algebraic topology to data settings. Computing persistent homology produces persistence diagrams, which have been successfully used in diverse domains. Despite its widespread use, persistent homology is simply impossible to compute when a dataset is very large. We study a statistical approach to the problem of comput
False Negative Reduction in Semantic Segmentation under Domain Shift using Depth Estimation
State-of-the-art deep neural networks demonstrate outstanding performance in semantic segmentation. However, their performance is tied to the domain represented by the training data. Open world scenarios cause inaccurate predictions which is hazardous in safety relevant applications like automated driving. In this work, we enhance semantic segmentation predictions using monocular depth estimation to improve segmentat
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD objects are understood as objects with a semantic class outside the semantic space of an underlying image segmentation algorithm, or an instance within the semantic space which however looks decisively different from the instances contained in the training data. OOD object
Visual Adversarial Attacks and Defenses in the Physical World: A Survey
Although Deep Neural Networks (DNNs) have been widely applied in various real-world scenarios, they remain vulnerable to adversarial examples. Adversarial attacks in computer vision can be categorized into digital attacks and physical attacks based on their different forms. Compared to digital attacks, which generate perturbations in digital pixels, physical attacks are more practical in real-world settings. Due to t
Generative Modeling via Hierarchical Tensor Sketching
We propose a hierarchical tensor-network approach for approximating high-dimensional probability density via empirical distribution. This leverages randomized singular value decomposition (SVD) techniques and involves solving linear equations for tensor cores in this tensor network. The complexity of the resulting algorithm scales linearly in the dimension of the high-dimensional density. An analysis of estimation er
Versatile audio-visual learning for emotion recognition
Most current audio-visual emotion recognition models lack the flexibility needed for deployment in practical applications. We envision a multimodal system that works even when only one modality is available and can be implemented interchangeably for either predicting emotional attributes or recognizing categorical emotions. Achieving such flexibility in a multimodal emotion recognition system is difficult due to the
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition
Intrinsic Image Decomposition (IID) is a challenging inverse problem that seeks to decompose a natural image into its underlying intrinsic components such as albedo and shading. While recent image decomposition methods rely on learning-based priors on these components, they often suffer from component cross-contamination owing to joint training of priors; or from Sim-to-Real gap since the priors trained on synthetic
The Interpolating Information Criterion for Overparameterized Models
The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical information criteria typically consider the large-data limit, penalizing model size. However, these criteria are not appropriate in modern settings where overparameterized models tend to perform well. For any overparameterized model, we show that the
Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra
The rich biodiversity of coral reefs in Indonesian waters represents a valuable asset that must be preserved. Rapid climate change and uncontrolled human activities have caused significant degradation of coral reef ecosystems, including coral bleaching, which is a critical indicator of declining reef health. Therefore, this study aims to develop an accurate classification model to distinguish between healthy corals a
A Convex Framework for Confounding Robust Inference
We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding over a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this pa
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation
State-of-the-art deep neural networks have been shown to be extremely powerful in a variety of perceptual tasks like semantic segmentation. However, these networks are vulnerable to adversarial perturbations of the input which are imperceptible for humans but lead to incorrect predictions. Treating image segmentation as a sum of pixel-wise classifications, adversarial attacks developed for classification models were
This paper proposes an efficient attempt to noisy speech emotion recognition (NSER). Conventional NSER approaches have proven effective in mitigating the impact of artificial noise sources, such as white Gaussian noise, but are limited to non-stationary noises in real-world environments due to their complexity and uncertainty. To overcome this limitation, we introduce a new method for NSER by adopting the automatic s
Efficient Continual Pre-training for Building Domain Specific Large Language Models
Large language models (LLMs) have demonstrated remarkable open-domain capabilities. LLMs tailored for a domain are typically trained entirely on domain corpus to excel at handling domain-specific tasks. In this work, we explore an alternative strategy of continual pre-training as a means to develop domain-specific LLMs over an existing open-domain LLM. We introduce FinPythia-6.9B, developed through domain-adaptive co
A Concentration Bound for TD(0) with Function Approximation
We derive uniform all-time concentration bound of the type 'for all $n \geq n_0$ for some $n_0$' for TD(0) with linear function approximation. We work with online TD learning with samples from a single sample path of the underlying Markov chain. This makes our analysis significantly different from offline TD learning or TD learning with access to independent samples from the stationary distribution of the Mar
Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets. By preserving data privacy, federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation, research and dev
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving
A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map learning primarily focuses on either detecting geometry-based lanelines or perceiving topology relationships of centerlines. Both of these methods ignore the intrinsic relationship of lanelines and centerlines, that lanelines bind centerline
TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation
The recent advances in natural language processing have predominantly favored well-resourced English-centric models, resulting in a significant gap with low-resource languages. In this work, we introduce the language model TURNA, which is developed for the low-resource language Turkish and is capable of both natural language understanding and generation tasks. TURNA is pretrained with an encoder-decoder architecture
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
This study investigates leveraging stochastic gradient descent (SGD) to learn operators between general Hilbert spaces. We propose weak and strong regularity conditions for the target operator to depict its intrinsic structure and complexity. Under these conditions, we establish upper bounds for convergence rates of the SGD algorithm and conduct a minimax lower bound analysis, further illustrating that our convergenc
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension $d$ and the number of data points $n$ diverge with a fixed ratio $α= n / d$. We introduce a tractable mathematical model where the interplay between the data and adversarial attacker geometries can be studied, while capturing the core phenomenology observed in the adversaria
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