Record 01012026 · captured 2026-08-25
The world looked up Tatiana Schlossberg. 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.
Tatiana Celia Kennedy Schlossberg was an American environmental journalist and author. She worked as a science and climate reporter for The New York Times and wrote for several other publications, including The Atlantic, The Washington Post, Vanity Fair, and B
1989 Tiananmen Square protests and massacre
Protests led by students and workers, known in China as the June Fourth Incident, were held in Tiananmen Square in Beijing, China, from 15 April to 4 June 1989. After weeks of unsuccessful attempts between the demonstrators and the Chinese government to find a
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
New Year's Eve in the Gregorian calendar refers to the evening—or commonly the entire day—of the last day of the year: 31 December. In many countries, New Year's Eve is celebrated with dancing, eating, drinking, and watching or lighting fireworks. Many Christi
Caroline Bouvier Kennedy is an American author, diplomat, and attorney. She served as the United States ambassador to Japan from 2013 to 2017 and ambassador to Australia from 2022 to 2024. Most of Kennedy's professional life has been in literature, law, politi
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
Isiah Whitlock Jr. was an American actor. He is best known for his role as corrupt state senator Clay Davis on the HBO television series The Wire, and for his frequent collaborations with filmmaker Spike Lee.
The fifth and final season of the American science fiction horror drama television series Stranger Things, marketed as Stranger Things 5, was released on the streaming service Netflix in two volumes and the finale. The first set of four episodes was released o
Stranger Things is an American television series created by the Duffer Brothers for Netflix. Produced by Monkey Massacre Productions and 21 Laps Entertainment, the first season was released on Netflix on July 15, 2016. The second and third seasons followed in
This is a list of lists of deaths of significant people, organized by year. New deaths articles are added to their respective month and then linked below.
Brigitte Anne-Marie Bardot, often referred to by her initials B.B., was a French actress, singer, model, and animal rights activist. She became one of the best-known symbols of the sexual revolution and gained international fame for portraying characters assoc
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
Rose Kennedy Schlossberg is an American artist and filmmaker specializing in video installations. She created the apocalyptic installation art and web series End Times Girls Club, co-produced and co-wrote the Peabody Award-winning documentary series Time: The
John Bouvier Kennedy Schlossberg is an American social media personality, political commentator and writer. He is a member of the Kennedy family and the Bouvier family.
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
Hull maintenance technician is a United States Navy occupational rating.
The 2025 Africa Cup of Nations was the 35th edition of the Africa Cup of Nations, a biennial football tournament contested by the men's national teams of the Confederation of African Football (CAF). It was hosted by Morocco — the second time the country had ho
Edwin Arthur Schlossberg is an American designer, artist, and author. A pioneer and leader of interactive museum installations, he is the founder and principal designer of ESI Design, a multidisciplinary firm specializing in interactive environments for discov
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
Ronan Patrick John Keating is an Irish singer, songwriter and media personality. He debuted in 1993 alongside Keith Duffy, Michael Graham, Shane Lynch, and Stephen Gately, as the co-lead singer of pop group Boyzone. His solo career started in 1999 and he has r
Archibald Charles Manning is an American college football quarterback for the Texas Longhorns. He is a member of the Manning family of football players.
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
Tarrare, sometimes spelt Tarar, was a French showman, soldier, and spy noted for his unusual appetite and eating habits. Able to eat vast amounts of meat, he was constantly hungry. His parents could not provide for him and he was turned out of the family home
Pluribus is an American post-apocalyptic science fiction television series created by Vince Gilligan for Apple TV. Set and filmed primarily in Albuquerque, New Mexico, the series follows novelist Carol Sturka, who finds herself isolated after an alien virus tr
Millie Bonnie Bongiovi, known professionally as Millie Bobby Brown, is a British actress and film producer. She gained international recognition for playing Eleven in the Netflix science fiction series Stranger Things (2016–2025), for which she received nomina
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
List of Stranger Things episodes
Stranger Things is an American science fiction, horror, mystery, and drama television series created by the Duffer Brothers for the streaming service Netflix. The brothers, as well as Karl Gajdusek in the first season only, are the program's showrunners. They
Stefon Marsean Diggs is an American professional football wide receiver for the Washington Commanders of the National Football League (NFL). Diggs played college football for the Maryland Terrapins and was selected by the Minnesota Vikings in the fifth round o
Wake Up Dead Man is a 2025 American mystery film written and directed by Rian Johnson. It is the third film in the Knives Out series. The film stars Daniel Craig, who reprises his role as master detective Benoit Blanc as he investigates the death of a Catholic
The Housemaid is a 2025 American erotic psychological thriller film directed by Paul Feig and written by Rebecca Sonnenshine. It is based on the 2022 novel by Freida McFadden, and stars Sydney Sweeney and Amanda Seyfried. In the film, Millie Calloway, a young
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions
A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input. Our goal is to identify the optimal approximation-smoothness tradeoffs for different measures of approximation and smoothness. This leads to novel soft-max functions, each of which is optimal for a differ
Efficient Active Learning with Abstention
The goal of active learning is to achieve the same accuracy achievable by passive learning, while using much fewer labels. Exponential savings in terms of label complexity have been proved in very special cases, but fundamental lower bounds show that such improvements are impossible in general. This suggests a need to explore alternative goals for active learning. Learning with abstention is one such alternative. In
Effective Online Exam Proctoring by Combining Lightweight Face Detection and Deep Recognition
Online exams conducted via video conferencing platforms such as Zoom have become widespread, yet ensuring exam integrity remains challenging due to the difficulty of monitoring multiple video feeds in real time. We present iExam, an online exam proctoring and analysis system that combines lightweight real-time face detection with deep face recognition for postexam analysis. iExam assists invigilators by monitoring st
Active Learning with Neural Networks: Insights from Nonparametric Statistics
Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning have been recently reported in the literature, however, rigorous label complexity guarantees of deep active learning have remained elusive. This constitu
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
We propose $\textsf{ScaledGD($λ$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is possibly ill-conditioned. Using overparametrized factor representations, $\textsf{ScaledGD($λ$)}$ starts from a small random initialization, and proceeds by gradient descent with a specific form of damped preconditioning to combat bad curvatu
CascadeNS: Confidence-Cascaded Neurosymbolic Model for Sarcasm Detection
Sarcasm detection in product reviews requires balancing domain-specific symbolic pattern recognition with deep semantic understanding. Symbolic representations capture explicit linguistic phenomena that are often decisive for sarcasm detection. Existing work either favors interpretable symbolic representation or semantic neural modeling, but rarely achieves both effectively. Prior hybrid methods typically combine the
Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU
This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on prior knowledge. The study performed experiments using GPU acceleration, resulting in over 500 times faster compared to the CPU-b
HiGen: Hierarchical Graph Generative Networks
Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. To address this limitation, we propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion. At each level of hierarchy, this model generates communities in parallel, followed by the predicti
HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising
Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only restore one clean HSI from the given noisy HSI with a deterministic mapping, thus ignoring the ill-posed issue and always resulting in an over-smoothing problem. Additionally, these DL-based methods often neglect that noise is part of the h
Generative Modelling of Lévy Area for High Order SDE Simulation
It is well understood that, when numerically simulating SDEs with general noise, achieving a strong convergence rate better than $O(\sqrt{h})$ (where h is the step size) requires the use of certain iterated integrals of Brownian motion, commonly referred to as its "Lévy areas". However, these stochastic integrals are difficult to simulate due to their non-Gaussian nature and for a $d$-dimensional Brownian mot
Content-based Recommendation Engine for Video Streaming Platform
Recommendation engines suggest content, products, or services to the user by using machine learning algorithms. This paper proposes a content-based recommendation engine that provides personalized video suggestions based on users' previous interactions and preferences. The engine uses TF-IDF (Term Frequency-Inverse Document Frequency) text vectorization technique to evaluate the relevance of words in video descri
Debiasing is a fundamental concept in high-dimensional statistics. While degrees-of-freedom adjustment is the state-of-the-art technique in high-dimensional linear regression, it is limited to i.i.d. samples and sub-Gaussian covariates. These constraints hinder its broader practical use. Here, we introduce Spectrum-Aware Debiasing--a novel method for high-dimensional regression. Our approach applies to problems with
Multi-fidelity Bayesian Optimization: A Review
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increas
Are Ensembles Getting Better all the Time?
Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on the kind of ensemble considered, as well as the predictive metric chosen. We focus on situations where all members of the ensemble are a priori expected to perform equally well, which is the case of several popular methods such as random for
Distribution-Dependent Rates for Multi-Distribution Learning
To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly across a variety of tasks. The recent multi-distribution learning (MDL) framework tackles this objective in a dynamic interaction with the environment, where the learner has sampling access to each target distribution. Drawing inspiration fro
Stochastic Gradient Descent for Nonparametric Additive Regression
This paper introduces an iterative algorithm for training nonparametric additive models that enjoys favorable memory storage and computational requirements. The algorithm can be viewed as the functional counterpart of stochastic gradient descent, applied to the coefficients of a truncated basis expansion of the component functions. We show that the resulting estimator satisfies an oracle inequality that allows for mo
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
In the rapidly evolving field of deep learning, the demand for models that are both expressive and computationally efficient has never been more critical. This paper introduces Orchid, a novel architecture designed to address the quadratic complexity of traditional attention mechanisms without compromising the ability to capture long-range dependencies and in-context learning. At the core of this architecture lies a
We propose a neural coding framework harmonic toroidal codes in which abstract cognitive operations are implemented through dynamical activity on manifolds derived from music theoretic structures.
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons
When training neural networks, dying neurons -- units becoming inactive or saturated -- are traditionally seen as harmful. This paper sheds new light on this phenomenon. By exploring the impact of various hyperparameter configurations on dying neurons during training, we gather insights on how to improve upon sparse training approaches to pruning. We introduce Demon Pruning (DemP), a method that controls the prolifer
Matching Semantically Similar Non-Identical Objects
Not identical but similar objects are ubiquitous in our world, ranging from four-legged animals such as dogs and cats to cars of different models and flowers of various colors. This study addresses a novel task of matching such non-identical objects at the pixel level. We propose a weighting scheme of descriptors, Semantic Enhancement Weighting (SEW), that incorporates semantic information from object detectors into
Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural network (PCNN) is a well applicated model for imitating the computation characteristics of the human brain in computer vision and neural network fields. However, differences between the PCNN and biological neural systems remain: limited neural
Reconstructing Hand-Held Objects in 3D from Images and Videos
Objects manipulated by the hand (i.e., manipulanda) are particularly challenging to reconstruct from Internet videos. Not only does the hand occlude much of the object, but also the object is often only visible in a small number of image pixels. At the same time, two strong anchors emerge in this setting: (1) estimated 3D hands help disambiguate the location and scale of the object, and (2) the set of manipulanda is
The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, with thousands of trusted users and multiple user interfaces, offering a unique experience and enormous capabilities. To name a few, the NOSTR applications include but are not limited to direct messaging, file sharing, audio/video streaming,
ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning
We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the
Symmetric Linear Bandits with Hidden Symmetry
High-dimensional linear bandits with low-dimensional structure have received considerable attention in recent studies due to their practical significance. The most common structure in the literature is sparsity. However, it may not be available in practice. Symmetry, where the reward is invariant under certain groups of transformations on the set of arms, is another important inductive bias in the high-dimensional ca
Notable events recorded on this day and month across all years.