Record 24112025 · 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.
Complete record JSON
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
Google Chrome is a cross-platform web browser developed by Google. It was launched in September 2008 for Microsoft Windows and was built with free software components from Apple WebKit and Mozilla Firefox. Versions for Linux, macOS, iOS, iPadOS, and Android we
Wicked: For Good is a 2025 musical fantasy film directed by Jon M. Chu and written by Winnie Holzman and Dana Fox. The sequel to Wicked (2024), it adapts the second act of the 2003 stage musical by Stephen Schwartz and Holzman, which was loosely based on Grego
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
The 2025 Full Gear, also promoted as Full Gear presented by DC, was a professional wrestling pay-per-view (PPV) event produced by All Elite Wrestling (AEW). It was the seventh annual Full Gear and took place on November 22, 2025, at the Prudential Center in Ne
2025 Formula One World Championship
The 2025 FIA Formula One World Championship was a motor racing championship for Formula One cars and the 76th running of the Formula One World Championship. It was recognised by the Fédération Internationale de l'Automobile (FIA), the governing body of interna
Senuran Muthusamy is a South African cricketer. He made his international debut for the South Africa cricket team in October 2019. Muthusamy traces his ancestry to South India, with family in Nagapattinam, Tamil Nadu.
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
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.
The Family Man (Indian TV series)
The Family Man is an Indian Hindi-language spy thriller streaming television series created by Raj & DK for Amazon Prime Video. It features Manoj Bajpayee as Srikant Tiwari, a middle-class man secretly working as an intelligence officer for the Threat Analysis
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
Eberechi Oluchi "Ebere" Eze is an English professional footballer who plays as an attacking midfielder for Premier League club Arsenal and the England national team.
Wicked, is a 2024 American musical fantasy film directed by Jon M. Chu and written by Winnie Holzman and Dana Fox. It adapts the first act of the 2003 stage musical by Stephen Schwartz and Holzman, which was loosely based on Gregory Maguire's 1995 novel, a re-
Cynthia Erivo is a British actress and singer. Known for her work on both stage and screen, she is the recipient of several accolades and one of few individuals nominated for an Emmy, a Grammy, an Oscar, and a Tony Award (EGOT), winning all but the Oscar. Eriv
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.
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
Ariana Grande-Butera is an American singer, songwriter, and actress. Known for her four-octave vocal range, which extends into the whistle register, she is an influential figure in popular music. Publications such as Rolling Stone and Billboard have deemed Gra
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
The Beast in Me is an American psychological crime thriller television miniseries for Netflix, starring Claire Danes and Matthew Rhys. Created by Gabe Rotter, the series follows an author (Danes) who begins writing a book about her new next-door neighbor (Rhys
Marjorie Taylor Greene, also known by her initials MTG, is an American politician, businesswoman, and conspiracy theorist who served as the U.S. representative for Georgia's 14th congressional district from 2021 until her resignation in 2026. A former member o
Zohran Kwame Mamdani is an American politician who has served since 2026 as the 112th mayor of New York City. A member of the Democratic Party and the Democratic Socialists of America, he represented the 36th district in the New York State Assembly from 2021 t
Rodney Ray Rogers Jr. was an American professional basketball player who played for several teams in the National Basketball Association (NBA). He played college basketball for the Wake Forest Demon Deacons, earning consensus second-team All-American honors in
Thomas Read Wilson is an English television presenter, author, actor and singer. He is best known as the client coordinator on the E4 reality television series; Celebs Go Dating. In 2021, he was the runner-up on Celebrity Best Home Cook. In 2025, he was the ru
James Abram Garfield was the 20th president of the United States, serving from March 1881 until his death in September that year after being shot in July. A preacher, lawyer, and Civil War general, Garfield served nine terms in the United States House of Repre
Claire Catherine Danes is an American actor. Prolific in film and television since her teens, she is the recipient of three Primetime Emmy Awards and four Golden Globe Awards. In 2012 and 2026, Time named her one of the 100 most influential people in the world
Ian David Machado Garry is an Irish professional mixed martial artist who currently competes in the Welterweight division of the Ultimate Fighting Championship (UFC). Prior to signing with the UFC, Garry was a Cage Warriors Welterweight Champion. As of 20 June
Frankenstein is a 2025 American Gothic science fiction horror film written, co-produced, and directed by Guillermo del Toro, based on the 1818 novel by Mary Shelley. The film stars Oscar Isaac as Victor Frankenstein and Jacob Elordi as the Creature, with Mia G
Train Dreams is a 2025 American period drama film directed by Clint Bentley, who co-wrote the screenplay with Greg Kwedar, based on the 2011 novella by Denis Johnson. The film stars Joel Edgerton, Felicity Jones, Nathaniel Arcand, Clifton Collins Jr., John Die
Wicked is a musical with music and lyrics by Stephen Schwartz and a book by Winnie Holzman. It is loosely based on the 1995 novel of the same name by Gregory Maguire, a reimagining of the 1900 novel The Wonderful Wizard of Oz by L. Frank Baum and its 1939 film
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
FaCells is a method, and an exhibition, that turns model internals into line based artworks. Aligned face photographs (CelebA, 260k images, 40 attributes) are translated into vector sketches suitable for an XY plotter. We study how to 'write' these drawings for a sequence model, comparing absolute vs. relative point encodings and random vs. travel-minimizing stroke order. A bidirectional LSTM is trained for a
Toward Super-polynomial Quantum Speedup of Equivariant Quantum Algorithms with SU($d$) Symmetry
We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU$(d)$ symmetries. It allows us to enhance a natural model of quantum computation -- permutational quantum computing (PQC) -- and define a more powerful model: PQC+. While PQC was shown to be efficiently classically simulatable, we exhibit a problem
Rigorous dynamical mean field theory for stochastic gradient descent methods
We prove closed-form equations for the exact high-dimensional asymptotics of a family of first order gradient-based methods, learning an estimator (e.g. M-estimator, shallow neural network, ...) from observations on Gaussian data with empirical risk minimization. This includes widely used algorithms such as stochastic gradient descent (SGD) or Nesterov acceleration. The obtained equations match those resulting from t
UplinkNet: Practical Commercial 5G Standalone (SA) Uplink Throughput Prediction
While 5G New Radio (NR) networks offer significant uplink throughput improvements, these gains are primarily realized when User Equipment (UE) connects to high-frequency millimeter wave (mmWave) bands. The growing demand for uplink-intensive applications, such as real-time UHD 4K/8K video streaming and Virtual Reality (VR)/Augmented Reality (AR) content, highlights the need for accurate uplink throughput prediction t
Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer. However, traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset usually produce unsatisfactory retrieval performance on colonoscopic datasets due
Minimax Statistical Estimation under Wasserstein Contamination
Contaminations are a key concern in modern statistical learning, as small but systematic perturbations of all datapoints can substantially alter estimation results. Here, we study Wasserstein-$r$ contaminations ($r\ge 1$) in an $\ell_q$ norm ($q\in [1,\infty]$), in which each observation may undergo an adversarial perturbation with bounded cost, complementing the classical Huber model, corresponding to total variatio
A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
Interpretability plays a crucial role in the application of statistical learning to estimate heterogeneous treatment effects (HTE) in complex diseases. In this study, we leverage a rule-based workflow, namely causal rule learning (CRL), to estimate and improve our understanding of HTE for atrial septal defect, addressing an overlooked question in the previous literature: what if an individual simultaneously belongs t
Multimodal manipulations (also known as audio-visual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely detection is crucial. The damage to either modality (i.e., visual or audio) can only be discovered through multimodal models that can exploit both pieces of information simultaneously. However, previou
Code-switching entails mixing multiple languages. It is an increasingly occurring phenomenon in social media texts. Usually, code-mixed texts are written in a single script, even though the languages involved have different scripts. Pre-trained multilingual models primarily utilize the data in the native script of the language. In existing studies, the code-switched texts are utilized as they are. However, using the
A statistical method for crack pre-detection in 3D concrete images
In practical applications, effectively segmenting cracks in large-scale computed tomography (CT) images holds significant importance for understanding the structural integrity of materials. Classical image-processing techniques and modern deep-learning models both face substantial computational challenges when applied directly to high resolution big data volumes. This paper introduces a statistical framework for crac
Interpretable Machine Learning for Survival Analysis
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly relevant for survival analysis, where the adoption of IML techniques promotes transparency, accountability and fairness in sensitive areas, such as clinical decision making
Aiming to reconstruct visual stimuli from brain signals, brain decoding has recently made significant progress using functional magnetic resonance imaging (fMRI). However, it still has challenging issues such as substantial individual differences and high data collection costs. To simplify these problems, most methods adopt the per-subject-per-model paradigm, but this greatly limits their applications. In this paper,
In machine learning practice it is often useful to identify relevant input features. Isolating key input elements, ranked according their respective degree of relevance, can help to elaborate on the process of decision making. Here, we propose a novel method to estimate the relative importance of the input components for a Deep Neural Network. This is achieved by leveraging on a spectral re-parametrization of the opt
Generative AI and Power Imbalances in Global Education: Frameworks for Bias Mitigation
This study examines how Generative Artificial Intelligence reproduces global power hierarchies in education and proposes a framework to address resulting inequities. Using a critical qualitative design, the study conducted zero-shot prompt testing with two leading systems, ChatGPT-4 Turbo and Gemini 1.5, and collected real-time outputs from Global North and South contexts. A critical interpretive analysis traced text
CATCODER: Repository-Level Code Generation with Relevant Code and Type Context
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, repository-level code generation presents unique challenges, particularly due to the need to utilize information spread across multiple files within a repository. Specifically, successful generation depends on a solid grasp of both general, context-agnostic knowledge and specific, context-dependent knowledge. Whi
We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets." Nets are dynamically assembled from overlapping net fragments, which are learned based on statistical regularities in sensory input. This architecture offers robustness to noise, deformation, and generalization to out-of-distribution data,
With the proliferation of social media posts in recent years, the need to detect sentiments in multimodal (image-text) content has grown rapidly. Since posts are user-generated, the image and text from the same post can express different or even contradictory sentiments, leading to potential \textbf{sentiment discrepancy}. However, existing works mainly adopt a single-branch fusion structure that primarily captures t
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image Restoration
Recently, the degenerate preconditioned proximal point (PPP) method provides a unified and flexible framework for designing and analyzing operator-splitting algorithms such as Douglas-Rachford (DR). However, the degenerate PPP method exhibits weak convergence in the infinite-dimensional Hilbert space and lacks accelerated variants. To address these issues, we propose a Halpern-type PPP (HPPP) algorithm, which leverag
"Normalized Stress" is Not Normalized: How to Interpret Stress Correctly
Stress is among the most commonly employed quality metrics and optimization criteria for dimension reduction projections of high dimensional data. Complex, high dimensional data is ubiquitous across many scientific disciplines, including machine learning, biology, and the social sciences. One of the primary methods of visualizing these datasets is with two dimensional scatter plots that visually capture some properti
Aerospace embodied intelligence aims to empower unmanned aerial vehicles (UAVs) and other aerospace platforms to achieve autonomous perception, cognition, and action, as well as egocentric active interaction with humans and the environment. The aerospace embodied world model serves as an effective means to realize the autonomous intelligence of UAVs and represents a necessary pathway toward aerospace embodied intelli
Variational Search Distributions
We develop VSD, a method for conditioning a generative model of discrete, combinatorial designs on a rare desired class by efficiently evaluating a black-box (e.g. experiment, simulation) in a batch sequential manner. We call this task active generation; we formalize active generation's requirements and desiderata, and formulate a solution via variational inference. VSD uses off-the-shelf gradient based optimizat
MonoKAN: Certified Monotonic Kolmogorov-Arnold Network
Artificial Neural Networks (ANNs) have significantly advanced various fields by effectively recognizing patterns and solving complex problems. Despite these advancements, their interpretability remains a critical challenge, especially in applications where transparency and accountability are essential. To address this, explainable AI (XAI) has made progress in demystifying ANNs, yet interpretability alone is often in
In an era where vast amounts of data are collected and processed from diverse sources, there is a growing demand for sophisticated AI systems capable of intelligently fusing and analyzing this information. To address these challenges, researchers have turned towards integrating tools into LLM-powered agents to enhance the overall information fusion process. However, the conjunction of these technologies and the propo
Instance Configuration for Sustainable Job Shop Scheduling
The Job Shop Scheduling Problem (JSP) is a pivotal challenge in operations research and is essential for evaluating the effectiveness and performance of scheduling algorithms. Scheduling problems are a crucial domain in combinatorial optimization, where resources (machines) are allocated to job tasks to minimize the completion time (makespan) alongside other objectives like energy consumption. This research delves in
The increase in Arctic marine activity due to rapid warming and significant sea ice loss necessitates highly reliable, short-term sea ice forecasts to ensure maritime safety and operational efficiency. In this work, we present a novel data-driven approach for sea ice condition forecasting in the Gulf of Ob, leveraging sequences of radar images from Sentinel-1, weather observations, and GLORYS forecasts. Our approach
Notable events recorded on this day and month across all years.