Record 27022026 · captured 2026-08-25
The world looked up Rashmika Mandanna. 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.
Rashmika Mandanna is an Indian actress who primarily works in Telugu and Hindi films. Her accolades include four SIIMA Awards and a Filmfare Award South. One of India's highest-paid actresses, she was featured in Forbes India's 2024 list of "30 Under 30".
Deverakonda Vijay Sai, widely known as Vijay Deverakonda, is an Indian actor and film producer who works in Telugu films. Vijay is the recipient of a Filmfare Award, a Nandi Award and three SIIMA Awards.
Scream 7 is a 2026 American slasher film directed by Kevin Williamson and written by Williamson and Guy Busick. It is the sequel to Scream VI (2023) and the seventh installment in the Scream film series. The film stars Neve Campbell, Jasmin Savoy Brown, Mason
On 4 August 2002, two 10-year-old girls, Holly Marie Wells and Jessica Amiee Chapman, were lured into the home of a local resident and school caretaker, Ian Huntley, in Soham, Cambridgeshire, England. Both children were murdered – most likely by asphyxiation –
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
Alysa Liu is an American figure skater. She is the 2026 Olympic champion in both the women's singles and team events, the 2025 World champion, the 2022 World bronze medalist, the 2025–26 Grand Prix Final champion, a two-time Grand Prix medalist, a four-time Ch
Erotic photography is a style of art photography of an erotic, sexually suggestive or sexually provocative nature. It is a type of erotic art.
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
Nemesio Rubén Oseguera Cervantes, commonly referred to by his alias "El Mencho", was a Mexican drug lord and head of the Jalisco New Generation Cartel (CJNG), an organized crime group based in Jalisco. He was the most wanted person in Mexico and one of the mos
Robert Reed Carradine was an American actor. He made his first appearances in television Western series, such as Bonanza and his brother David's television series, Kung Fu. Carradine starred as Lewis Skolnick in teen comedy film series Revenge of the Nerds and
The ICC Men's T20 World Cup, formerly the ICC World Twenty20, is a biennial world cup for cricket in Twenty20 International (T20I) format, organised by the International Cricket Council (ICC). It was held in every odd year from 2007 to 2009, and since 2010 has
Eric A. Slover is a United States Army Chief Warrant Officer 5 (CW5) who received the Medal of Honor for his actions during Operation Absolute Resolve. During the operation, Slover was a MH-47 Chinook pilot in the 160th Special Operations Aviation Regiment (Ai
Survivor 50: In the Hands of the Fans
Survivor 50: In the Hands of the Fans is the 50th season of the American competitive reality television series Survivor. It premiered on February 25, 2026, on CBS in the United States, and it is the eighteenth consecutive season to be filmed in the Mamanuca Is
Paula Casey Means, known as Casey Means, is an American wellness influencer, author, and former physician.
Bridgerton is an American alternative history, Regency romance television series created by Chris Van Dusen for Netflix. Based on the book series of the same name by Julia Quinn, it is Shondaland's first scripted show for Netflix. The series stars an ensemble
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
Resident Evil Requiem is a 2026 survival horror game developed and published by Capcom. It is the ninth main game in the Resident Evil series, following Resident Evil Village (2021). It features a new playable character, the FBI analyst Grace Ashcroft, who inv
John Fitzgerald Kennedy Jr., also referred to as JFK Jr., was an American businessman, attorney, magazine publisher, and journalist. He was the son of the 35th U.S. president John F. Kennedy, and First Lady Jacqueline Kennedy.
Ilhan Abdullahi Omar is an American politician serving as the U.S. representative for Minnesota's 5th congressional district since 2019. The district includes all of Minneapolis and some of its first-ring suburbs. From 2017 to 2019, Omar served in the Minnesot
Eileen Feng Gu, also known by her Chinese name Gu Ailing (谷爱凌), is a Chinese-American freestyle skier and model. She has represented China in halfpipe, slopestyle, and big air events since the 2018–19 season. With three gold and three silver medals, Gu is the
Smiling Friends is an adult animated sitcom created by Zach Hadel and Michael Cusack for Cartoon Network's nighttime programming block Adult Swim. The show revolves around the surreal misadventures of a small charity and its four employees dedicated to spreadi
Carolyn Jeanne Bessette-Kennedy was an American fashion publicist. Raised in Greenwich, Connecticut, she graduated from Boston University and joined Calvin Klein, where she rose from a sales position in Boston to publicity and show-production roles in New York
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
The Epstein files are a partially released collection of millions of documents, images, videos, and emails related to the activities of American financier and convicted child sex offender Jeffrey Epstein, including his social circle of public figures, politici
Eric William Dane was an American actor. After multiple television roles in the 1990s and 2000s, including his recurring role as Jason Dean on Charmed, he was cast as Dr. Mark Sloan on the ABC medical drama Grey's Anatomy. He went on to appear in films such as
Michelle Christine Trachtenberg was an American actress. After beginning her career in commercials at age three, she made her television debut in her first credited role on the Nickelodeon series The Adventures of Pete & Pete (1994–1996) and her feature film d
A company is a legal entity representing an association of legal persons with a shared objective, such as generating profit or benefiting society. Depending on the jurisdiction, companies can take on various forms, including voluntary associations, nonprofit o
A Knight of the Seven Kingdoms (TV series)
A Knight of the Seven Kingdoms is an American fantasy drama television series created by Ira Parker and George R. R. Martin. A prequel to Game of Thrones (2011–2019), it is the third television series in Martin's A Song of Ice and Fire franchise and is an adap
The 2025–26 UEFA Champions League was the 71st season of Europe's premier club football tournament organised by UEFA, and the 34th season since it was rebranded from the European Cup to the UEFA Champions League.
The 2026 ICC Men's T20 World Cup was the tenth edition of the ICC Men's T20 World Cup, co-hosted by Board of Control for Cricket in India and Sri Lanka Cricket from 7 February to 8 March 2026. Sri Lanka had previously hosted the competition in 2012 and India i
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Hardness of Maximum Likelihood Learning of DPPs
Determinantal Point Processes (DPPs) are a widely used probabilistic model for negatively correlated sets. DPPs have been successfully employed in Machine Learning applications to select a diverse, yet representative subset of data. In these applications, a set of parameters that maximize the likelihood of the data is typically desirable. The algorithms used for this task to date either optimize over a limited family
VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle Intelligent Connected System
The field of intelligent connected in modern vehicles continues to expand, and the functions of vehicles become more and more complex with the development of the times. This has also led to an increasing number of vehicle vulnerabilities and many safety issues. Therefore, it is particularly important to identify high-risk vehicle intelligent connected systems, because it can inform security personnel which systems ar
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
We study multi-product inventory control problems where a manager makes sequential replenishment decisions based on partial historical information in order to minimize its cumulative losses. Our motivation is to consider general demands, losses and dynamics to go beyond standard models which usually rely on newsvendor-type losses, fixed dynamics, and unrealistic i.i.d. demand assumptions. We propose MaxCOSD, an onlin
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection
Hyperspectral anomaly detection (HAD) is widely used in Earth observation and deep space exploration. A major challenge for HAD is the complex background of the input hyperspectral images (HSIs), resulting in anomalies confused in the background. On the other hand, the lack of labeled samples for HSIs leads to poor generalization of existing HAD methods. This paper starts the first attempt to study a new and generali
Entropic Matching for Expectation Propagation of Markov Jump Processes
We propose a novel, tractable latent state inference scheme for Markov jump processes, for which exact inference is often intractable. Our approach is based on an entropic matching framework that can be embedded into the well-known expectation propagation algorithm. We demonstrate the effectiveness of our method by providing closed-form results for a simple family of approximate distributions and apply it to the gene
Differentiable Particle Filtering using Optimal Placement Resampling
Particle filters are a frequent choice for inference tasks in nonlinear and non-Gaussian state-space models. They can either be used for state inference by approximating the filtering distribution or for parameter inference by approximating the marginal data (observation) likelihood. A good proposal distribution and a good resampling scheme are crucial to obtain low variance estimates. However, traditional methods li
In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS). Concretely, we first reveal that multi-head attentions (MHAs), the main computational overhead of MLLMs, are often redundant to downstream tasks. Based on this observation, EAS evaluates the attention redundancy and skips the less important MHAs
Procedural Fairness in Machine Learning
Fairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focus on another dimension of fairness, i.e., procedural fairness. In this paper, we first define the procedural fairness of ML models by drawing from the established understanding of procedural fairness in philosophy and psychology fields, and
Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces
In this work, we consider the approximation of a large class of bounded functions, with minimal regularity assumptions, by ReLU neural networks. We show that the approximation error can be bounded from above by a quantity proportional to the uniform norm of the target function and inversely proportional to the product of network width and depth. We inherit this approximation error bound from Fourier features residual
RLSF: Fine-tuning LLMs via Symbolic Feedback
Large Language Models (LLMs) have transformed AI but often struggle with tasks that require domain-specific reasoning and logical alignment. Traditional fine-tuning methods do not leverage the vast amount of symbolic domain-knowledge available to us via symbolic reasoning tools (e.g., provers), and are further limited by sparse rewards and unreliable reward models. We introduce Reinforcement Learning via Symbolic Fee
Meta-Designing Quantum Experiments with Language Models
Artificial Intelligence (AI) can solve complex scientific problems beyond human capabilities, but the resulting solutions offer little insight into the underlying physical principles. One prominent example is quantum physics, where computers can discover experiments for the generation of specific quantum states, but it is unclear how finding general design concepts can be automated. Here, we address this challenge by
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning
We introduce StableMaterials, a novel approach for generating photorealistic physical-based rendering (PBR) materials that integrate semi-supervised learning with Latent Diffusion Models (LDMs). Our method employs adversarial training to distill knowledge from existing large-scale image generation models, minimizing the reliance on annotated data and enhancing the diversity in generation. This distillation approach a
In recent years, transformer-based models have exhibited considerable potential in point cloud instance segmentation. Despite the promising performance achieved by existing methods, they encounter challenges such as instance query initialization problems and excessive reliance on stacked layers, rendering them incompatible with large-scale 3D scenes. This paper introduces a novel method, named SGIFormer, for 3D insta
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective
Parameter-efficient fine-tuning for continual learning (PEFT-CL) has shown promise in adapting pre-trained models to sequential tasks while mitigating catastrophic forgetting problem. However, understanding the mechanisms that dictate continual performance in this paradigm remains elusive. To unravel this mystery, we undertake a rigorous analysis of PEFT-CL dynamics to derive relevant metrics for continual scenarios
Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs
Combinatorial optimization problems near algorithmic phase transitions represent a fundamental challenge for both classical algorithms and machine learning approaches. Among them, graph coloring stands as a prototypical constraint satisfaction problem exhibiting sharp dynamical and satisfiability thresholds. Here we introduce a physics-inspired neural framework that learns to solve large-scale graph coloring instance
Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 Mortality
Accurate predictive models are crucial for analysing COVID-19 mortality trends. This study evaluates the impact of a custom data preprocessing pipeline on ten machine learning models predicting COVID-19 mortality using data from Our World in Data (OWID). Our pipeline differs from a standard preprocessing pipeline through four key steps. Firstly, it transforms weekly reported totals into daily updates, correcting repo
Evaluating the Evaluator: Measuring LLMs' Adherence to Task Evaluation Instructions
LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to widespread use of RLHF (Reinforcement Learning from Human Feedback), state-of-the-art LLMs like GPT4 and Llama3 are expected to have strong alignment with human preferences when prompted for a quality judgement, such as the coherence of a text. While this
Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing
Mamba-based models have drawn much attention in offline RL. However, their selective mechanism often detrimental when key steps in RL sequences are omitted. To address these issues, we propose a simple yet effective structure, called Decision MetaMamba (DMM), which replaces Mamba's token mixer with a dense layer-based sequence mixer and modifies positional structure to preserve local information. By performing se
Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
Open-set face forgery detection poses significant security threats and presents substantial challenges for existing detection models. These detectors primarily have two limitations: they cannot generalize across unknown forgery domains and inefficiently adapt to new data. To address these issues, we introduce an approach that is both general and parameter-efficient for face forgery detection. It builds on the assumpt
Abstracted Gaussian Prototypes for True One-Shot Concept Learning
We introduce a cluster-based generative image segmentation framework to encode higher-level representations of visual concepts based on one-shot learning inspired by the Omniglot Challenge. The inferred parameters of each component of a Gaussian Mixture Model (GMM) represent a distinct topological subpart of a visual concept. Sampling new data from these parameters generates augmented subparts to build a more robust
On the Complexity of Neural Computation in Superposition
Superposition, the ability of neural networks to represent more features than neurons, is increasingly seen as key to the efficiency of large models. This paper investigates the theoretical foundations of computing in superposition, establishing complexity bounds for explicit, provably correct algorithms. We present the first lower bounds for a neural network computing in superposition, showing that for a broad class
Beyond Attribution: Unified Concept-Level Explanations
There is an increasing need to integrate model-agnostic explanation techniques with concept-based approaches, as the former can explain models across different architectures while the latter makes explanations more faithful and understandable to end-users. However, existing concept-based model-agnostic explanation methods are limited in scope, mainly focusing on attribution-based explanations while neglecting diverse
Testing the Efficacy of Hyperparameter Optimization Algorithms in Short-Term Load Forecasting
Accurate forecasting of electrical demand is essential for maintaining a stable and reliable power grid, optimizing the allocation of energy resources, and promoting efficient energy consumption practices. This study investigates the effectiveness of five hyperparameter optimization (HPO) algorithms -- Random Search, Covariance Matrix Adaptation Evolution Strategy (CMA--ES), Bayesian Optimization, Partial Swarm Optim
Multi-view biomedical foundation models for molecule-target and property prediction
Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates graph, image and text views in a foundation model setting and may be readily exten
Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy
Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. However, LLMs may produce invalid or hallucinated test cases, which can mislead feedback loops and degrade the performance of agents in refining and improving code. This paper introduces VALTEST, a novel framework that leverages semantic entr
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