Record 01042026 · captured 2026-08-25
The world looked up Dhurandhar: The Revenge. 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.
Dhurandhar: The Revenge is a 2026 Indian Hindi-language spy action-thriller film written and directed by Aditya Dhar. It is produced by Dhar, Lokesh Dhar, and Jyoti Deshpande under Jio Studios and B62 Studios. It is a sequel to the 2025 film Dhurandhar and the
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
The 2026 FIFA World Cup was the 23rd FIFA World Cup, the quadrennial international men's soccer championship contested by the national teams of the member associations of FIFA. The tournament began on June 11, 2026, and concluded on July 19 with Spain winning
Jaden Edward Dhananjay Ivey is an American professional basketball player who last played for the Chicago Bulls of the National Basketball Association (NBA). He played college basketball for the Purdue Boilermakers. Ivey has also played for the U.S. national u
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
Project Hail Mary is a 2026 American science fiction film produced and directed by Phil Lord and Christopher Miller and written by Drew Goddard, based on the 2021 novel of the same name by Andy Weir. It stars Ryan Gosling, who also produced the film, as Ryland
Scott Robert Mills is an English radio DJ, television presenter and occasional actor. He is best known for presenting the Scott Mills show on BBC Radio 1 from 2004 to 2022 and then, on BBC Radio 2, hosting the station's flagship breakfast show from January 202
WrestleMania 42, also promoted as WrestleMania Vegas, was a 2026 professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 42nd annual WrestleMania and took place as a two-night event on Saturday, April 18 and Sunday, April
Lamar Joseph Odom is an American former professional basketball player who played for four teams during his 14-year career in the National Basketball Association (NBA), and won back-to-back championships in 2009 and 2010 with the Los Angeles Lakers. He was als
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
Roberto De Zerbi is an Italian professional football head coach and former player who is the head coach of Premier League club Tottenham Hotspur.
2026 FIFA World Cup qualification
The 2026 FIFA World Cup qualification decided the 45 teams that joined hosts Canada, Mexico, and the United States at the 2026 FIFA World Cup.
.xyz is a top-level domain name that was proposed in ICANN's new generic top-level domain (gTLD) Program for consisting of the last three letters of the Latin-script alphabet. XYZ.com and CentralNic are the registries for the domain, which was created by entre
Artemis II was a crewed flyby of the Moon. It is currently the only crewed flight beyond low Earth orbit since Apollo 17 in 1972. It was the first crewed flight of the NASA-led Artemis program, the first crewed flight of the Space Launch System (SLS), and the
Something Very Bad Is Going to Happen
Something Very Bad Is Going to Happen is an American horror television miniseries created by Haley Z. Boston for Netflix. Boston serves as the series showrunner and is also an executive producer along with the Duffer Brothers. Camila Morrone and Adam DiMarco s
The Super Mario Galaxy Movie is a 2026 American animated adventure comedy film based on Nintendo's Mario video game franchise. Directed by Aaron Horvath and Michael Jelenic and written by Matthew Fogel, it is the sequel to The Super Mario Bros. Movie (2023). C
2026 FIFA World Cup qualification – UEFA second round
The UEFA second round of the qualification tournament for the 2026 FIFA World Cup, also known as the UEFA play-offs or European play-offs, was contested by sixteen teams from the UEFA segment of qualifying. The play-offs determined the final four European team
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
1937 tour of Germany by the Duke and Duchess of Windsor
Prince Edward, Duke of Windsor, and Wallis, Duchess of Windsor, visited Nazi Germany in October 1937. Edward had abdicated the British throne in December 1936, and his brother George VI had become king. Edward had been given the title Duke of Windsor on abdica
The Acarnanian Mountains is a mountain range in the northwestern part of the Aetolia-Acarnania regional unit in western Greece. It stretches from the village Monastiraki, near Vonitsa, in the north to Astakos in the south, with a total length of nearly 40 km.
Vanessa Kay is an American model. She is the ex-wife of Donald Trump Jr. They were married from 2005 to 2018, and had five children.
Project Hail Mary is a 2021 hard science fiction novel by American writer Andy Weir. It centers on science teacher and former biologist Ryland Grace, who wakes up aboard a spacecraft, afflicted with amnesia.
Jefri Nichol is an Indonesian actor and model. He is widely known following his role in Dear Nathan (2017).
The Drama is a 2026 American dark romantic comedy film written and directed by Kristoffer Borgli. It stars Zendaya and Robert Pattinson as a happily engaged couple whose relationship is tested by an unexpected revelation the week before their wedding.
Backrooms is a 2026 American science fiction psychological horror film directed and co-scored by Kane Parsons, and written by Will Soodik. It is based on Parsons's web series which was inspired by the "Backrooms" creepypasta. In the film, Clark, a furniture st
Mirchi Music Awards are presented annually by Radio Mirchi to honour both artistic and technical excellence of professionals in the Hindi language film music industry of India. The awards, given in seventeen different categories, were instituted to award the b
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
Paradise is an American post-apocalyptic political thriller television series created by Dan Fogelman and starring Sterling K. Brown, Julianne Nicholson, and James Marsden. It was released on Hulu in the United States on January 26, 2025. The series has receiv
2026 FIFA World Cup qualification (inter-confederation play-offs)
The inter-confederation play-offs of the 2026 FIFA World Cup qualification tournament determined two qualification spots for the 2026 FIFA World Cup, played in Canada, Mexico, and the United States. The play-offs took place on 26 and 31 March 2026 at two venue
The Backrooms is a fictional location invented in a 2019 thread on the imageboard website 4chan. The Backrooms are usually portrayed as an impossibly large extradimensional complex of empty rooms, accessed by exiting reality. They are one of the best-known exa
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Discriminative Dictionary Learning based on Statistical Methods
Sparse Representation (SR) of signals or data has a well founded theory with rigorous mathematical error bounds and proofs. SR of a signal is given by superposition of very few columns of a matrix called Dictionary, implicitly reducing dimensionality. Training dictionaries such that they represent each class of signals with minimal loss is called Dictionary Learning (DL). Dictionary learning methods like Method of Op
What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective
It has previously been reported that the representation that is learned in the first layer of deep Convolutional Neural Networks (CNNs) is highly consistent across initializations and architectures. In this work, we quantify this consistency by considering the first layer as a filter bank and measuring its energy distribution. We find that the energy distribution is very different from that of the initial weights and
Early Exiting Predictive Coding Neural Networks for Edge AI
The Internet of Things is transforming various fields, with sensors increasingly embedded in wearables, smart buildings, and connected equipment. While deep learning enables valuable insights from IoT data, conventional models are too computationally demanding for resource-limited edge devices. Moreover, privacy concerns and real-time processing needs make local computation a necessity over cloud-based solutions. Ins
Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications. However, it is challenging to directly bridge the reliable non-linear mapping relations between SC and functional magnetic resonance imaging (fMRI). In this paper, a novel symmetric diffusive generative adversarial network-based fMRI-to-S
Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent
Stochastic gradient descent (SGD) or stochastic approximation has been widely used in model training and stochastic optimization. While there is a huge literature on analyzing its convergence, inference on the obtained solutions from SGD has only been recently studied, yet it is important due to the growing need for uncertainty quantification. We investigate two computationally cheap resampling-based methods to const
Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion
Existing methods for synthesizing 3D human gestures from speech have shown promising results, but they do not explicitly model the impact of emotions on the generated gestures. Instead, these methods directly output animations from speech without control over the expressed emotion. To address this limitation, we present AMUSE, an emotional speech-driven body animation model based on latent diffusion. Our observation
This paper presents a novel approach at the intersection of machine learning and number theory, focusing on the classification of prime and non-prime numbers. At the core of our research is the development of a highly sparse encoding method, integrated with conventional neural network architectures. This combination has shown promising results, achieving a recall of over 99\% in identifying prime numbers and 79\% for
GenOL: Generating Diverse Examples for Name-only Online Learning
Online learning methods often rely on supervised data. However, under data distribution shifts, such as in continual learning (CL), where continuously arriving online data streams incorporate new concepts (e.g., classes), real-time manual annotation is impractical due to its costs and latency, which hinder real-time adaptation. To alleviate this, 'name-only' setup has been proposed, requiring only the name of
Measuring the Predictability of Recommender Systems using Structural Complexity Metrics
Recommender Systems (RS) shape the filtering and curation of online content, yet we have limited understanding of how predictable their recommendation outputs are. We propose data-driven metrics that quantify the predictability of recommendation datasets by measuring the structural complexity of the user-item interaction matrix. High complexity indicates intricate interaction patterns that are harder to predict; low
Image-Specific Adaptation of Transformer Encoders for Compute-Efficient Segmentation
Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segmentation tasks, their use of computational resources can be taxing on deployed devices. One way to overcome this challenge is by adapting the computation level to the specific needs of the input image rather than the current one-size-fits-al
Improving Plan Execution Flexibility using Block-Substitution
Partial-order plans in AI planning facilitate execution flexibility due to their less-constrained nature. Maximizing plan flexibility has been studied through the notions of plan deordering, and plan reordering. Plan deordering removes unnecessary action orderings within a plan, while plan reordering modifies them arbitrarily to minimize action orderings. This study, in contrast with traditional plan deordering and r
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
We devise a control-theoretic reinforcement learning approach to support direct learning of the optimal policy. We establish various theoretical properties of our approach, such as convergence and optimality of our analog of the Bellman operator and Q-learning, a new control-policy-variable gradient theorem, and a specific gradient ascent algorithm based on this theorem within the context of a specific control-theore
Improving Execution Concurrency in Partial-Order Plans via Block-Substitution
Partial-order plans in AI planning facilitate execution flexibility and several other tasks, such as plan reuse, modification, and decomposition, due to their less constrained nature. A \acrfull*{pop} specifies partial-order over actions, providing the flexibility of executing unordered actions in different sequences. This flexibility can be further extended by enabling parallel execution of actions in the POP to red
Large Language Models (LLMs) have been shown to encode clinical knowledge. Many evaluations, however, rely on structured question-answer benchmarks, overlooking critical challenges of interpreting and reasoning about unstructured clinical narratives in real-world settings. In this study we task eight Large Language models including two medical models (GPT-3.5, GPT-4, Mixtral-8x7B, Qwen-72B, LlaMa2, LlaMa3, OpenBioLLM
Large language models (LLMs) present an enormous evolution in the strategic potential of conversational recommender systems (CRS). Yet to date, research has predominantly focused upon technical frameworks to implement LLM-driven CRS, rather than end-user evaluations or strategic implications for firms, particularly from the perspective of a small to medium enterprises (SME) that makeup the bedrock of the global econo
Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts
Codebooks -- documents that operationalize concepts and outline annotation procedures -- are used almost universally by social scientists when coding political texts. To code these texts automatically, researchers are increasing turning to generative large language models (LLMs). However, there is limited empirical evidence on whether "off-the-shelf" LLMs faithfully follow real-world codebook operationalizati
Image Segmentation via Divisive Normalization: dealing with environmental diversity
Autonomous driving is a challenging scenario for image segmentation due to the presence of uncontrolled environmental conditions and the eventually catastrophic consequences of failures. Previous work suggested that a biologically motivated computation, the so-called Divisive Normalization, could be useful to deal with image variability, but its effects have not been systematically studied over different data sources
Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
Evolutionary multi-task optimization (EMTO) is an advanced optimization paradigm that improves search efficiency by enabling knowledge transfer across multiple tasks solved in parallel. Accordingly, a broad range of knowledge transfer methods (KTMs) have been developed as integral components of EMTO algorithms, most of which are tailored to specific problem settings. However, the design of effective KTMs typically re
JKO for Landau: a variational particle method for homogeneous Landau equation
Inspired by the gradient flow viewpoint of the Landau equation and the corresponding dynamic formulation of the Landau metric in [arXiv:2007.08591], we develop a novel implicit particle method for the Landau equation in the framework of the JKO scheme. We first reformulate the Landau metric in a computationally friendly form, and then translate it into the Lagrangian viewpoint using the flow map. A key observation is
We study transformers' in-context learning of variable-length Markov chains (VOMCs), focusing on the finite-sample accuracy as the number of in-context examples increases. Compared to fixed-order Markov chains (FOMCs), learning VOMCs is substantially more challenging due to the additional structural learning component. The problem is naturally suited to a Bayesian formulation, where the context-tree weighting (CT
The non-convex nature of trained neural networks has created significant obstacles in their incorporation into optimization models. In this context, Anderson et al. (2020) provided a framework to obtain the convex hull of the graph of a piecewise linear convex activation function composed with an affine function; this effectively convexifies activations such as the ReLU together with the affine transformation that pr
Positron emission tomography (PET) is a critical tool for diagnosing tumors and neurological disorders but poses radiation risks to patients, particularly to sensitive populations. While reducing injected radiation dose mitigates this risk, it often compromises image quality. To reconstruct full-dose-quality images from low-dose scans, we propose a Cycle-constrained Adversarial Denoising Convolutional Network (Cycle-
Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, legal, and financial sectors. Those are commonly referred to as low-resource domains, which comprise long-tail entities, due to the scarcity of available data. To address this, data augmentation techniq
Real-Time Operator Takeover for Visuomotor Diffusion Policy Training
We present a Real-Time Operator Takeover (RTOT) paradigm that enables operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back to desirable states or providing targeted corrective demonstrations. Within this framework, the operator can intervene to correct the robot's motion, after which control is smoothly returned to the policy until further intervention is needed. We
Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling
We propose the Value Gradient Sampler (VGS), a diffusion sampler parameterized by value functions. VGS generates samples from an unnormalized target density (i.e., energy) by evolving randomly initialized particles along the gradient of the value function. In many sampling problems where the target density exhibits invariant symmetries, value functions provide a novel approach to leveraging invariant networks for sam
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