Record 12052026 · captured 2026-08-25
The world looked up C. Joseph Vijay. 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.
Chandrasekaran Joseph Vijay is an Indian politician and former actor who is currently serving as the ninth chief minister of Tamil Nadu since May 2026. He is the founder and president of the political party Tamilaga Vettri Kazhagam (TVK). Prior to entering pol
Orthohantavirus is a genus of viruses which includes all hantaviruses that cause disease in humans. Hantaviruses are naturally found primarily in rodents. In general, each hantavirus is carried by one rodent species and each rodent that carries a hantavirus ca
Sheryl Patrice Underwood is an American comedian, actress, and television host. She first rose to prominence in the comedy world as the first female finalist in 1989's Miller Lite Comedy Search. Underwood was one of the hosts on the CBS Daytime talk show The T
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
John Derek Radford is a British convicted serial sex offender, known as the Black Cab Rapist. Worboys was convicted in 2009 for attacks on 12 women, committed between 2007 and 2008. In 2019, he was convicted for attacks on four more women, the earliest of whic
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
Tony Hinchcliffe is an American comedian and podcaster. Since 2013, he has hosted the stand-up comedy podcast Kill Tony, a showcase of professional and amateur comedians who take turns doing one-minute sets. Hinchcliffe is known primarily for roast comedy, hav
Yahoo is an American web portal based in Sunnyvale, California. It provides the search engine Yahoo Search and related services including My Yahoo, Yahoo Mail, Yahoo News, Yahoo Finance, Yahoo Sports, y!entertainment, yahoo!life, and its advertising platform,
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
Mortal Kombat II is a 2026 American martial arts high fantasy film based on the video-game series created by Ed Boon and John Tobias. It is the sequel to Mortal Kombat (2021) and is the fourth installment in the Mortal Kombat film series. Directed by Simon McQ
Chelsea Joy Handler is an American stand-up comedian, actress, writer, television host, and producer. She hosted the late-night talk show Chelsea Lately on the E! network from 2007 to 2014 and released a documentary series, Chelsea Does, on Netflix in January
MV Hondius hantavirus outbreak
In April 2026, an outbreak of hantavirus infection caused by the Andes virus was identified on the Dutch cruise ship MV Hondius. There were ten confirmed cases and two suspected cases directly linked to the outbreak as of 22 May. There have been three deaths,
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
Wade Steven Wilson is an American criminal convicted of the 2019 murders of Kristine Melton and Diane Ruiz in Cape Coral, Florida. Due to sharing the name of the Marvel character Wade "Deadpool" Wilson, Wilson has been referred to in the media as the "Deadpool
Trisha Krishnan is an Indian actress known for her work primarily in Tamil and Telugu cinema. One of the highest-paid actresses in India, she has sustained a successful career as a leading actress for over two decades in Tamil cinema. Trisha gained prominence
Flash Forward is an album by the Siegel–Schwall Band. Released by Alligator Records in 2005, it was the second album recorded by the band after they re-formed in 1987, and their first studio album since R.I.P. Siegel/Schwall in 1974.
The chief minister of Tamil Nadu is the head of government of the Indian state of Tamil Nadu. In accordance with the Constitution of India, the governor is a state's de jure head, while the de facto authority rests with the chief minister. Following elections
.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
Remarkably Bright Creatures (film)
Remarkably Bright Creatures is a 2026 American drama film directed by Olivia Newman, who co-wrote the film with screenwriter John Whittington. It is an adaptation on the 2022 novel of the same name by Shelby Van Pelt. The film stars Sally Field, Lewis Pullman,
Sir David Frederick Attenborough is an English broadcaster, natural historian, and writer. His presenting career began as host of Zoo Quest in 1954, and has spanned seven decades; it includes the nine documentary series forming The Life Collection, Natural Wor
Legends is a British crime thriller television series written and created by Neil Forsyth and produced by his Tannadice Pictures production company. It is a dramatisation of the true story of undercover British customs investigators who infiltrated the drug wo
Mesomachilis is a genus of jumping bristletails in the family Machilidae. There are about six described species in Mesomachilis.
The Odyssey is a 2026 epic action fantasy film written and directed by Christopher Nolan, who produced it with his wife Emma Thomas. An adaptation of Homer's ancient Greek epic poem the Odyssey, it stars an ensemble cast including Matt Damon, Tom Holland, Anne
The Devil Wears Prada 2 is a 2026 American comedy drama film directed by David Frankel and written by Aline Brosh McKenna. A sequel to the 2006 film The Devil Wears Prada, it sees Meryl Streep, Anne Hathaway, Emily Blunt, and Stanley Tucci reprising their role
Plasma contactors are devices used on spacecraft in order to prevent accumulation of electrostatic charge through the expulsion of plasma.
Jaafar Jeremiah Jackson is an American actor and singer. A member of the Jackson family, he released his debut single "Got Me Singing" in 2019 but gained popularity for portraying his uncle Michael Jackson in the record-breaking biographical film Michael (2026
Sean Thomas Strickland is an American professional mixed martial artist. He currently competes in the Middleweight division of the Ultimate Fighting Championship (UFC), where he is the current and two-time UFC Middleweight Champion. A professional since 2008,
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
Spencer William Pratt is an American reality television personality. In 2007, he began dating Heidi Montag, a primary cast member of the reality television series The Hills and came to prominence after being cast in the series. A feud between them and Montag's
The Mistick Krewe of Comus (MKC), founded in 1856, is the oldest extant New Orleans, Louisiana Carnival Krewe, the longest to continually parade with few interruptions from 1856 to 1991, and continues to hold a tableau ball for its members and guests, to date.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Probability Bracket Notation: Multivariable Systems and Static Bayesian Networks
We extend Probability Bracket Notation (PBN), inspired by the Dirac notation in quantum mechanics, to multivariable probability systems and static Bayesian networks (BNs). By defining probability distributions and conditional expectations in a unified, basis-independent algebraic form, PBN provides a systematic way to represent and manipulate dependencies among random variables. Using the well-known Student BN as an
Provable Exactness for Asymmetric Low-Rank SDP Learning
Low-rank factorization is a standard way to make structured optimization problems in machine learning more tractable by replacing matrix variables with compact factors. For positive semidefinite (PSD) variables, the symmetric Burer--Monteiro factorization (sBMF) writes $Z=XX^\top$ with a single low-rank factor $X$. A recent asymmetric alternative (aBMF) writes $Z=XY^\top$ and adds a quadratic penalty $(γ/2)\|X-Y\|_F^
Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
Existing rigorous convergence guarantees for the Hamiltonian Monte Carlo (HMC) algorithm use Gaussian auxiliary momentum variables, which are crucially symmetrically distributed. We present a novel convergence analysis for HMC utilizing new dynamical and probabilistic arguments. The convergence is rigorously established under significantly weaker conditions, which among others allow for general auxiliary distribution
Spectrally Adapted Physics-Informed Neural Networks for Solving Unbounded Domain Problems
Solving analytically intractable partial differential equations (PDEs) that involve at least one variable defined on an unbounded domain arises in numerous physical applications. Accurately solving unbounded domain PDEs requires efficient numerical methods that can resolve the dependence of the PDE on the unbounded variable over at least several orders of magnitude. We propose a solution to such problems by combining
Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Control
We study projection-free methods for functional constrained optimization with convex or smooth nonconvex objectives. Such problems arise in applications such as portfolio optimization and radiation therapy planning, where risk-aware criteria and sparsity frequently appear together. For the convex setting, we propose a Level Conditional Gradient (LCG) method that combines a level-set outer loop with a conditional grad
Learning Strategic Value and Cooperation in Multi-Player Stochastic Games through Side Payments
We study general-sum, multi-player stochastic games with transferable utility, motivated by settings where agents can use side payments to make cooperation individually rational. Building on the Harsanyi--Shapley (HS) value for normal-form games, we introduce two HS-based value notions for stochastic games: HS-S, defined by aggregating dynamic coalition-versus-complement threat powers, and Coco-S, defined as fixed po
In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in a topological clustering algorithm plays an important role in adaptively generating well-separated clusters during a self-organizing process. In this paper, we propose an ART-based
LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning
Fine-tuning large language models (LLMs) is crucial for improving their performance on downstream tasks, but full-parameter fine-tuning (Full-FT) is computationally expensive and memory-intensive. Parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), address this by optimizing only a small subset of parameters. However, LoRA may underperform Full-FT in certain scenarios due to the intrin
An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors
Security code review is a time-consuming and labor-intensive process typically requiring integration with automated security defect detection tools. However, existing security analysis tools struggle with poor generalization, high false positive rates, and coarse detection granularity. Large Language Models (LLMs) have been considered promising candidates for addressing those challenges. In this study, we conducted a
U-shaped architectures have long dominated the field of medical image segmentation, while Transformers are widely employed for modeling long-range dependencies. The former typically handles scale variations implicitly by aggregating multi-level features, whereas the efficiency of the latter is constrained by its quadratic computational and memory complexity. In this work, we propose an effective alternative to tradit
FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time
Heart murmurs are abnormal sounds caused by turbulent blood flow in the heart. Several diagnostic methods are available to detect heart murmurs and their severity, including cardiac auscultation, echocardiography, and phonocardiography (PCG). However, these methods have limitations, including the need for extensive training among healthcare providers, the cost and accessibility of echocardiography, and noise interfer
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
Federated Learning (FL) is a distributed and privacy-preserving machine learning paradigm that coordinates multiple clients to train a model while keeping the raw data localized. However, this traditional FL poses some challenges, including privacy risks, data heterogeneity, communication bottlenecks, and system heterogeneity issues. To tackle these challenges, knowledge distillation (KD) has been widely applied in F
Quasi-Linear ICA for Motor Unit Decomposition during Dynamic Contractions
Decomposing surface electromyography (EMG) into the spike trains of individual motor neurons is a long-standing inverse problem and a key step toward motor-neuron-driven neural interfaces such as prosthetics and exoskeletons. The standard approach, independent component analysis (ICA) of the multichannel signal, assumes that the mixing from neurons to electrodes is stationary in time. This assumption fails during mov
Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs
Bias and fairness risks in Large Language Models (LLMs) vary substantially across deployment contexts, yet existing approaches lack systematic guidance for selecting appropriate evaluation metrics. We present a decision framework that maps LLM use cases, characterized by a model and population of prompts, to relevant bias and fairness metrics based on task type, whether prompts contain protected attribute mentions, a
Generative diffusion models have emerged as a powerful tool for high-quality image synthesis, yet their iterative nature demands significant computational resources. This paper proposes an efficient time step sampling method based on an image spectral analysis of the diffusion process, aimed at optimizing the denoising process. Instead of the traditional uniform distribution-based time step sampling, we introduce a B
The combination of machine learning (ML) and sparsity-promoting techniques is enabling direct extraction of governing equations from data, revolutionizing computational modeling in diverse fields of science and engineering. The discovered dynamical models could be used to address challenges in climate science, neuroscience, ecology, finance, epidemiology, and beyond. However, most existing sparse identification metho
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians
We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set $S \subseteq \mathbb{R}^d$. Kontonis, Tzamos, and Zampetakis (FOCS'19) gave a $d^{\mathrm{poly}(1/\varepsilon)}$ time algorithm for finding $\varepsilon$-accurate parameters for the special case of Gaussian distributions with diagonal covariance matrix. Recently, Diakonikolas, Kane, Pittas, and Zarifi
Diffusion Models are Evolutionary Algorithms
In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a denoising process and reversed evolution as diffusion, we mathematically demonstrate that diffusion models inherently perform evolutionary algorithms, naturally encompassing selection, mutation, and reproductive isolation. Building on this equivalence, we propose the Diffusion E
Human dexterity arises from combining high-level task reasoning with finger-level dexterity control and physical compliance at the muscle and skin layers. In robotics, large Vision-Language-Action (VLA) models demonstrate text-conditioned high-level planning across diverse manipulation tasks, typically using pincher grippers. Smaller imitation-learning policies, conversely, show success in dexterous tasks using highe
Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA
Machine unlearning is an emerging technology that removes a subset of the training data from a trained model without significantly affecting the model performance on the remaining data. This topic is becoming increasingly important in protecting user privacy and eliminating harmful or outdated data. The key challenge lies in effectively and efficiently unlearning specific information without compromising the model
Topological Data Analysis Applications in Natural Language Processing: A Survey
The surge of data available on the Internet has driven the adoption of a wide range of computational methods for analyzing and extracting insights from large-scale data. Among these, Machine Learning (ML) has become a central paradigm, offering powerful tools for pattern discovery, prediction, and representation learning across many domains. At the same time, real-world data often exhibit properties such as noise, im
Implicit Neural Compression of Point Clouds
Point clouds have gained prominence across numerous applications due to their ability to accurately represent 3D objects and scenes. However, efficiently compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we propose NeRC$^3$, a novel point cloud compression framework that leverages implicit neural representations (INRs) to encode both geometry and attributes of d
Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields
Novel-view synthesis plays a crucial role in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent approaches, such as 3D Gaussian Splatting (3DGS), have emerged as state-of-the-art solutions, offering high-quality novel view synthesis in real time. However, training 3DGS models remains slow, particularly for high-resolution images, often requiring hours to fit a scene with 200 v
Ister: Linear Transformer for Efficient Multivariate Time Series Forecasting
Transformer-based models have achieved remarkable success in multivariate time series forecasting (MTSF) by capturing long-range dependencies. However, their widespread adoption is hindered by the quadratic computational complexity of self-attention, which limits scalability on high-dimensional sequences. To address this challenge, we propose the Inverted Seasonal-Trend Decomposition Transformer (Ister), a novel arch
PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models
Recent text-to-image (T2I) models have exhibited remarkable performance in generating high-quality images from text descriptions. However, these models are vulnerable to misuse, particularly generating not-safe-for-work (NSFW) content, such as sexually explicit, violent, political, and disturbing images, raising serious ethical concerns. In this work, we present PromptGuard, a novel content moderation technique that
Notable events recorded on this day and month across all years.