Record 11052026 · 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
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
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,
UFC 328: Chimaev vs. Strickland was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on May 9, 2026, at the Prudential Center, in Newark, New Jersey, United States.
Mother's Day is a celebration honoring the mother of the family or individual, as well as motherhood, maternal bonds, and the influence of mothers in society. It is celebrated on different days in many parts of the world, most commonly in March or May. It comp
Khamzat Khizarovich Chimaev is a Russian-Emirati professional mixed martial artist and freestyle wrestler. He currently competes in the Middleweight division of the Ultimate Fighting Championship (UFC), where he is the former UFC Middleweight Champion. As of 1
The 2026 Backlash, also promoted as Backlash: Tampa, was a professional wrestling pay-per-view (PPV) and livestreaming event produced by WWE. It was the 21st Backlash event and took place on Saturday, May 9, 2026, at the Benchmark International Arena in Tampa,
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
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
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
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
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
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
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
Joshua Van Bawi Thawng is a Burmese and American professional mixed martial artist currently competing in the Flyweight division of the Ultimate Fighting Championship (UFC), where he is the current UFC Flyweight Champion. Van is the first fighter from Myanmar
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
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,
Tristan da Cunha, colloquially known as Tristan, is a remote group of volcanic islands in the South Atlantic Ocean. It is one of three constituent parts of the British Overseas Territory of Saint Helena, Ascension and Tristan da Cunha, with its own constitutio
.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
Elisabeth Anne Broderick was an American woman who murdered her ex-husband, Daniel T. Broderick III and his succeeding wife, Linda, on November 5, 1989, as an act of revenge after Daniel cheated on and divorced her. At a second trial that began on December 11,
Tamilaga Vettri Kazhagam is an Indian regional political party active in the state of Tamil Nadu and the union territory of Puducherry. It was founded on 2 February 2024 by actor-turned-politician C. Joseph Vijay, the party's president, and is headquartered in
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
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
S. Keerthana is an Indian politician from Tamil Nadu. She is currently the Minister for Industries in Government of Tamil Nadu and a member of the Tamil Nadu Legislative Assembly from Sivakasi in Virudhunagar district representing the Tamilaga Vettri Kazhagam.
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
2026 Tamil Nadu Legislative Assembly election
Elections to appoint the 234 members of the 17th Tamil Nadu Legislative Assembly, the highest body of the Government of Tamil Nadu, were held on 23 April 2026. The results were declared on 4 May 2026 by the Election Commission of India. It recorded the highest
Aadhav Arjuna is an Indian politician, political strategist, philanthropist and sports administrator from Tamil Nadu. He is the elected Member of Legislative Assembly (MLA) representing the Villivakkam assembly constituency in Chennai, having won the seat in t
Dysstroma colvillei is a species of geometrid moth in the family Geometridae. It is found in North America.
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
Sally Margaret Field is an American actress. Known for her roles on screen and stage, she has received various accolades including two Academy Awards, two Golden Globe Awards, and three Primetime Emmy Awards, as well as nominations for two British Academy Film
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm
HIVE-COTE 2.0: a new meta ensemble for time series classification
The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR ti
Multi-Stage Prototype Learning for Interpretable Time Series Classification
Deep learning methods are powerful tools in classifying multivariate time series data. Despite their high performance, these methods are hard to interpret, which diminishes their applications in high-risk domains such as healthcare. In this paper, we propose a novel multi-stage prototype learning framework for multivariate time series classification. By design, our framework identifies predictive temporal patterns in
Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems
This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distributed controller, called $κ$-distributed control, which lets the agents make control decisions based on the state information within distance $κ$ on the underlying graph. This controller can tu
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a `bake off', identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of featur
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the compar
On Diffusion Modeling for Anomaly Detection
Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By
Multispectral Indices for Wildfire Management
The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional ground-based techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This study investigates the use of multispectral aerial and satellite imagery for wildfire management through an assessment of current literature and two practical case
Learning quantum Hamiltonians at any temperature in polynomial time
We study the problem of learning a local quantum Hamiltonian $H$ given copies of its Gibbs state $ρ= e^{-βH}/\textrm{tr}(e^{-βH})$ at a known inverse temperature $β>0$. Anshu, Arunachalam, Kuwahara, and Soleimanifar (arXiv:2004.07266) gave an algorithm to learn a Hamiltonian on $n$ qubits to precision $ε$ with only polynomially many copies of the Gibbs state, but which takes exponential time. Obtaining a computati
Causal Unsupervised Semantic Segmentation
Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations. With the advent of self-supervised pre-training, various frameworks utilize the pre-trained features to train prediction heads for unsupervised dense prediction. However, a significant challenge in this unsupervised setup is determining the appropriate level of clustering required for segmenting concep
Active teacher selection for reward learning
Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes from a single human teacher, despite gathering feedback from large and heterogeneous populations. We propose the Hidden Utility Bandit (HUB) framework to model differences in teacher rationality, expertise, and costliness, formalizing the pr
Few studies have investigated the diagnostic utilities of biomarkers for predicting bacteremia among septic patients admitted to intensive care units (ICU). Therefore, this study evaluated the prediction power of laboratory biomarkers to utilize those markers with high performance to optimize the predictive model for bacteremia. This retrospective cross-sectional study was conducted at the ICU department of Gyeongsan
Structure learning of Hamiltonians from real-time evolution
We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply $e^{-\mathrm{i} Ht}$ for an unknown local Hamiltonian $H = \sum_{a = 1}^m λ_a E_a$ on $n$ qubits, the goal is to recover $H$. This problem is already well-understood under the assumption that the interaction terms, $E_a$, are given, and only the interaction strengths, $λ_a$, are unknown. But how efficiently can
Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitated the collection of multi-modal mobility data, offering valuable insights into daily activity patterns. Many studies have adopted advanced data-driven techniques to explore the potential of these multi-modal mobility data in land use infere
Annealing-based approach to solving partial differential equations
Solving partial differential equations (PDEs) using an annealing-based approach involves solving generalized eigenvalue problems. Discretizing a PDE yields a system of linear equations (SLE). Solving an SLE can be formulated as a general eigenvalue problem, which can be transformed into an optimization problem with an objective function given by a generalized Rayleigh quotient. The proposed algorithm requires iterati
Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms
Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms. Is providing LLMs with community rules enough for models to follow these norms? We evaluate LLMs' capacity to detect (Task 1) and correct (Task 2) biased Wikipedia edits according to Wikipedia's Neutral Point of View (NPOV) policy. LLMs struggled with bias detection, achieving only 64% accuracy on a
We introduce Proximal Policy Distillation (PPD), a novel policy distillation method that integrates student-driven distillation and Proximal Policy Optimization (PPO) to increase sample efficiency and to leverage the additional rewards that the student policy collects during distillation. To assess the efficacy of our method, we compare PPD with two common alternatives, student-distill and teacher-distill, over a wid
ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However, we observe that when adopting CLIP to such a pixel-level understanding task, unexpected bias (including class-preference bias and space-preference bias) occurs. Previous works don't explicitly model the bias, which largely constrains the segmentation performanc
Optimising MFCC parameters for the automatic detection of respiratory diseases
Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) is widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise
Inspired by the idea of Positive-incentive Noise (Pi-Noise or $π$-Noise) that aims at learning the reliable noise beneficial to tasks, we scientifically investigate the connection between contrastive learning and $π$-noise in this paper. By converting the contrastive loss to an auxiliary Gaussian distribution to quantitatively measure the difficulty of the specific contrastive model under the information theory frame
Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature
Physics seeks to uncover the laws of Nature and express them through mathematical equations. Despite the vast diversity of natural phenomena, physical equations exhibit structural regularities that set them apart from arbitrary mathematical expressions. While principles such as dimensional analysis have long guided the formulation of physical models, the exploration of more subtle statistical patterns within the equa
UNA: A Unified Supervised Framework for Efficient LLM Alignment Across Feedback Types
RL alignment methods, including RLHF and DPO, are primarily based on pairwise preference data. Although scalar or score-based feedback has been collected in some settings, it is rarely used directly, and preference magnitude information is typically ignored. Furthermore, current alignment frameworks offer limited capability for unifying heterogeneous supervision signals, making it difficult to jointly leverage divers
How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?
Message-passing graph neural networks (MPGNNs) are commonly compared with the Weisfeiler-Lehman (WL) color-refinement procedure, but this comparison does not quantify the resource parameters a network needs to realize color refinement with bounded-size messages and finite numerical precision. We study the cost of simulating a single color-refinement step on unattributed graphs. We distinguish input-independent, or ob
A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection
Graph neural networks (GNNs) are becoming increasingly popular for EEG-based depression detection. However, previous GNN-based methods fail to sufficiently consider the characteristics of depression, thus limiting their performance. Firstly, studies in neuroscience indicate that depression patients exhibit both common and individualized brain abnormal patterns. Previous GNN-based approaches typically focus either on
ChatSearch: a Dataset and a Generative Retrieval Model for General Conversational Image Retrieval
In this paper, we investigate the task of general conversational image retrieval on open-domain images. The objective is to search for images based on interactive conversations between humans and computers. To advance this task, we curate a dataset called ChatSearch. This dataset includes a multi-round multimodal conversational context query for each target image, thereby requiring the retrieval system to find the ac
Notable events recorded on this day and month across all years.