Record 06052026 · 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
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
2026 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. The election saw the defeat
Cinco de Mayo is an annual celebration held on May 5 to celebrate Mexico's victory over the Second French Empire at the Battle of Puebla in 1862, led by General Ignacio Zaragoza. Zaragoza died months after the battle from an illness, however, and a larger Fren
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
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
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
Mamata Banerjee is an Indian politician and lawyer who served as the eighth chief minister of West Bengal from 2011 to 2026. She was the first and only woman to hold that office. Being the founder and president of the All India Trinamool Congress (AITC), she p
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
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
2026 Kerala Legislative Assembly election
2026 Kerala Legislative Assembly Elections were conducted in Kerala on 9 April 2026 to elect 140 members of the Kerala Legislative Assembly. The votes were counted and the results were declared on 4 May 2026, leading to Satheesan ministry.
The Met Gala, formally known as the Costume Institute Benefit, is the annual haute couture fundraising festival held at and for the benefit of the Metropolitan Museum of Art's Costume Institute on the Museum Mile of Fifth Avenue in Manhattan. The Met Gala is p
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
Tamil Nadu Legislative Assembly
The Tamil Nadu Legislative Assembly is the unicameral legislature of the Indian state of Tamil Nadu. It has a strength of 234 members, all of whom are democratically elected using the first-past-the-post system. The presiding officer of the assembly is the Spe
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
Randall Darius Jackson is an American record executive, television presenter and musician, best known as a judge on American Idol from 2002 to 2013.
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
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
Ratanlal Bhagatram Choudary was an Indian film producer who worked in several film industries, predominantly in Tamil and Telugu along with a few films in Malayalam and Hindi. He was the founder of the production company Super Good Films.
Transmission voie-machine is a form of in-cab signalling originally deployed in France and is mainly used on high-speed railway lines. TVM-300 was the first version, followed by TVM-430.
Muthuvel Karunanidhi Stalin is an Indian politician and statesman who served as the eighth chief minister of Tamil Nadu from 2021 to 2026. He became president of the Dravida Munnetra Kazhagam (DMK) on 28 August 2018, after serving as the party's working presid
Suvendu Adhikari is an Indian politician who is serving as the 9th Chief Minister of West Bengal since 9 May 2026. He is the first chief minister of West Bengal belonging to the Bharatiya Janata Party (BJP).
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
Marshall Bruce Mathers III, known professionally as Eminem, is an American rapper, songwriter, record producer, and record executive. Known for his rap flow and conscious rap, which includes political criticism and social commentary, he is widely regarded as o
Dame Anna Wintour is a British and American media executive who served as editor-in-chief of Vogue from 1988 to 2025. Currently, Wintour serves as global chief content officer and artist director at Condé Nast. Known for her trademark pageboy bob haircut and d
International Boxing Union (since 1996)
The International Boxing Union (IBU) is a professional boxing sanctioning body founded in Atlanta, Georgia, United States, in 1996. It is unrelated to the International Boxing Union, based in Europe, which operated until the Second World War.
Connor Storrie is an American actor. He is best known for his breakout role as Ilya Rozanov in the sports romance series Heated Rivalry (2025–present). He hosted an episode of Saturday Night Live in 2026, for which he received a Primetime Emmy Award nomination
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
Louise Arbour is a Canadian jurist who has served as the 31st governor general of Canada since 2026.
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innovative and ground-breaking applications can be considered as a boon, at the same time they raise significant privacy concerns. In fact, recent GDPR (General Data Protection Regulation) legislation has highlighted and become an incentive for
Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing with SE problem are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December 2019. It has already caused thousands of causalities and infected several millions worldwide. Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance. The main tools practitioners currently use to diagnose Covid-19 are Reverse Tra
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection ar
We propose an efficient algorithm for learning mappings between two metric spaces, $\X$ and $\Y$. Our procedure is strongly Bayes-consistent whenever $\X$ and $\Y$ are topologically separable and $\Y$ is "bounded in expectation" (our term; the separability assumption can be somewhat weakened). At this level of generality, ours is the first such learnability result for unbounded loss in the agnostic setting. O
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the c
Finding Minimum-Cost Explanations for Predictions made by Tree Ensembles
The ability to explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations. In this paper, we aim at finding explanations for predictions made by tree ensembles that are not only minimal, but also mini
Improved Active Fire Detection using Operational U-Nets
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can cause quickly spreading fires and make them harder to control; therefore, early detection and accurate locating of active fires are crucial in environmental monitoring. Using satellite imagery to monitor and detect active fires has been crit
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spectral information from recent HSI sensors, analyzing the acquired images is challenging using traditional Machine Learning (ML) methods. As the number of frequency bands increases, the required number o
Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
Detection Transformers (DETR) have recently set new benchmarks in object detection. However, their performance in detecting rotated objects lags behind established oriented object detectors. Our analysis identifies a key observation: the boundary discontinuity and square-like problem in bipartite matching poses an issue with assigning appropriate ground truths to predictions, leading to duplicate low-confidence predi
Can Blockchains Reliably Train Machine Learning Models?
Large proof of work (PoW) networks allow anyone to earn rewards by running computation-intensive hash puzzles for profit, yet they typically consume electricity comparable to that of medium-sized countries. Repurposing computing resources from hash puzzles to machine learning training can benefit the energy sector as a whole, since this computing power is no longer wasted on solving hash puzzles but is instead used t
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms for deep artificial and spiking neural networks are unable to adequately auto-regulate the limited resources in the network, which leads to performance drop along with energy consumption rise as the increase of tasks. In this paper, we prop
Recent Advances in Generative AI for Healthcare Applications
The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentat
A Scoping Review of Deep Learning Methods for Photoplethysmography Data
Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information, with broad deployment in both clinical monitoring systems and wearable devices. In recent years, the integration of deep learning has substantially advanced PPG signal analysis and expanded its applications across healthcare and non-healthcare domains. Methods: We conducted a comprehensiv
Dropout Concrete Autoencoder for Band Selection on HSI Scenes
Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies. However, the existing deep learning-based methods either need additional post-processing strategies to select the descriptive bands or optimize the model indirectly, due to the parameterization inability of discrete variables for the selection
Improved Hardness Results for Learning Intersections of Halfspaces
We show strong (and surprisingly simple) lower bounds for weakly learning intersections of halfspaces in the improper setting. Strikingly little is known about this problem. For instance, it is not even known if there is a polynomial-time algorithm for learning the intersection of only two halfspaces. On the other hand, lower bounds based on well-established assumptions (such as approximating worst-case lattice probl
Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current approaches for tabular data often fail to preserve feature correlations and distributions during training, struggle with multi-metric hyperparameter selection, and lack comprehensive evaluation protocols. We address this gap with a unified
Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
Due to the increasing share of renewables, the analysis of the dynamical behavior of power grids gains importance. Effective risk assessments necessitate the analysis of large number of fault scenarios. The computational costs inherent in dynamic simulations impose constraints on the number of configurations that can be analyzed. Machine Learning (ML) has proven to efficiently predict complex power grid properties. H
psifx -- Psychological and Social Interactions Feature Extraction Package
psifx is a plug-and-play multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning techniques for human sciences research. It is motivated by a need (a) to automate and standardize data annotation processes that typically require expensive, lengthy, and inconsistent human labour; (b) to develop and distribute open-source community-driven psychology resea
The symmetry of dynamical systems can be exploited for state-transition prediction and to facilitate control policy optimization. This paper leverages system symmetry to develop sample-efficient offline reinforcement learning (RL) approaches. Under the symmetry assumption for a Markov Decision Process (MDP), a symmetric data augmentation method is proposed. The augmented samples are integrated into the dataset of Dee
Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions
The geometry of a graph is encoded in dynamical processes on the graph. Many graph neural network (GNN) architectures are inspired by such dynamical systems, typically based on the graph Laplacian. Here, we introduce Dirac--Bianconi GNNs (DBGNNs), which are based on the topological Dirac equation recently proposed by Bianconi. Based on the graph Laplacian, we demonstrate that DBGNNs explore the geometry of the graph
Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise
The Sparse Vector Technique (SVT) is one of the most fundamental tools in differential privacy (DP). It works as a backbone for adaptive data analysis by answering a sequence of queries on a given dataset, and gleaning useful information in a privacy-preserving manner. Unlike the typical private query releases that directly publicize the noisy query results, SVT is less informative -- it keeps the noisy query results
The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence
Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet researchers, policymakers, and technology companies lack shared terminology for discussing AI risks. Consider "privacy": one framework uses this term to describe a model's ability to leak sensitive training data, while another uses it to mean freedom from government surv
Power-Softmax: Towards Secure LLM Inference over Encrypted Data
Modern cryptographic methods for implementing privacy-preserving LLMs such as \gls{HE} require the LLMs to have a polynomial form. Forming such a representation is challenging because transformers include non-polynomial components, such as \Softmax and layer normalization. Previous approaches have either directly approximated pre-trained models with large-degree polynomials, which are less efficient over HE, or repla
On Verbalized Confidence Scores for LLMs
The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness. Uncertainty quantification for LLMs can establish more human trust into their responses, but also allows LLM agents to make more informed decisions based on each other's uncertainty. To estimate the uncertainty in a response, internal token logits, task-spec
Notable events recorded on this day and month across all years.