Record 05052026 · captured 2026-08-25
The world looked up 2026 West Bengal Legislative Assembly election. 30 tracked subjects, 25 discussions, 25 papers. This record is frozen and will not change.
Complete record JSON
What the most people looked up, ranked by Wikipedia pageviews for that day.
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
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
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
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 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
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.
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
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
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
2021 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal, to elect all 294 members of West Bengal Legislative Assembly. This electoral process of 292 seats unfolded between 27 March to 29 April 2021, taking place in eight phases. Voting for the two remaining co
United Democratic Front (Kerala)
The United Democratic Front (UDF) is the Indian National Congress–led alliance of political parties in the Indian state of Kerala. It is one of the two major political alliances in Kerala, the other being Communist Party of India (Marxist)–led Left Democratic
2026 Assam Legislative Assembly election
The 2026 Assam Legislative Assembly elections were held in Assam on 9 April 2026 to elect 126 members to the Assam Legislative Assembly. Votes were counted and the results were declared on 4 May 2026 by the Election Commission of India. The election results we
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).
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
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
The 2026 elections in India were held from April to May to include the elections of the Rajya Sabha, 4 states and 1 union territory legislative assemblies, several by-elections and several local body elections.
Bhabanipur, West Bengal Assembly constituency
Bhabanipur Assembly constituency is a Legislative Assembly constituency of Kolkata district in the Indian state of West Bengal. it's the seat of current chief minister.
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
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
2021 Tamil Nadu Legislative Assembly election
Tamil Nadu legislative assembly election was held on 6 April 2020 to elect the representatives of the 16th Tamil Nadu assembly. Elections were held for all the 234 constituencies in the assembly. The Election Commission of India announced the schedule for the
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
Rudolph William Louis Giuliani is an American politician and disbarred lawyer who served as the 108th mayor of New York City from 1994 to 2001. He previously served as the U.S. Associate Attorney General from 1981 to 1983 and the U.S. Attorney for the Southern
2021 Kerala Legislative Assembly election
The 2021 Kerala Legislative Assembly election was held in Kerala on 6 April 2021 to elect 140 members to the 15th Kerala Assembly. The results were declared on 2 May.
West Bengal Legislative Assembly
The West Bengal Legislative Assembly is the unicameral legislature of the Indian state of West Bengal. It is located in the B. B. D. Bagh area of Kolkata, the capital of the state. Members of the Legislative assembly are directly elected by the people. The leg
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
The Chief Minister of West Bengal is the de facto head of the executive branch of the Government of West Bengal, the subnational authority of the Indian state of West Bengal. The chief minister is the head of the Council of Ministers and advises the governor o
John Sterling was an American sportscaster, best known as the radio play-by-play announcer of the New York Yankees of Major League Baseball (MLB) from 1989 to 2024. Sterling called 5,060 consecutive Yankees games from 1989 to 2019.
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
Senapathy Albert Chandrasekaran,, popularly known as S. A. Chandrasekhar, is an Indian film director, producer, screenwriter and actor who primarily works in Tamil cinema along with some Telugu, Kannada and Hindi films. He made his directorial debut with Aval
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Neural Network Training with Approximate Logarithmic Computations
The high computational complexity associated with training deep neural networks limits online and real-time training on edge devices. This paper proposed an end-to-end training and inference scheme that eliminates multiplications by approximate operations in the log-domain which has the potential to significantly reduce implementation complexity. We implement the entire training procedure in the log-domain, with fixe
Efficient Parameter Estimation of Truncated Boolean Product Distributions
We study the problem of estimating the parameters of a Boolean product distribution in $d$ dimensions, when the samples are truncated by a set $S \subset \{0, 1\}^d$ accessible through a membership oracle. This is the first time that the computational and statistical complexity of learning from truncated samples is considered in a discrete setting. We introduce a natural notion of fatness of the truncation set $S$, u
Online Graph Topology Learning from Matrix-valued Time Series
The focus is on the statistical analysis of matrix-valued time series, where data is collected over a network of sensors, typically at spatial locations, over time. Each sensor records a vector of features at each time point, creating a vectorial time series for each sensor. The goal is to identify the dependency structure among these sensors and represent it with a graph. When only one feature per sensor is observed
Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or biologist expertise), understand sophisticated chemical or biological process mechanisms, and provide heuristic ideas for structure optimization in pharmaceutical industry. For this purpose, we construct a multi-layer self-attention based Graph Neural Network framework, namely L
Wasserstein multivariate auto-regressive models for modeling distributional time series
This paper is focused on the statistical analysis of data consisting of a collection of multiple series of probability measures that are indexed by distinct time instants and supported over a bounded interval of the real line. By modeling these time-dependent probability measures as random objects in the Wasserstein space, we propose a new auto-regressive model for the statistical analysis of multivariate distributio
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found that a kind of imitation learning approach, adversarial imitation learning (AIL), can have exceptional performance. With as little as only one expert trajectory, AIL can match the expert performance even in a long horizon, on tasks such as locomotion control. There are two m
Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant reso
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching
The matching of 3D shapes has been extensively studied for shapes represented as surface meshes, as well as for shapes represented as point clouds. While point clouds are a common representation of raw real-world 3D data (e.g. from laser scanners), meshes encode rich and expressive topological information, but their creation typically requires some form of (often manual) curation. In turn, methods that purely rely on
Unsupervised Learning of Robust Spectral Shape Matching
We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous deep functional map methods mainly focus on predicting optimised functional maps alone, and then rely on off-the-shelf post-processing to obtain accurate point-wise maps during inference. However, this two-stage procedure for obtaining point-w
Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Bandits
We study fair multi-agent multi-armed bandit learning under collision-only coordination. Agents cannot communicate explicitly during learning and observe only their own rewards and whether collisions occur when several agents access the same arm. The goal is to learn a max-min fair allocation while keeping each agent's reward samples and empirical reward estimates local. We propose a fully distributed algorithm f
Large language models (LLMs) have recently gained popularity. However, the impact of their general availability through ChatGPT on sensitive areas of everyday life, such as education, remains unclear. Nevertheless, the societal impact on established educational methods is already being experienced by both students and educators. Our work focuses on higher physics education and examines problem solving strategies. In
Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-spatial properties, synthetic aperture radar (SAR) to provide structural information about the Earth's surface, and light detection and ranging (LiDAR) to cover altitude information about ground elevation. Therefore, a natural idea is to combine multimodal images for r
Using Analytics on Student Created Data to Content Validate Pedagogical Tools
Conceptual and simulation models can function as useful pedagogical tools, however it is important to categorize different outcomes when evaluating them in order to more meaningfully interpret results. VERA is a ecology-based conceptual modeling software that enables users to simulate interactions between biotics and abiotics in an ecosystem, allowing users to form and then verify hypothesis through observing a time
CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets
Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where identifying rare but critical anomalies is of paramount importance. Traditional generative models often decouple data synthesis from classification,leading to a distribution mismatch that limits their practical benefit. To address these shortcomings, we introduce Causal Cooperative Networks (CCNET
Facial expression recognition has gained significance as a means of imparting social robots with the capacity to discern the emotional states of users. The use of social robotics includes a variety of settings, including homes, nursing homes or daycare centers, serving to a wide range of users. Remarkable performance has been achieved by deep learning approaches, however, its direct use for recognizing facial express
BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion
Witnessing the evolution of text-to-image diffusion models, significant strides have been made in text-to-3D generation. Currently, two primary paradigms dominate the field of text-to-3D: the feed-forward generation solutions, capable of swiftly producing 3D assets but often yielding coarse results, and the Score Distillation Sampling (SDS) based solutions, known for generating high-fidelity 3D assets albeit at a slo
Analyzing Adversarial Inputs in Deep Reinforcement Learning
In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However, even the state-of-the-art DRL models have been shown to suffer from reliability concerns -- for example, their susceptibility to adversarial inputs, i.e., small and abundant input perturbations that can fool the models into making unpredict
Proof-of-Learning with Incentive Security
Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance. However, the substantial energy expenditure stemming from computationally intensive yet meaningless tasks has raised considerable concerns surrounding traditional PoW approaches, The PoS mechanism, while free of energy consumption, is subject to security and
PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference
This paper presents PipeFusion, an innovative parallel methodology to tackle the high latency issues associated with generating high-resolution images using diffusion transformers (DiTs) models. PipeFusion partitions images into patches and the model layers across multiple GPUs. It employs a patch-level pipeline parallel strategy to orchestrate communication and computation efficiently. By capitalizing on the high si
Deep Time Series Models: A Comprehensive Survey and Benchmark
Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios. Unlike other data modalities, time series present unique challenges in learning and modeling due to their intricate and dynamic nature, including the entanglement of nonlinear patterns and time-variant trends. Recent years have witnessed remarkable breakthroughs in time series analysis,
xAI-Drop: Don't Use What You Cannot Explain
Graph Neural Networks (GNNs) have emerged as the predominant paradigm for learning from graph-structured data, offering a wide range of applications from social network analysis to bioinformatics. Despite their versatility, GNNs face challenges such as lack of generalization and poor interpretability, which hinder their wider adoption and reliability in critical applications. Dropping has emerged as an effective para
Towards Agentic Runtime Healing
Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human intervention. Traditional approaches rely on predefined heuristic rules, such as reusing error handlers or rolling back to checkpoints, but these methods struggle to adapt to the diverse range of runtime errors. The emergence of Large Language Models offers a new opportunity to ad
Several forms for constructing novel physics-informed neural-networks (PINN) for the solution of partial-differential-algebraic equations based on derivative operator splitting are proposed, using the nonlinear Kirchhoff rod as a prototype for demonstration. The open-source DeepXDE is likely the most well documented framework with many examples. Yet, we encountered some pathological problems and proposed novel method
Code-switching in text and speech challenges information-theoretic speaker design
In this work, we use language modeling to investigate the factors that influence insertional code-switching. Code-switching occurs when a speaker alternates between one language variety (the primary language) and another (the secondary language), and is widely observed in multilingual contexts. Recent work has shown that code-switching is often correlated with areas of low predictability in the primary language, but
GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
Although data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML benchmarks for node property prediction only cover a surprisingly narrow set of data domains, and graph neural networks (GNNs) are often evaluated on just a few academic citation networks. This issue is particularly pressing in light of the recent growing interest in designing
Notable events recorded on this day and month across all years.