Record 04062026 · captured 2026-08-25
The world looked up Murder of Henry Nowak. 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.
On 3 December 2025, Henry Nowak, an 18‑year‑old university student, was murdered in Southampton, Hampshire, England, by 23‑year‑old Vickrum Singh Digwa. Police bodycam footage showing officers arresting Nowak as he lay dying from stab wounds sparked public out
Robert Lee "Peabo" Bryson was an American singer and songwriter. After collaborating with singers Luther Vandross and Cissy Houston on his debut album Peabo (1976), he signed to Capitol Records and released the 1978 albums Reaching for the Sky and Crosswinds,
Obsession is a 2025 American supernatural horror film written, directed, and edited by Curry Barker. The film follows Bear, a music store employee who buys a supernatural toy that grants his wish for his friend Nikki to fall in love with him, which makes her b
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
Stephen Glenn Charles Hilton is a British-born American conservative political commentator, former political adviser, and contributor for the Fox News Channel. He served as director of strategy for British prime minister David Cameron from 2010 to 2012. Hilton
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
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
Diana Maximovna Shnaider is a Russian professional tennis player. She has a career-high singles ranking by the WTA of No. 11, achieved on 5 May 2025, and a best doubles ranking of No. 8, reached on 16 June 2025.
Scott Cameron Pelley is an American author and broadcast journalist. Pelley is the author of the 2019 book, Truth Worth Telling, and a former correspondent for the CBS News magazine 60 Minutes. Pelley served as anchor and managing editor of the CBS Evening New
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
2026 California gubernatorial election
An election will be held in the U.S. state of California on November 3, 2026, to elect the next governor of California. The statewide top-two primary election was held on June 2, 2026, with Democrat Xavier Becerra advancing to the general election alongside Re
Bari Weiss is an American journalist and political commentator who has served since October 2025 as editor-in-chief of CBS News. She was an op-ed and book review editor at The Wall Street Journal from 2013 to 2017 and an op-ed staff editor and writer on cultur
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
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
Xavier Becerra is an American attorney and politician who served as the 25th United States secretary of health and human services under President Joe Biden from 2021 to 2025. A member of the Democratic Party, Becerra previously served as the 33rd attorney gene
.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
Masters of the Universe (2026 film)
Masters of the Universe is a 2026 American sword-and-sorcery film based on the media franchise by Mattel. It is the second live-action film adaptation, the 1987 film was the first. It was directed by Travis Knight and written by Chris Butler, Aaron Nee, Adam N
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
Anna's Archive is an open source search engine for shadow libraries that was launched by the pseudonymous Anna shortly after law enforcement efforts to shut down Z-Library in 2022. The site aggregates records from Z-Library, Sci-Hub, and Library Genesis (LibGe
Maja Ewa Chwalińska is a Polish professional tennis player. She has career-high WTA rankings of No. 21 in singles, achieved on 8 June 2026 and No. 91 in doubles, reached on 9 June 2025. Chwalińska's best result is reaching the final of the 2026 French Open, th
Aryna Siarhiejeŭna Sabalenka is a Belarusian professional tennis player. She is the current world No. 1 in women's singles by the WTA and is a former No. 1 in doubles. Sabalenka has won 24 career singles titles, including four majors—two each at the Australian
Nicholas Bilton is a British-American journalist, author, and filmmaker. He was named executive producer of 60 Minutes on 28 May 2026. He has also been a special correspondent at Vanity Fair, author of a number of New York Times-bestselling books, and a screen
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
The kirpan is a blade that Khalsa Sikhs are required to wear as part of their religious uniform, as prescribed by the Sikh Code of Conduct. Traditionally, the kirpan was a full-sized talwar at around 76 cm long (30 in); however, British colonial policies and l
Spider-Noir is an American superhero series developed by Oren Uziel for MGM+ and Prime Video. Based on Marvel Comics featuring the character Spider-Man Noir, the series follows an aging private investigator and superhero in 1930s New York City who grapples wit
Callum Robilliard Turner is a British actor. After working as a fashion model, he began working in film and television. He had lead roles in the drama film Queen and Country (2014) and the mystery drama series Glue (2014), and played Theseus, the brother of Ne
Victor Wembanyama, nicknamed "Wemby" and "the Alien", is a French professional basketball player for the San Antonio Spurs of the National Basketball Association (NBA). He was selected first overall by the Spurs in the 2023 NBA draft and is considered one of t
Begtse is a dharmapala and the lord of war in Tibetan Buddhism, originally a pre-Buddhist war god of the Mongols.
William John Pulte is an American businessman who has served as the director of the Federal Housing Finance Agency (FHFA) and the chairman of Fannie Mae and Freddie Mac since 2025. Pulte served as the acting director of national intelligence from June to Augus
Dua Lipa is an English singer and songwriter. Her accolades include seven Brit Awards and three Grammy Awards.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Predicting the Likely Behaviors of Continuous Nonlinear Systems in Equilibrium
This paper introduces a method for predicting the likely behaviors of continuous nonlinear systems in equilibrium in which the input values can vary. The method uses a parameterized equation model and a lower bound on the input joint density to bound the likelihood that some behavior will occur, such as a state variable being inside a given numeric range. Using a bound on the density instead of the density itself is
Recently hybrid evolutionary computation (EC) techniques are successfully implemented for solving large sets of linear equations. All the recently developed hybrid evolutionary algorithms, for solving linear equations, contain both the recombination and the mutation operations. In this paper, two modified hybrid evolutionary algorithms contained time-variant adaptive evolutionary technique are proposed for solving li
Estimation Procedures for Robust Sensor Control
Many robotic sensor estimation problems can characterized in terms of nonlinear measurement systems. These systems are contaminated with noise and may be underdetermined from a single observation. In order to get reliable estimation results, the system must choose views which result in an overdetermined system. This is the sensor control problem. Accurate and reliable sensor control requires an estimation procedure w
Application of Evidential Reasoning to Helicopter Flight Path Control
This paper presents a methodology for research and development of the inferencing and knowledge representation aspects of an Expert System approach for performing reasoning under uncertainty in support of a real time vehicle guidance and navigation system. Such a system could be of major benefit for non-terrain following low altitude flight systems operating in foreign hostile environments such as might be experience
Information and Multi-Sensor Coordination
The control and integration of distributed, multi-sensor perceptual systems is a complex and challenging problem. The observations or opinions of different sensors are often disparate incomparable and are usually only partial views. Sensor information is inherently uncertain and in addition the individual sensors may themselves be in error with respect to the system as a whole. The successful operation of a multi-sen
For small number of equations, systems of linear (and sometimes nonlinear) equations can be solved by simple classical techniques. However, for large number of systems of linear (or nonlinear) equations, solutions using classical method become arduous. On the other hand evolutionary algorithms have mostly been used to solve various optimization and learning problems. Recently, hybridization of evolutionary algorithm
Spectral Compressed Sensing via Structured Matrix Completion
The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples. Specifically, the object of interest with ambient dimension $n$ is assumed to be a mixture of $r$ complex multi-dimensional sinusoids, while the underlying frequencies can assume any value in the unit disk. Conventional compressed sensing paradigms suffer from the {\em basis mismatch} issue when imposing
Learning Geo-Temporal Non-Stationary Failure and Recovery of Power Distribution
Smart energy grid is an emerging area for new applications of machine learning in a non-stationary environment. Such a non-stationary environment emerges when large-scale failures occur at power distribution networks due to external disturbances such as hurricanes and severe storms. Power distribution networks lie at the edge of the grid, and are especially vulnerable to external disruptions. Quantifiable approaches
Robust Spectral Compressed Sensing via Structured Matrix Completion
The paper explores the problem of \emph{spectral compressed sensing}, which aims to recover a spectrally sparse signal from a small random subset of its $n$ time domain samples. The signal of interest is assumed to be a superposition of $r$ multi-dimensional complex sinusoids, while the underlying frequencies can assume any \emph{continuous} values in the normalized frequency domain. Conventional compressed sensing p
A least-squares method for sparse low rank approximation of multivariate functions
In this paper, we propose a low-rank approximation method based on discrete least-squares for the approximation of a multivariate function from random, noisy-free observations. Sparsity inducing regularization techniques are used within classical algorithms for low-rank approximation in order to exploit the possible sparsity of low-rank approximations. Sparse low-rank approximations are constructed with a robust upda
Quantile Regression for Large-scale Applications
Quantile regression is a method to estimate the quantiles of the conditional distribution of a response variable, and as such it permits a much more accurate portrayal of the relationship between the response variable and observed covariates than methods such as Least-squares or Least Absolute Deviations regression. It can be expressed as a linear program, and, with appropriate preprocessing, interior-point methods c
Tensor Decompositions: A New Concept in Brain Data Analysis?
Matrix factorizations and their extensions to tensor factorizations and decompositions have become prominent techniques for linear and multilinear blind source separation (BSS), especially multiway Independent Component Analysis (ICA), NonnegativeMatrix and Tensor Factorization (NMF/NTF), Smooth Component Analysis (SmoCA) and Sparse Component Analysis (SCA). Moreover, tensor decompositions have many other potential a
We solve the image denoising problem with a dictionary learning technique by writing a convex functional of a new form. This functional contains beside the usual sparsity inducing term and fidelity term, a new term which induces similarity between overlapping patches in the overlap regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing $L_{1}$ norm of th
Hybrid fuzzy logic and pid controller based ph neutralization pilot plant
Use of Control theory within process control industries has changed rapidly due to the increase complexity of instrumentation, real time requirements, minimization of operating costs and highly nonlinear characteristics of chemical process. Previously developed process control technologies which are mostly based on a single controller are not efficient in terms of signal transmission delays, processing power for comp
Owing to the edge preserving ability and low computational cost of the total variation (TV), variational models with the TV regularization have been widely investigated in the field of multiplicative noise removal. The key points of the successful application of these models lie in: the optimal selection of the regularization parameter which balances the data-fidelity term with the TV regularizer; the efficient algor
Conditions for Convergence in Regularized Machine Learning Objectives
Analysis of the convergence rates of modern convex optimization algorithms can be achived through binary means: analysis of emperical convergence, or analysis of theoretical convergence. These two pathways of capturing information diverge in efficacy when moving to the world of distributed computing, due to the introduction of non-intuitive, non-linear slowdowns associated with broadcasting, and in some cases, gather
Note on Evaluation of Hierarchical Modular Systems
This survey note describes a brief systemic view to approaches for evaluation of hierarchical composite (modular) systems. The list of considered issues involves the following: (i) basic assessment scales (quantitative scale, ordinal scale, multicriteria description, two kinds of poset-like scales), (ii) basic types of scale transformations problems, (iii) basic types of scale integration methods. Evaluation of the m
Towards Detection of Bottlenecks in Modular Systems
The paper describes some basic approaches to detection of bottlenecks in composite (modular) systems. The following basic system bottlenecks detection problems are examined: (1) traditional quality management approaches (Pareto chart based method, multicriteria analysis as selection of Pareto-efficient points, and/or multicriteria ranking), (2) selection of critical system elements (critical components/modules, criti
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
We study a probabilistic numerical method for the solution of both boundary and initial value problems that returns a joint Gaussian process posterior over the solution. Such methods have concrete value in the statistics on Riemannian manifolds, where non-analytic ordinary differential equations are involved in virtually all computations. The probabilistic formulation permits marginalising the uncertainty of the nume
We present the PyHST2 code which is in service at ESRF for phase-contrast and absorption tomography. This code has been engineered to sustain the high data flow typical of the third generation synchrotron facilities (10 terabytes per experiment) by adopting a distributed and pipelined architecture. The code implements, beside a default filtered backprojection reconstruction, iterative reconstruction techniques with a
Spectral Convergence of the connection Laplacian from random samples
Spectral methods that are based on eigenvectors and eigenvalues of discrete graph Laplacians, such as Diffusion Maps and Laplacian Eigenmaps are often used for manifold learning and non-linear dimensionality reduction. It was previously shown by Belkin and Niyogi \cite{belkin_niyogi:2007} that the eigenvectors and eigenvalues of the graph Laplacian converge to the eigenfunctions and eigenvalues of the Laplace-Beltram
Fast greedy algorithm for subspace clustering from corrupted and incomplete data
We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm providing an efficient method for clustering data belonging to a few low-dimensional linear or affine subspaces. The main difference of our algorithm from predecessors is its ability to work with noisy data having a high rate of erasures (missed entries with the known coordinates) and errors (corrupted entries with unknown coordinates). We discu
Large Margin Low Rank Tensor Analysis
Other than vector representations, the direct objects of human cognition are generally high-order tensors, such as 2D images and 3D textures. From this fact, two interesting questions naturally arise: How does the human brain represent these tensor perceptions in a "manifold" way, and how can they be recognized on the "manifold"? In this paper, we present a supervised model to learn the intrinsic stru
Precisely Verifying the Null Space Conditions in Compressed Sensing: A Sandwiching Algorithm
In this paper, we propose new efficient algorithms to verify the null space condition in compressed sensing (CS). Given an $(n-m) \times n$ ($m>0$) CS matrix $A$ and a positive $k$, we are interested in computing $\displaystyle α_k = \max_{\{z: Az=0,z\neq 0\}}\max_{\{K: |K|\leq k\}}$ ${\|z_K \|_{1}}{\|z\|_{1}}$, where $K$ represents subsets of $\{1,2,...,n\}$, and $|K|$ is the cardinality of $K$. In particular, we
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference \emph{and learning} (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture
Notable events recorded on this day and month across all years.