Record 24022026 · captured 2026-08-25
The world looked up El Mencho. 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.
Nemesio Rubén Oseguera Cervantes, commonly referred to by his alias "El Mencho", was a Mexican drug lord and head of the Jalisco New Generation Cartel (CJNG), an organized crime group based in Jalisco. He was the most wanted person in Mexico and one of the mos
John Michael Gaudreau was an American professional ice hockey player. A left winger, he played 11 seasons in the National Hockey League (NHL). He played college ice hockey for the Boston College Eagles in NCAA Division I for three seasons beginning in 2011 and
Linus Williams Ifejirika, popularly known as Blord, is a Nigerian cryptocurrency entrepreneur and the founder of Blord Group. His business interests span fintech, digital payments, and real estate, with companies such as Blord Real Estate Ltd., Blord Jetpay Lt
Jack Rowden Hughes is an American professional ice hockey player who is a center and alternate captain for the New Jersey Devils of the National Hockey League (NHL). A product of the U.S. National Development Team, Hughes was drafted first overall by the Devil
Alysa Liu is an American figure skater. She is the 2026 Olympic champion in both the women's singles and team events, the 2025 World champion, the 2022 World bronze medalist, the 2025–26 Grand Prix Final champion, a two-time Grand Prix medalist, a four-time Ch
The Jalisco New Generation Cartel, also known as CJNG, is a Mexican criminal syndicate based in Jalisco founded and headed by Nemesio Oseguera Cervantes, commonly known as El Mencho, until he was killed by the Mexican Army in 2026. The cartel has been characte
Robert Michael Aramayo is an English actor. From 2016 to 2017, he played the role of young Eddard Stark in the sixth and seventh seasons of the HBO series Game of Thrones. In 2021, he starred in the Netflix psychological thriller miniseries Behind Her Eyes. Si
A Knight of the Seven Kingdoms (TV series)
A Knight of the Seven Kingdoms is an American fantasy drama television series created by Ira Parker and George R. R. Martin. A prequel to Game of Thrones (2011–2019), it is the third television series in Martin's A Song of Ice and Fire franchise and is an adap
Eileen Feng Gu, also known by her Chinese name Gu Ailing (谷爱凌), is a Chinese-American freestyle skier and model. She has represented China in halfpipe, slopestyle, and big air events since the 2018–19 season. With three gold and three silver medals, Gu is the
Eric William Dane was an American actor. After multiple television roles in the 1990s and 2000s, including his recurring role as Jason Dean on Charmed, he was cast as Dr. Mark Sloan on the ABC medical drama Grey's Anatomy. He went on to appear in films such as
On 22 February 2026, the Mexican Armed Forces conducted an operation which killed El Mencho, the leader of the Jalisco New Generation Cartel (CJNG), and six others in Tapalpa, Jalisco, Mexico. The operation sparked clashes in the area, resulting in shootouts,
I Swear is a 2025 British biographical drama film directed, written, and produced by Kirk Jones. It is based on the true life story of John Davidson, a Scottish man with severe Tourette syndrome who, as a teenager, was the subject of the 1989 television docume
Rosalinda González Valencia is a Mexican businesswoman and convicted money launderer of the Jalisco New Generation Cartel (CJNG), a criminal group based in Jalisco. She has also been known by her alias "La Jefa". She was married to El Mencho, once one of Mexic
John Craig Davidson is a Scottish activist and campaigner for Tourette syndrome. At age 16, Davidson was the subject of the BBC television documentary John's Not Mad (1989) about the manifestations of his Tourette's. He has also appeared in several other BBC d
Connor Charles Hellebuyck is an American professional ice hockey player who is a goaltender for the Winnipeg Jets of the National Hockey League (NHL).
Rubén Oseguera González, commonly referred to by his alias El Menchito, is a Mexican-American convicted drug lord and former high-ranking member of the Jalisco New Generation Cartel (CJNG), a criminal group based in Jalisco. He is the son of Nemesio Oseguera C
Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. Over several decades, he made much of his fortune providing ta
Rondale DaSean Moore was an American professional football player who was a wide receiver in the National Football League (NFL). He played college football for the Purdue Boilermakers, earning consensus All-American honors as a freshman. Moore was selected by
Jane Dawn Elizabeth Andrews was a royal dresser for Sarah, Duchess of York. Andrews was imprisoned in 2001 for murdering her lover, and released from prison in 2019.
The 2026 Winter Olympics, officially the XXV Olympic Winter Games and commonly known as Milano Cortina 2026, were an international winter multi-sport event held from 6 to 22 February 2026, at multiple sites across Lombardy, Veneto and Trentino-Alto Adige/Südti
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
Quintin Jerome Hughes is an American professional ice hockey player who is a defenseman for the Minnesota Wild of the National Hockey League (NHL). Hughes was drafted seventh overall by the Vancouver Canucks in the 2018 NHL entry draft and played his first sev
John Fitzgerald Kennedy Jr., also referred to as JFK Jr., was an American businessman, attorney, magazine publisher, and journalist. He was the son of the 35th U.S. president John F. Kennedy, and First Lady Jacqueline Kennedy.
Carolyn Jeanne Bessette-Kennedy was an American fashion publicist. Raised in Greenwich, Connecticut, she graduated from Boston University and joined Calvin Klein, where she rose from a sales position in Boston to publicity and show-production roles in New York
Peter Benjamin Mandelson, Baron Mandelson, is a British former Labour Party politician, lobbyist and diplomat. He was the Member of Parliament (MP) for Hartlepool from 1992 to 2004. He served in Tony Blair and Gordon Brown's cabinets as Minister without portfo
79th British Academy Film Awards
The 79th British Academy Film Awards, more commonly known as the BAFTAs, were held on 22 February 2026, honouring the best national and foreign films of 2025, at the Royal Festival Hall within London's Southbank Centre. Presented by the British Academy of Film
The Sinaloa Cartel is a large and powerful drug trafficking transnational organized crime syndicate based in Culiacán, Sinaloa, Mexico, that specializes in illegal drug trafficking, money laundering, and murder. It has been Mexico’s most dominant drug cartel s
List of Olympic medalists in ice hockey
Ice hockey is a sport that is contested at the Winter Olympic Games. A men's ice hockey tournament has been held every Winter Olympics ; an ice hockey tournament was also held at the 1920 Summer Olympics. From 1920 to 1968, the Olympics also acted as the Ice H
Joaquín Archivaldo Guzmán Loera, commonly known as "El Chapo", is a Mexican former drug lord and the former top leader of the Sinaloa Cartel. Guzmán is believed to be responsible for the deaths of over 34,000 people, and was considered to be the most powerful
Rebecca Gayheart is an American actress and model. Gayheart began her career as a teen model in the 1980s before becoming an advertising spokeswoman. She has been active as an actress since 1990.
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Joint auto-encoders: a flexible multi-task learning framework
The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a wa
An Elo-based rating system for TopCoder SRM
This paper presents an Elo-based rating system for programming contests, specifically Topcoder's Single Round Matches (SRMs). We introduce a logarithmic rank-based performance metric that allows single-round, multi-player contest results to be incorporated into an Elo-style continuous rating framework. Model parameters and adjustment factors are calibrated empirically by minimizing absolute prediction error over
Deep Learning: Our Miraculous Year 1990-1991
The Deep Learning Artificial Neural Networks (NNs) of our team have revolutionised Machine Learning & AI. Many of the basic ideas behind this revolution were published within the 12 months of our "Annus Mirabilis" 1990-1991 at our lab in TU Munich. Back then, few people were interested. But a quarter century later, NNs based on our "Miraculous Year" were on over 3 billion devices, and used many bi
Recent years have seen various rumor diffusion models being assumed in detection of rumor source research of the online social network. Diffusion model is arguably considered as a very important and challengeable factor for source detection in networks but it is less studied. This paper provides an overview of three representative schemes of Independent Cascade-based, Epidemic-based, and Learning-based to model the p
Effective Universal Unrestricted Adversarial Attacks using a MOE Approach
Recent studies have shown that Deep Leaning models are susceptible to adversarial examples, which are data, in general images, intentionally modified to fool a machine learning classifier. In this paper, we present a multi-objective nested evolutionary algorithm to generate universal unrestricted adversarial examples in a black-box scenario. The unrestricted attacks are performed through the application of well-known
Information-Theoretic Limits of Quantum Learning via Data Compression
Understanding the power of quantum data in machine learning is central to many proposed applications of quantum technologies. While access to quantum data can offer exponential advantages for carefully designed learning tasks and often under strong assumptions on the data distribution, it remains an open question whether such advantages persist in less structured settings and under more realistic, naturally occurring
Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
Multi-robot decision-making is the process where multiple robots coordinate actions. In this paper, we aim for efficient and effective multi-robot decision-making despite the robots' limited on-board resources and the often resource-demanding complexity of their tasks. We introduce the first algorithm enabling the robots to choose with which few other robots to coordinate and provably balance the trade-off of cen
Towards Unifying Perceptual Reasoning and Logical Reasoning
An increasing number of scientific experiments support the view of perception as Bayesian inference, which is rooted in Helmholtz's view of perception as unconscious inference. Recent study of logic presents a view of logical reasoning as Bayesian inference. In this paper, we give a simple probabilistic model that is applicable to both perceptual reasoning and logical reasoning. We show that the model unifies the
Face Pyramid Vision Transformer
A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a discriminative multi-scale facial representations for face recognition and verification. In FPVT, Face Spatial Reduction Attention (FSRA) and Dimensionality Reduction (FDR) layers are employed to make the feature maps compact, thus reducing the computations. An Improved Patch Embedding (IPE) algorithm is proposed to exploit the benefits of CNNs in
Learning to See the Elephant in the Room: Self-Supervised Context Reasoning in Humans and AI
Humans rarely perceive objects in isolation but interpret scenes through relationships among co-occurring elements. How such contextual knowledge is acquired without explicit supervision remains unclear. Here we combine human psychophysics experiments with computational modelling to study the emergence of contextual reasoning. Participants were exposed to novel objects embedded in naturalistic scenes that followed pr
Exploring Singularities in point clouds with the graph Laplacian: An explicit approach
We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of datasets. Our theory provides theoretical guarantees and explicit bounds on the functional forms of the graph Laplacian when it acts on functions defined close to singularities of the underlying manifold. We use these explicit bounds to develop tests for singularities and propose methods that can be used t
A Simple Generative Model of Logical Reasoning and Statistical Learning
Statistical learning and logical reasoning are two major fields of AI expected to be unified for human-like machine intelligence. Most existing work considers how to combine existing logical and statistical systems. However, there is no theory of inference so far explaining how basic approaches to statistical learning and logical reasoning stem from a common principle. Inspired by the fact that much empirical work in
Robust low-rank training via approximate orthonormal constraints
With the growth of model and data sizes, a broad effort has been made to design pruning techniques that reduce the resource demand of deep learning pipelines, while retaining model performance. In order to reduce both inference and training costs, a prominent line of work uses low-rank matrix factorizations to represent the network weights. Although able to retain accuracy, we observe that low-rank methods tend to co
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments. For example, transparent materials frequently elude detection by depth sensors; surfaces may introduce measurement inaccuracies due to their polished textures, extended distances, and oblique incidence angles from the sensor. The presence of incomplete depth maps i
A simple connection from loss flatness to compressed neural representations
Despite extensive study, the significance of sharpness -- the trace of the loss Hessian at local minima -- remains unclear. We investigate an alternative perspective: how sharpness relates to the geometric structure of neural representations, specifically representation compression, defined as how strongly neural activations concentrate under local input perturbations. We introduce three measures -- Local Volumetric
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
Previous research on behavior-based attack detection for networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited and often not demonstrated. This paper presents IoTGeM, an approach for modeling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance. We first introduce an improved rolling window approach for feat
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collection process in the real world. Current imitation learning (IL) typically discards imperfect data, focusing solely on successful expert data. While reinforcement learning (RL) can learn from explorations and failures, the sim2real gap and it
Inference of Abstraction for a Unified Account of Symbolic Reasoning from Data
Inspired by empirical work in neuroscience for Bayesian approaches to brain function, we give a unified probabilistic account of various types of symbolic reasoning from data. We characterise them in terms of formal logic using the classical consequence relation, an empirical consequence relation, maximal consistent sets, maximal possible sets and maximum likelihood estimation. The theory gives new insights into reas
Stochastic Localization via Iterative Posterior Sampling
Building upon score-based learning, new interest in stochastic localization techniques has recently emerged. In these models, one seeks to noise a sample from the data distribution through a stochastic process, called observation process, and progressively learns a denoiser associated to this dynamics. Apart from specific applications, the use of stochastic localization for the problem of sampling from an unnormalize
Calibrating Large Language Models with Sample Consistency
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application. However, LLMs are often uncalibrated inherently and elude conventional calibration techniques due to their proprietary nature and massive scale. In this work, we explore the potential of deriving confidence from the distribution of multiple randomly sampled model generations, via three m
PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus
The Potsdam Textbook Corpus (PoTeC) is a naturalistic eye-tracking-while-reading corpus containing data from 75 participants reading 12 scientific texts. PoTeC is the first naturalistic eye-tracking-while-reading corpus that contains eye-movements from domain-experts as well as novices in a within-participant manipulation: It is based on a 2x2x2 fully-crossed factorial design which includes the participants' leve
Multi-agent reinforcement learning (MARL) for cyber-physical vehicle systems usually requires a significantly long training time due to their inherent complexity. Furthermore, deploying the trained policies in the real world demands a feature-rich environment along with multiple physical embodied agents, which may not be feasible due to monetary, physical, energy, or safety constraints. This work seeks to address the
Quantformer: from attention to profit with a quantitative transformer trading strategy
In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Fully capturing various market variables, including long-term information, as well as essential signals that may lead to profit remains a difficult task for learning algorithms. In order to tackle this challenge, this paper introduces quantformer, an enhanced neural network architectu
Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response
In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propos
Visual Question Answering (VQA) is a challenging task that requires the joint understanding of natural language and visual content. While early research primarily focused on recognizing objects and scene context, it often overlooked scene text-an essential source of explicit semantic information. This paper introduces \textbf{ViTextVQA} (\textbf{Vi}etnamese \textbf{Text}-based \textbf{V}isual \textbf{Q}uestion \textb
Notable events recorded on this day and month across all years.