Record 09072026 · captured 2026-08-25
The world looked up Arthur Fery. 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.
Arthur Fery is a British professional tennis player. He has a career-high ATP singles ranking of world No. 36 achieved on 13 July 2026 and a doubles ranking of No. 201 achieved on 29 July 2024. His breakthrough came at the 2026 Wimbledon Championships, reachin
Erling Braut Haaland is a Norwegian professional footballer who plays as a striker for Premier League club Manchester City and the Norway national team. Regarded as one of the best players in the world and the greatest Norwegian player of all time, he is known
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
Count Binface is a novelty candidate persona adopted by the British writer and comedian Jon Harvey to contest British elections. Binface is presented as an "intergalactic space warrior" who wears a dustbin-shaped helmet.
Lionel Andrés "Leo" Messi is an Argentine professional footballer who plays as a forward for and captains both Major League Soccer (MLS) club Inter Miami and the Argentina national team. Widely regarded as one of the greatest players in history, Messi has set
.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
Addison Mitchell McConnell III is an American politician and attorney who has been a United States senator from Kentucky since 1985 and has been Kentucky's senior U.S. senator since 1999. A member of the Republican Party, McConnell is in his seventh Senate ter
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
Cristiano Ronaldo dos Santos Aveiro is a Portuguese professional footballer who plays as a forward for and captains the Saudi Pro League club Al-Nassr and the Portugal national team. Nicknamed CR7, he is widely regarded as one of the greatest players in histor
Giovanni Vincenzo "Gianni" Infantino is a Swiss football administrator who has served as the president of FIFA since 2016. He was previously Secretary General of UEFA from 2009 to 2016, where he formalised the body's financial regulations and oversaw tournamen
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
The FIFA World Cup is an international association football competition among the senior men's national teams of the members of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The tournament has been held every
Andrey Nascimento dos Santos is a Brazilian professional footballer who plays as a midfielder for Premier League club Manchester United and the Brazil national team. Operating primarily as a central or defensive midfielder, Santos is known for his work rate, t
François Letexier is a French football referee who has officiated in Ligue 1 since January 2016. He has been a FIFA referee since 2017 and is ranked as a UEFA elite category referee, and has taken charge in every major UEFA club and international-level tournam
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
A by-election for the United Kingdom parliamentary constituency of Clacton was held on 13 August 2026, following the resignation of Nigel Farage, its member of Parliament. Farage, who is the leader of Reform UK, had represented Clacton since the 2024 general e
Graham Cunningham Platner is an American oyster farmer, Marine Corps veteran, and politician. Platner was the Democratic nominee in the 2026 US Senate election in Maine until he ended his campaign in July.
The FIFA World Cup is an international association football competition contested by the senior men's national teams of the Fédération Internationale de Football Association (FIFA), the sport's global governing body. The championship has been awarded every fou
Kylian Mbappé Lottin is a French professional footballer who plays as a forward for La Liga club Real Madrid and captains the France national team. Widely regarded as one of the best players in the world and one of the greatest French players of all time, he i
Loïc Féry is a French businessman. He worked as a financial trader in Hong Kong and the City of London from 1997 to 2007, for Société Générale and Crédit Agricole. He founded the investment firm Chenavari in 2007. He became the owner and president of FC Lorien
Olivia Féry is a French former tennis player, who also represented Hong Kong during parts of her professional career.
Jude Victor William Bellingham is an English professional footballer who plays as a midfielder for La Liga club Real Madrid and the England national team. Regarded as one of the best players in the world, he is known for his athleticism and ball-winning abilit
Jaswant Singh Khalra (1952–1995) was an Indian human rights activist. He was one of the most prominent human rights activists in Punjab. He gained international recognition after uncovering evidence of thousands of alleged extrajudicial killings, enforced disa
Lord Buckethead was a novelty candidate who stood in four British general elections between 1987 and 2019, portrayed by three different people. Buckethead is an interstellar villain resembling the Star Wars character Darth Vader.
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
Mohamed Salah Hamed Mahrous Ghaly is an Egyptian professional footballer who plays as a right winger for Süper Lig club Trabzonspor and captains the Egypt national team. He is widely regarded as one of the best players of his generation and one of the greatest
The FIFA Men's World Ranking is a ranking system for men's national teams in association football, first introduced in December 1992. The men's teams of the member nations of FIFA, football's world governing body, are ranked based on their game results with th
Ruth Ellis was a Welsh-born nightclub hostess and convicted murderer who became the last woman to be executed in the United Kingdom, following the fatal shooting of her abusive lover, David Blakely.
Sarah Margaret Qualley is an American actress. A daughter of actress Andie MacDowell, she trained as a ballet dancer in her youth. She made her acting debut in the 2013 drama film Palo Alto and gained recognition for her supporting role in the HBO drama series
Lauren Diane Bennett-Wormald was an English singer from Meopham, Kent. She was a member of Paradiso Girls, who were known for the song "Patron Tequila", and G.R.L., who were featured on Pitbull's "Wild Wild Love" and were known for their song "Ugly Heart". She
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential
Research on Domain Information Mining and Theme Evolution of Scientific Papers
In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Cross-disciplinary research results have gradually become an emerging frontier research direction. There is a certain dependence between a large number of research results. It is difficult to effectively analyze today's scientific research results when looki
Symbolic Recovery of Differential Equations: The Identifiability Problem
Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations with the use of machine learning techniques. In contrast to classical methods which assume the structure of the equation to be known and focus on the estimation of specific parameters, these algorithms aim to learn the structure and the parameters simultaneously. While the uniqueness and, therefore
Adversarial Rademacher Complexity of Deep Neural Networks
Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training data and unseen perturbed test data. While DNNs can fit perturbed training data, generalizing to perturbed test data remains a significant challenge. This motivates the study of generalization guarantees from a learning theory perspective. This paper focuses on adversarial R
As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and ass
InferNet: Exploiting Aggregate GPU Profiles as Side-Channel for DNN Architecture Inference
Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural language processing, and speech recognition. However, as their adoption grows, they face a range of security threats, such as model stealing, architecture extraction, and manipulation, which can compromise their integrity, privacy, and functionality. Past works have relied
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering
Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into global models. There is a problem in federated learning, that is, the negative impact caused by the n
From system models to class models: An in-context learning paradigm
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class? This central question drives the study presented in this paper. In response to this query, we introduce a novel paradigm for system identification, addressing two primary tasks: one-step-ahead prediction and multi-step simulation. Unlik
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations in deployments, such as relying on impractical assumptions (e.g., adversaries acknowledging the presence of attacks before attacking) or undermining accuracy in model training, even in benign scenarios. To address these challenges, we propo
A Distributionally Robust Optimisation Approach to Fair Credit Scoring
Credit scoring has been catalogued by the European Commission and the Executive Office of the US President as a high-risk classification task, in light of the potential harms of making loan approval decisions based on models that would be biased against certain groups. To address this concern, recent credit scoring research has considered a range of fairness-enhancing techniques put forward by the machine learning co
Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems
Mathematical and computational tools have proven to be reliable in decision-making processes. In recent times, in particular, machine learning-based methods are becoming increasingly popular as advanced support tools. When dealing with control problems, reinforcement learning has been applied to decision-making in several applications, most notably in games. The success of these methods in finding solutions to comple
Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertainty
Uncertainties in transition dynamics pose a critical challenge in reinforcement learning (RL), often resulting in performance degradation of trained policies when deployed on hardware. Many robust RL approaches follow two strategies: enforcing smoothness in actor or actor-critic modules with Lipschitz regularization, or learning robust Bellman operators. However, the first strategy does not investigate the impact of
Provably Efficient Off-Policy Adversarial Imitation Learning with Convergence Guarantees
Adversarial Imitation Learning (AIL) faces challenges with sample inefficiency because of its reliance on sufficient on-policy data to evaluate the performance of the current policy during reward function updates. In this work, we study the convergence properties and sample complexity of off-policy AIL algorithms. We show that, even in the absence of importance sampling correction, reusing samples generated by the $o
MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization
Discrete optimization is ubiquitous in science and engineering. The vast array of existing discrete optimization problems, coupled with the continuous emergence of new ones, necessitates off-the-shelf optimizers capable of generating high-quality solutions for a large variety of optimization problems. This article introduces MEGO, a novel general-purpose neural optimizer for binary optimization under the black-box se
Faster and Simpler Greedy Algorithm for $k$-Median and $k$-Means
Clustering problems such as $k$-means and $k$-median are staples of unsupervised learning, and many algorithmic techniques have been developed to tackle their numerous aspects. In this paper, we focus on the class of greedy approximation algorithm, that attracted less attention than local-search or primal-dual counterparts. In particular, we study the recursive greedy algorithm developed by Mettu and Plaxton [SIAM J.
The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social World
Machine Learning research, including work promoting fair or equitable algorithms, often relies on the concept of a data-generating probability distribution. The standard presumption is that since data points are 'sampled from' such a distribution, one can learn from observed data about this distribution and, thus, predict future data points which are also drawn from it. We argue, however, that such true proba
Learning to Explore for Stochastic Gradient MCMC
Bayesian Neural Networks(BNNs) with high-dimensional parameters pose a challenge for posterior inference due to the multi-modality of the posterior distributions. Stochastic Gradient MCMC(SGMCMC) with cyclical learning rate scheduling is a promising solution, but it requires a large number of sampling steps to explore high-dimensional multi-modal posteriors, making it computationally expensive. In this paper, we prop
This paper presents a tutorial of an explainable approach using Convolutional Neural Network (CNN) and Gradient-weighted Class Activation Mapping (Grad-CAM) to classify four progressive dementia stages based on open MRI brain images. The detailed implementation steps are demonstrated with an explanation. Whilst the proposed CNN architecture is demonstrated to achieve more than 99% accuracy for the test dataset, the c
Efficient Scene Appearance Aggregation for Level-of-Detail Rendering
Creating an appearance-preserving level-of-detail (LoD) representation for arbitrary 3D scenes is a challenging problem. The appearance of a scene is an intricate combination of both geometry and material models, and is further complicated by correlation due to the spatial configuration of scene elements. We present a novel volumetric representation for the aggregated appearance of complex scenes and an efficient pip
How Learning Dynamics Drive Adversarially Robust Generalization?
Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing empirical and theoretical explorations fail to provide a satisfactory mechanistic interpretation of the phenomenon. By modeling adversarial training with momentum SGD as a discrete-time dynamical system, we propose a PAC-Bayesian analytical framework that proves time-resolve
ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving
Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to comprehend the actions of other road users. This paper presents ROAD-Waymo, an extensive dataset for
ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term as a Negative Prompting (NP) to filter out unwanted features from samples. However, simply negating CFG guidance creates an inverted probability distribution, often distorting samples away from the marginal distribution. Inspired by recent advances i
Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators
Motivated by dynamic risk measures and conditional $g$-expectations, in this work we propose a numerical method to approximate the solution operator given by a Backward Stochastic Differential Equation (BSDE). The main ingredients for this are the Wiener chaos decomposition and the classical Euler scheme for BSDEs. We show convergence of this scheme under very mild assumptions, and provide a rate of convergence in mo
Large language models (LLMs) have been demonstrated to possess the capabilities to understand fundamental graph properties and address various graph reasoning tasks. Existing methods fine-tune LLMs to understand and execute graph reasoning tasks by specially designed task instructions. However, these Text-Instruction methods generally exhibit poor performance. Inspired by tool learning, researchers propose Tool-Instr
Network Dynamics-Based Framework for Understanding Deep Neural Networks
Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning. In this work, we propose a theoretical framework to analyze learning dynamics through the lens of dynamical systems theory. We redefine the notions of linearity and nonlinearity in neural networks by introducing two fundamental transformation units at the neuron level: order-preserving trans
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