Record 09032026 · captured 2026-08-25
The world looked up Men's T20 World Cup. 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.
The ICC Men's T20 World Cup, formerly the ICC World Twenty20, is a biennial world cup for cricket in Twenty20 International (T20I) format, organised by the International Cricket Council (ICC). It was held in every odd year from 2007 to 2009, and since 2010 has
Since 28 February 2026, the United States and Israel have been at war with Iran and its regional allies. Hostilities broke out after US–Israeli airstrikes killed several Iranian officials, including Supreme Leader Ali Khamenei. The strikes were launched amid o
On 4 August 2002, two 10-year-old girls, Holly Marie Wells and Jessica Amiee Chapman, were lured into the home of a local resident and school caretaker, Ian Huntley, in Soham, Cambridgeshire, England. Both children were murdered – most likely by asphyxiation –
International Women's Day (IWD) is celebrated on 8 March, commemorating women's fight for equality and liberation along with the women's rights movement. International Women's Day gives focus to issues such as gender equality, reproductive rights, and violence
Mojtaba Hosseini Khamenei is an Iranian Shia cleric and politician who has served as the third supreme leader of Iran since 2026. A member of the Khamenei family and the second son of second supreme leader Ali Khamenei, he previously served as Vakil of the Sup
War Machine is a 2026 military science fiction action film directed, co-produced, and co-written by Patrick Hughes. It stars Alan Ritchson, Dennis Quaid, Stephan James, Jai Courtney, Esai Morales, Keiynan Lonsdale, and Daniel Webber, and follows a staff sergea
Ali Hosseini Khamenei was an Iranian politician and Shia cleric who served as the second supreme leader of Iran from 1989 until his assassination in 2026. A member of the Khamenei family who held the title Grand Ayatollah, he previously served as the third pre
UFC 326: Holloway vs. Oliveira 2 was a mixed martial arts event produced by the Ultimate Fighting Championship that took place on March 7, 2026, at the T-Mobile Arena in Paradise, Nevada, part of the Las Vegas Valley, United States.
Sanju Viswanath Samson is an Indian cricketer who plays for the India national cricket team in the T20I format. He was part of the 2024 and 2026 T20 world cup winning teams, including a Player of the Tournament performance in 2026. He plays for Chennai Super K
Jasprit Jasbirsingh Bumrah is an Indian International cricketer who plays for the Indian national cricket team in all formats of this game and has captained India in Tests and T20Is. He is widely regarded as one of the greatest fast bowlers of his generation.
Arvid Anand Olof Lindblad is a British and Swedish racing driver who competes in Formula One for Racing Bulls under a British flag.
Iran, officially the Islamic Republic of Iran, and historically known as Persia, is a country in West Asia. It borders Iraq to the west, Turkey, Azerbaijan, and Armenia to the northwest, the Caspian Sea to the north, Turkmenistan to the northeast, Afghanistan
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
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.
Daylight saving time (DST), also referred to as daylight savings time, daylight time, or summer time, is the practice of advancing clocks to make better use of the longer daylight available during summer by having darkness fall at a later clock time. The typic
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
Kristi Lynn Arnold Noem is an American politician serving as the United States special envoy for the Shield of the Americas since 2026. From 2025 to 2026, she served as the eighth United States secretary of homeland security. A member of the Republican Party,
Gorillaz are an English virtual band formed in 1998 by the musician Damon Albarn and the artist Jamie Hewlett. The band primarily consists of four fictional members: 2-D, Murdoc Niccals, Noodle and Russel Hobbs (drums). Their universe is presented in media suc
Akshay Bhatia is an American professional golfer. He made his first PGA Tour start in 2019 at the age of 17 after receiving a sponsor exemption into Valspar Championship. He turned pro later that year and made his professional debut at Sanderson Farms Champion
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
The ICC Men's Cricket World Cup is a quadrennial world cup for cricket in One Day International (ODI) format, organised by the International Cricket Council (ICC). The tournament is one of the world's most viewed sporting events and considered the flagship eve
The 2026 ICC Men's T20 World Cup was the tenth edition of the ICC Men's T20 World Cup, co-hosted by Board of Control for Cricket in India and Sri Lanka Cricket from 7 February to 8 March 2026. Sri Lanka had previously hosted the competition in 2012 and India i
The Bride! is a 2026 American gothic romance film directed and written by Maggie Gyllenhaal and starring Jessie Buckley, Christian Bale, Peter Sarsgaard, Annette Bening, Jake Gyllenhaal, and Penélope Cruz. The film draws inspiration from the 1935 film Bride of
The 2026 World Baseball Classic was an international professional baseball tournament between 20 national baseball teams, and the sixth iteration of the World Baseball Classic (WBC). It ran from March 5 to 17, 2026. The pool-play rounds were played in LoanDepo
Charles Oliveira da Silva is a Brazilian professional mixed martial artist and fourth degree black belt Brazilian jiu-jitsu practitioner. Oliveira currently competes in the Lightweight division of the Ultimate Fighting Championship (UFC), where he is a former
Corey Parker was an American actor and acting coach.
Jerome Max Keliʻi Holloway is an American professional mixed martial artist. He currently competes in the Lightweight division of the Ultimate Fighting Championship (UFC), where he is a former UFC Featherweight Champion and former symbolic UFC "BMF" titleholde
Jessie Buckley is an Irish actress and singer. Her accolades include an Academy Award, two BAFTAs, an Actor Award, a Golden Globe Award, a Critics' Choice Award and a Laurence Olivier Award.
The World Baseball Classic (WBC), also referred to as The Classic, is a quadrennial international baseball tournament sanctioned by the World Baseball Softball Confederation (WBSC), the sport's global governing body, and organized by World Baseball Classic Inc
Hoppers is a 2026 American animated science fiction comedy film directed by Daniel Chong from a screenplay by Jesse Andrews and a story by Chong and Andrews. Produced by Pixar Animation Studios for Walt Disney Pictures, the film stars the voices of Piper Curda
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
A mixed-frequency approach for exchange rates predictions
Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding alternative predictors and statistical models based on temporal aggregation. In this paper, we propose an approa
A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts
Fetal standard scan plane detection during 2-D mid-pregnancy examinations is a highly complex task, which requires extensive medical knowledge and years of training. Although deep neural networks (DNN) can assist inexperienced operators in these tasks, their lack of transparency and interpretability limit their application. Despite some researchers have been committed to visualizing the decision process of DNN, most
Mean-based incomplete pairwise comparisons method with the reference values
In this article, we propose two quantitative methods for calculating weight vectors for incomplete pairwise comparison matrices using reference values. Both procedures are extensions of arithmetic and geometric heuristic estimation (HRE) methods. The proposed solutions allow flexible selection of the number of reference alternatives and the range of comparisons, from the acceptable minimum to a complete set. In this
Correlations Between COVID-19 and Dengue
A dramatic increase in the number of outbreaks of Dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate Dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide
As phasor measurement units (PMUs) become more widely used in transmission power systems, a fast state estimation (SE) algorithm that can take advantage of their high sample rates is needed. To accomplish this, we present a method that uses graph neural networks (GNNs) to learn complex bus voltage estimates from PMU voltage and current measurements. We propose an original implementation of GNNs over the power system&
Analyzing the Performance of ChatGPT in Cardiology and Vascular Pathologies
The article aims to analyze the performance of ChatGPT, a large language model developed by OpenAI, in the context of cardiology and vascular pathologies. The study evaluated the accuracy of ChatGPT in answering challenging multiple-choice questions (QCM) using a dataset of 190 questions from the Siamois-QCM platform. The goal was to assess ChatGPT potential as a valuable tool in medical education compared to two wel
Expert-Aided Causal Discovery of Ancestral Graphs
Causal discovery (CD) is an important component of many scientific applications, yet most techniques produce unreliable point estimates that often contradict expert knowledge. To mitigate this, recent research has focused on ex-ante incorporation of background knowledge into the CD process, typically under an unrealistic causal sufficiency assumption. When probing experts is costly (e.g., hidden behind expensive LLM
RBF Weighted Hyper-Involution for RGB-D Object Detection
A vast majority of augmented reality devices come equipped with depth and color cameras. Despite their advantages, extracting both photometric and depth features simultaneously in real-time remains challenging due to inherent differences between depth and color images. Furthermore, standard convolution operations are insufficient for extracting information directly from raw depth images, leading to inefficient interm
A unified framework for learning with nonlinear model classes from arbitrary linear samples
We study the fundamental problem of learning an unknown object from data using a prescribed model class. We introduce a unified framework that accommodates objects in arbitrary Hilbert spaces, general (possibly vector-valued) random linear measurements and general types of nonlinear models. We establish novel learning guarantees for this framework that explicitly relate the required amount of data to structural prope
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation. However, SNNs suffer from memory and computation overhead due to spatio-temporal dynamics and multiple backpropagation computations across timesteps during training. To address this issue, we introduce Tensor Train Decomposition for Spiking Ne
The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate
This paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators. We assess the performance of three LLMs and one open-source LM in Question-Answering (QA) and evaluation tasks using the TriviaQA (Joshi et al., 2017) dataset. Results indicate a significant disparity, with LLMs exhibiting lower performance in evaluation tasks compared to generation tasks.
We need to trust robots that use often opaque AI methods. They need to explain themselves to us, and we need to trust their explanation. In this regard, explainability plays a critical role in trustworthy autonomous decision-making to foster transparency and acceptance among end users, especially in complex autonomous driving. Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising poten
Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information
Leveraging large-scale Text-to-Image (TTI) models have become a common technique for generating exemplar or training dataset in the fields of image synthesis, video editing, 3D reconstruction. However, semantic structural visual hallucinations involving perceptually severe defects remain a concern, especially in the domain of non-photorealistic rendering (NPR) such as cartoons and pixelization-style character. To det
Algorithmic Collusion by Large Language Models
We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). In oligopoly settings, LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits. Variation in seemingly innocuous phrases in LLM instructions ("prompts") substantially influence the degree of supracompetitive pricing. We develop novel techniques for behavioral analysis of LLMs and u
Scientific articles play a crucial role in advancing knowledge and informing research directions. One key aspect of evaluating scientific articles is the analysis of citations, which provides insights into the impact and reception of the cited works. This article introduces the innovative use of large language models, particularly ChatGPT, for comprehensive sentiment analysis of citations within scientific articles.
Recent remarkable advancements in geometric deep generative models, coupled with accumulated structural data, enable structure-based drug design (SBDD) using only target protein information. However, existing models often struggle to balance multiple objectives, excelling only in specific tasks. BInD, a diffusion model with knowledge-based guidance, is introduced to address this limitation by co-generating molecules
This paper introduces a novel two-stage active learning (AL) pipeline for automatic speech recognition (ASR), combining unsupervised and supervised AL methods. The first stage utilizes unsupervised AL by using x-vectors clustering for diverse sample selection from unlabeled speech data, thus establishing a robust initial dataset for the subsequent supervised AL. The second stage incorporates a supervised AL strategy,
My part is bigger than yours -- assessment within a group of peers
A project (e.g., writing a collaborative research paper) is often a group effort. At the end, each contributor identifies their contribution, often verbally. The reward, however, is very frequently financial. It leads to the question of what (percentage) share in the creation of the paper is due to individual authors. Different authors may have various opinions on the matter; even worse, their opinions may have diffe
Predictive Coding Networks and Inference Learning: Tutorial and Survey
Recent years have witnessed a growing call for renewed emphasis on neuroscience-inspired approaches in artificial intelligence research, under the banner of NeuroAI. A prime example of this is predictive coding networks (PCNs), based on the neuroscientific framework of predictive coding. This framework views the brain as a hierarchical Bayesian inference model that minimizes prediction errors through feedback connect
Transforming Agency. On the mode of existence of Large Language Models
This paper investigates the ontological characterization of Large Language Models (LLMs) like ChatGPT. Between inflationary and deflationary accounts, we pay special attention to their status as agents. This requires explaining in detail the architecture, processing, and training procedures that enable LLMs to display their capacities, and the extensions used to turn LLMs into agent-like systems. After a systematic a
Do Prevalent Bias Metrics Capture Allocational Harms from LLMs?
Allocational harms occur when resources or opportunities are unfairly withheld from specific groups. Many proposed bias measures ignore the discrepancy between predictions, which are what the proposed methods consider, and decisions that are made as a result of those predictions. Our work examines the reliability of current bias metrics in assessing allocational harms arising from predictions of large language models
Fuse4Seg: Image Fusion for Multi-Modal Medical Segmentation via Bi-level Optimization
Multi-modal medical image fusion is traditionally optimized for human visual perception, aiming to maximize generic contrast and structural fidelity. However, when these visually pleasing fused images are deployed in automated clinical workflows, this visual-semantic discrepancy causes task-agnostic feature degradation, inadvertently smoothing out critical, high-frequency tumor boundaries. To bridge this semantic gap
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
Parameter-Efficient Fine-Tuning (PEFT) effectively adapts pre-trained transformers to downstream tasks. However, the optimization of tasks performance often comes at the cost of generalizability in fine-tuned models. To address this issue, we theoretically connect smaller weight gradient norms during training and larger datasets to the improvements in model generalization. Motivated by this connection, we propose red
FALCON: Future-Aware Learning with Contextual Object-Centric Pretraining for UAV Action Recognition
We introduce FALCON, a unified self-supervised video pretraining approach for UAV action recognition from raw RGB aerial footage, requiring no additional preprocessing at inference. UAV videos exhibit severe spatial imbalance: large, cluttered backgrounds dominate the field of view, causing reconstruction-based pretraining to waste capacity on uninformative regions and under-learn action-relevant human/object cues. F
AuthFace: Towards Authentic Blind Face Restoration with Face-oriented Generative Diffusion Prior
Blind face restoration (BFR) is a fundamental and challenging problem in computer vision. To faithfully restore high-quality (HQ) photos from poor-quality ones, recent research endeavors predominantly rely on facial image priors from the powerful pretrained text-to-image (T2I) diffusion models. However, such priors often lead to the incorrect generation of non-facial features and insufficient facial details, thus ren
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