Record 21012026 · captured 2026-08-25
The world looked up Fernando Mendoza. 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.
Fernando Gabriel Mendoza V is an American professional football quarterback for the Las Vegas Raiders of the National Football League (NFL). Mendoza played college football for the California Golden Bears for three seasons before transferring to the Indiana Ho
Nicola Anne Peltz Beckham is an American actress. She is known for her roles as Katara in the film The Last Airbender (2010), Bradley Martin in the A&E drama series Bates Motel (2013–2015) and Tessa Yeager in the film Transformers: Age of Extinction (2014).
Curtis John Cignetti is an American college football coach who is the head football coach at Indiana University Bloomington. He previously served as the head coach at Indiana University of Pennsylvania (IUP) from 2011 to 2016, Elon University from 2017 to 2018
Nelson Peltz is an American billionaire businessman and investor. He is a founding partner, together with Peter W. May and Edward P. Garden, of Trian Partners, an alternative investment management fund based in New York. He is a former director of Heinz, Monde
Carson Raine Beck is an American professional football quarterback for the Arizona Cardinals of the National Football League (NFL). He played college football for the Georgia Bulldogs, where he was part of two national championships as a backup in 2021 and 202
Brooklyn Joseph Peltz Beckham is the eldest child of former professional footballer David Beckham and fashion designer and former Spice Girls member Victoria Beckham.
Greenland is an autonomous territory of the Kingdom of Denmark and is the largest of the kingdom's three constituent parts by land area, the others being Denmark proper and the Faroe Islands. Citizens of Greenland are citizens of Denmark. They are thus citizen
The Rip is a 2026 American action thriller film written and directed by Joe Carnahan, who developed the story with Michael McGrale. The film stars Matt Damon and Ben Affleck as police officers in the Miami-Dade Police Department narcotics unit. It also stars S
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
Valentino Clemente Ludovico Garavani, known mononymously as Valentino, was an Italian fashion designer who founded Valentino S.p.A., a luxury fashion house, in 1960 and served as its creative director until 2007. A flamboyant designer noted for his retro piece
The Indiana Hoosiers football program represents Indiana University Bloomington in NCAA Division I Football Bowl Subdivision college football (FBS) and in the Big Ten Conference. The Hoosiers have played their home games at Memorial Stadium in Bloomington, Ind
Abella Danger is an American former pornographic film actress and director.
Nitin Nabin is an Indian politician, political organiser, and activist who has been serving as the 16th national president of the Bharatiya Janata Party (BJP) since January 2026 and an MP in the upper chamber of the Indian Parliament, the Rajya Sabha since Apr
Martin Luther King Jr. was an American civil rights activist and Baptist minister who was a prominent leader of the civil rights movement from 1955 until his assassination in 1968. He advanced civil rights for people of color in the United States through the u
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
Since 2025, the second Donald Trump administration of the United States has sought to annex Greenland, an autonomous territory of Denmark, triggering an ongoing international diplomatic crisis. This escalated in early 2026 after Trump refused to rule out the u
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
28 Years Later: The Bone Temple
28 Years Later: The Bone Temple is a 2026 post-apocalyptic horror film directed by Nia DaCosta and written by Alex Garland. It is the direct sequel to 28 Years Later (2025) and the fourth instalment in the 28 Days Later film series. It stars Ralph Fiennes, Jac
Donald John Trump is an American politician, media personality, and businessman who is the 47th president of the United States. A member of the Republican Party, he served as the 45th president from 2017 to 2021.
William Peltz is an American actor, known for his roles in the supernatural horror film Unfriended (2014), the comedy-drama film Men, Women & Children (2014), and the supernatural drama television series Manifest (2021).
Bruce Lee was a Hong Kong and American martial artist, actor, and filmmaker. He was the founder of Jeet Kune Do, a hybrid martial arts philosophy, which was formed from his experiences in unarmed fighting and self-defense—as well as eclectic, Zen Buddhist, and
Sir David Robert Joseph Beckham is an English former professional footballer, the president and co-owner of Inter Miami CF and co-owner of Salford City F.C.. Primarily a right midfielder and known for his range of passing, crossing ability and set-piece taking
Malachi Toney is an American college football wide receiver for the Miami Hurricanes.
Mark Cuban is an American businessman, entrepreneur, and television personality. He is the former principal owner and current minority owner of the Dallas Mavericks of the National Basketball Association (NBA) and co-owner of 2929 Entertainment. From 2012 to 2
The Board of Peace (BoP), or the Peace Board, is an international organization with the stated purpose of promoting peacebuilding around the world. Established by President Donald Trump and led by the government of the United States, the board is named in Unit
Dhurandhar is a 2025 Indian Hindi-language spy action thriller film written and directed by Aditya Dhar. It is produced by Aditya Dhar, Lokesh Dhar and Jyoti Deshpande under Jio Studios and B62 Studios. The film features an ensemble cast consisting of Ranveer
"XXX" is a song by American rapper Kendrick Lamar, from his fourth studio album Damn, released on April 14, 2017. The eleventh track on the album, the song was written by Lamar, Mike Will Made It, DJ Dahi, Mark Spears a.k.a. Sounwave, Anthony Tiffith, Bono, th
Mario Manuel Cristobal is an American college football coach who is the head football coach for the University of Miami. He previously served as the head football coach at Florida International University (FIU) from 2007 to 2012 and the University of Oregon fr
Victoria Caroline, Lady Beckham, is an English fashion designer, singer, and television personality. She rose to prominence in the 1990s as a member of the pop group the Spice Girls, in which she was nicknamed Posh Spice. After the Spice Girls disbanded in 200
The Chagos Archipelago or Chagos Islands is a group of seven atolls comprising more than 60 islands in the Indian Ocean about 500 kilometres (310 mi) south of the Maldives archipelago. This chain of islands is the southernmost archipelago of the Chagos–Laccadi
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Local Minima Structures in Gaussian Mixture Models
We investigate the landscape of the negative log-likelihood function of Gaussian Mixture Models (GMMs) with a general number of components in the population limit. As the objective function is non-convex, there can be multiple local minima that are not globally optimal, even for well-separated mixture models. Our study reveals that all local minima share a common structure that partially identifies the cluster center
Generalization Bounds for Sparse Random Feature Expansions
Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach to standard neural networks since they can represent similar function spaces without a costly training phase. However, for accuracy, random feature methods require more measurements than trainable parameters, limiting their use for data-sc
Parallel evolutionary algorithms (PEAs) have been studied for reducing the execution time of evolutionary algorithms by utilizing parallel computing. An asynchronous PEA (APEA) is a scheme of PEAs that increases computational efficiency by generating a new solution immediately after a solution evaluation completes without the idling time of computing nodes. However, because APEA gives more search opportunities to sol
Acoustics-specific Piano Velocity Estimation
Motivated by the state-of-art psychological research, we note that a piano performance transcribed with existing Automatic Music Transcription (AMT) methods cannot be successfully resynthesized without affecting the artistic content of the performance. This is due to 1) the different mappings between MIDI parameters used by different instruments, and 2) the fact that musicians adapt their way of playing to the surrou
Deep Feature Learning for Medical Acoustics
The purpose of this paper is to compare different learnable frontends in medical acoustics tasks. A framework has been implemented to classify human respiratory sounds and heartbeats in two categories, i.e. healthy or affected by pathologies. After obtaining two suitable datasets, we proceeded to classify the sounds using two learnable state-of-art frontends -- LEAF and nnAudio -- plus a non-learnable baseline fronte
Functional dimension of feedforward ReLU neural networks
It is well-known that the parameterized family of functions representable by fully-connected feedforward neural networks with ReLU activation function is precisely the class of piecewise linear functions with finitely many pieces. It is less well-known that for every fixed architecture of ReLU neural network, the parameter space admits positive-dimensional spaces of symmetries, and hence the local functional dimensio
Bias and Extrapolation in Markovian Linear Stochastic Approximation with Constant Stepsizes
We consider Linear Stochastic Approximation (LSA) with a constant stepsize and Markovian data. Viewing the joint process of the data and LSA iterate as a time-homogeneous Markov chain, we prove its convergence to a unique limiting and stationary distribution in Wasserstein distance and establish non-asymptotic, geometric convergence rates. Furthermore, we show that the bias vector of this limit admits an infinite ser
Adapting Node-Place Model to Predict and Monitor COVID-19 Footprints and Transmission Risks
The node-place model has been widely used to classify and evaluate transit stations, which sheds light on individual travel behaviors and supports urban planning through effectively integrating land use and transportation development. This article adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a ci
Risk-sensitive reinforcement learning (RL) has become a popular tool for controlling the risk of uncertain outcomes and ensuring reliable performance in highly stochastic sequential decision-making problems. While it has been shown that policy gradient methods can find globally optimal policies in the risk-neutral setting, it remains unclear if the risk-averse variants enjoy the same global convergence guarantees. In
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset
Social media plays a significant role in cross-cultural communication. A vast amount of this occurs in code-mixed and multilingual form, posing a significant challenge to Natural Language Processing (NLP) tools for processing such information, like language identification, topic modeling, and named-entity recognition. To address this, we introduce a large-scale multilingual, and multi-topic dataset (MMT) collected fr
GDP nowcasting with artificial neural networks: How much does long-term memory matter?
We apply artificial neural networks (ANNs) to nowcast quarterly GDP growth for the U.S. economy. Using the monthly FRED-MD database, we compare the nowcasting performance of five different ANN architectures: the multilayer perceptron (MLP), the one-dimensional convolutional neural network (1D CNN), the Elman recurrent neural network (RNN), the long short-term memory network (LSTM), and the gated recurrent unit (GRU).
Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges
The latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention and cognitive processes in VR. Despite these possibilities, eye-tracking data also reveals users
A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing
Industrial soft sensing is crucial for accurate process monitoring through reliable inference of dominant sensor variables. However, developing effective data-driven soft sensor models presents challenges, such as achieving domain adaptability, addressing incomplete sensor labels, and learning stochastic data variability. To overcome these challenges, we propose a Deep Variational Potential Flow (DVPF) framework for
Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images
Segmentation networks are not explicitly imposed to learn global invariants of an image, such as the shape of an object and the geometry between multiple objects, when they are trained with a standard loss function. On the other hand, incorporating such invariants into network training may help improve performance for various segmentation tasks when they are the intrinsic characteristics of the objects to be segmente
Shape Completion with Prediction of Uncertain Regions
Shape completion, i.e., predicting the complete geometry of an object from a partial observation, is highly relevant for several downstream tasks, most notably robotic manipulation. When basing planning or prediction of real grasps on object shape reconstruction, an indication of severe geometric uncertainty is indispensable. In particular, there can be an irreducible uncertainty in extended regions about the presenc
Compositional Feature Augmentation for Unbiased Scene Graph Generation
Scene Graph Generation (SGG) aims to detect all the visual relation triplets $<$\texttt{sub}, \texttt{pred}, \texttt{obj}$>$ in a given image. With the emergence of various advanced techniques for better utilizing both the intrinsic and extrinsic information in each relation triplet, SGG has achieved great progress over the recent years. However, due to the ubiquitous long-tailed predicate distributions, today&
Optimal Conditional Inference in Adaptive Experiments
We study batched bandit experiments and consider the problem of inference conditional on the realized stopping time, assignment probabilities, and target parameter, where all of these may be chosen adaptively using information up to the last batch of the experiment. Absent further restrictions on the experiment, we show that inference using only the results of the last batch is optimal. When the adaptive aspects of t
Contextualising Levels of Language Resourcedness that affect NLP tasks
Several widely used software applications involve some form of processing of natural language, with tasks ranging from digitising hardcopies and text processing to speech generation. Varied language resources are used to develop software systems to accomplish a wide range of natural language processing (NLP) tasks, such as the ubiquitous spellcheckers and chatbots. Languages are typically characterised as either low
Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, especially when it comes to only partial observability and versatile grasping with multi-fingered hands. We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single dept
Shadow loss: Memory-linear deep metric learning for efficient training
Deep metric learning objectives (e.g., triplet loss) require storing and comparing high-dimensional embeddings, making the per-batch loss buffer scale as $O(S\cdot D)$, where $S$ is the number of samples in a batch and $D$ is the feature dimension, thus limiting training on memory-constrained hardware. We propose Shadow Loss, a proxy-free, parameter-free objective that measures similarity via scalar projections onto
Learning to Simulate: Generative Metamodeling via Quantile Regression
Stochastic simulation models effectively capture complex system dynamics but are often too slow for real-time decision-making. Traditional metamodeling techniques learn relationships between simulator inputs and a single output summary statistic, such as the mean or median. These techniques enable real-time predictions without additional simulations. However, they require prior selection of one appropriate output sum
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. Federated learning is one of the most promising techniques for training global machine learning models while preserving data privacy of vehicles and optimizing c
GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs
Despite the rapid progress of large language models (LLMs), knowledge graph-based question answering (KGQA) remains essential for producing verifiable and hallucination-resistant answers in many real-world settings where answer trustworthiness and computational efficiency are highly valued. However, most existing KGQA methods provide only final answers in the form of KG entities. Without explicit explanations -- idea
The target of Electronic Health Record (EHR) coding is to find the diagnostic codes according to the EHRs. In previous research, researchers have preferred to do multi-classification on the EHR coding task; most of them encode the EHR first and then process it to get the probability of each code based on the EHR representation. However, the question of complicating diseases is neglected among all these methods. In th
Hidden Minima in Two-Layer ReLU Networks
We consider the optimization problem associated with training two-layer ReLU networks with \(d\) inputs under the squared loss, where the labels are generated by a target network. Recent work has identified two distinct classes of infinite families of minima: one whose training loss vanishes in the high-dimensional limit, and another whose loss remains bounded away from zero. The latter family is empirically avoided
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