Record 23012026 · captured 2026-08-25
The world looked up Board of Peace. 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 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
Sinners is a 2025 American horror film produced, written, and directed by Ryan Coogler. Set in 1932 in the Mississippi Delta, it stars Michael B. Jordan in dual roles as criminal twin brothers who return to their hometown in the Jim Crow South, where they are
The 98th Academy Awards ceremony, presented by the Academy of Motion Picture Arts and Sciences (AMPAS), took place on March 15, 2026, at the Dolby Theatre in Hollywood, Los Angeles. During the gala, the AMPAS presented Academy Awards in 24 categories honoring
Elizabeth Ann Gilmour is an American child safety activist and commentator for ABC News. She was put into the national spotlight in 2002 at age 14 when she was abducted from her home in Salt Lake City by Brian David Mitchell. Mitchell and his wife, Wanda Barze
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
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).
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
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
The kidnapping of Elizabeth Smart occurred on June 5, 2002, when 14-year-old American girl Elizabeth Ann Smart was kidnapped from her home in Salt Lake City, Utah by Brian David Mitchell.
Jesse Cole Minter is an American professional football coach who is the head coach for the Baltimore Ravens of the National Football League (NFL), returning to the organization he served from 2017 to 2020. Prior to this, he was the defensive coordinator for th
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
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
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.
The 97th Academy Awards ceremony, presented by the Academy of Motion Picture Arts and Sciences (AMPAS), took place on March 2, 2025, at the Dolby Theatre in Hollywood, Los Angeles. During the gala, the AMPAS presented Academy Awards in 23 categories honoring f
"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
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
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
One Battle After Another is a 2025 American action thriller film written, directed, and produced by Paul Thomas Anderson. Inspired by the 1990 novel Vineland by Thomas Pynchon, the film's ensemble cast includes Leonardo DiCaprio, Sean Penn, Benicio del Toro, R
Marty Supreme is a 2025 American sports comedy-drama film directed by Josh Safdie, who co-wrote it with Ronald Bronstein. Set in the 1950s, it stars Timothée Chalamet as table tennis player Marty Mauser and follows his quest to become world champion. Gwyneth P
Hamnet is a 2025 historical drama film directed by Chloé Zhao, who co-wrote the screenplay with Maggie O'Farrell, based on the 2020 novel by O'Farrell. The film dramatises the family life of William Shakespeare and his wife Agnes Hathaway as they cope with the
Mark Joseph Carney is a Canadian politician and economist who became the 24th prime minister of Canada in 2025. Carney was also elected as leader of the Liberal Party and the member of Parliament (MP) for Nepean in 2025. He was previously Governor of the Bank
The Beauty is an American science fiction body horror television series created by Ryan Murphy and Matthew Hodgson. It is based on the comic book The Beauty, written by Jeremy Haun and Jason A. Hurley, and stars Evan Peters, Anthony Ramos, Jeremy Pope, Rebecca
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
Wade Steven Wilson is an American criminal convicted of the 2019 murders of Kristine Melton and Diane Ruiz in Cape Coral, Florida. Due to sharing the name of the Marvel character Wade "Deadpool" Wilson, Wilson has been referred to in the media as the "Deadpool
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
Sentimental Value is a 2025 drama film directed by Joachim Trier, who co-wrote it with Eskil Vogt. It follows sisters Nora and Agnes in their reunion with their estranged father Gustav, who is preparing to make his next film with young American actress Rachel.
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
Ivan Eric Raiklin is an American far-right political operative, constitutional lawyer, and former Army reservist.
Delroy George Lindo is a British-born American actor. Starting his career in the 1975 stage production of Of Mice and Men, he later earned a nomination for the Tony Award for Best Featured Actor in a Play for his work in the 1988 production of Joe Turner's Com
The Traitors (British TV series) series 4
The fourth series of The Traitors was first broadcast on BBC One on 1 January 2026. Claudia Winkleman returned to present the series, which was accompanied by the spin-off show The Traitors: Uncloaked, presented by Ed Gamble. The series concluded on 23 January
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Control Occupation Kernel Regression for Nonlinear Control-Affine Systems
This manuscript presents an algorithm for obtaining an approximation of a nonlinear high order control affine dynamical system. Controlled trajectories of the system are leveraged as the central unit of information via embedding them in vector-valued reproducing kernel Hilbert space (vvRKHS). The trajectories are embedded as the so-called higher order control occupation kernels which represent an operator on the vvRK
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XAI methods are unable to produce precisely located domain-specific explanations, making the explanati
Point2Vec for Self-Supervised Representation Learning on Point Clouds
Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach. However, it remains open whether such a framework generalizes to the unique challenges of 3D point clouds. To answer this question, we extend data2vec to the point cloud domain and report encouraging results on several downstream tasks. In an in-depth analysis, we d
Representation-Driven Reinforcement Learning
We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual bandits to guide exploration and exploitation. Particularly, embedding a policy network into a linear feature space allows us to reframe the exploration-exploitation problem as a representation-exploitation problem, where good policy representa
Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the impact of multiple real-world dermoscopic views of a single lesion of interest on a CNN-based melanoma classifier. Methods: This study evaluated 656 suspected melanoma lesions. Classifier performance was measured using area under the receiver
Multi-event Video-Text Retrieval
Video-Text Retrieval (VTR) is a crucial multi-modal task in an era of massive video-text data on the Internet. A plethora of work characterized by using a two-stream Vision-Language model architecture that learns a joint representation of video-text pairs has become a prominent approach for the VTR task. However, these models operate under the assumption of bijective video-text correspondences and neglect a more prac
This paper presents a unified analysis for the proximal subgradient method (Prox-SubGrad) type approach to minimize an overall objective of $f(x)+r(x)$, subject to convex constraints, where both $f$ and $r$ are weakly convex, nonsmooth, and non-Lipschitz. Leveraging on the properties of the Moreau envelope of weakly convex functions, we are able to relate error-bound conditions, the growth conditions of the subgradie
Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, ther
RFI Detection with Spiking Neural Networks
Detecting and mitigating Radio Frequency Interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning methods has led to their application in radio astronomy, and in RFI detection. Spiking Neural Networks (SNNs), inspired by biological systems, are well-suited for processing spatio-temporal data. This study introduces the first exploratory app
The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnostic potential over text-based descriptions.
Early detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. However, there are few prospective studies confirming these promising results. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion o
Multilingual large language models (LLMs) are expensive to pretrain and often suffer from imbalances across languages and datasets, English-centric bias, tokenizer oversegmentation for morphologically rich low-resource languages, and the curse of multilinguality. We introduce PARAMANU, the first family of Indian-only autoregressive language models trained from scratch on open-source language-specific data for the fiv
Scalable Multi-view Clustering via Explicit Kernel Features Maps
The proliferation of high-dimensional data from sources such as social media, sensor networks, and online platforms has created new challenges for clustering algorithms. Multi-view clustering, which integrates complementary information from multiple data perspectives, has emerged as a powerful solution. However, existing methods often struggle with scalability and efficiency, particularly on large attributed networks
Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes
Structured pruning is a promising approach to create smaller, faster large language models. However, existing methods typically rely on computing the gradient via backward passes, which can inflate memory requirements and compute costs. In this work we introduce Bonsai, a gradient-free structured pruning method that eliminates the need for backpropagation, significantly reducing memory requirements and compute costs
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
We interact with the world with our hands and see it through our own (egocentric) perspective. A holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation. Accurately reconstructing such interactions in 3D is challenging due to heavy occlusion, viewpoint bias, camera distortion, and motion blur from the head movement. To thi
Thought of Search: Planning with Language Models Through The Lens of Efficiency
Among the most important properties of algorithms investigated in computer science are soundness, completeness, and complexity. These properties, however, are rarely analyzed for the vast collection of recently proposed methods for planning with large language models. In this work, we alleviate this gap. We analyse these properties of using LLMs for planning and highlight that recent trends abandon both soundness and
WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users
Existing pose estimation models perform poorly on wheelchair users due to a lack of representation in training data. We present a data synthesis pipeline to address this disparity in data collection and subsequently improve pose estimation performance for wheelchair users. Our configurable pipeline generates synthetic data of wheelchair users using motion capture data and motion generation outputs simulated in the Un
Efficient Multimodal Large Language Models: A Survey
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning. However, the extensive model size and high training and inference costs have hindered the widespread application of MLLMs in academia and industry. Thus, studying efficient and lightweight MLLMs has enormous potential, especially in edge co
On the Exponential Convergence for Offline RLHF with Pairwise Comparisons
We consider the problem of offline reinforcement learning from human feedback (RLHF) with pairwise comparisons proposed by Zhu et al. (2023), where the implicit reward is a linear function of an unknown parameter. Given an offline dataset, our objective consists in ascertaining the optimal action for each state, with the ultimate goal of minimizing the {\em simple regret}. We propose an algorithm, \underline{RL} with
Vision-Language Models Align with Human Neural Representations in Concept Processing
Recent studies suggest that transformer-based vision-language models (VLMs) capture the multimodality of concept processing in the human brain. However, a systematic evaluation exploring different types of VLM architectures and the role played by visual and textual context is still lacking. Here, we analyse multiple VLMs employing different strategies to integrate visual and textual modalities, along with language-on
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6
Climate change will impact wind and therefore wind power generation with largely unknown effect and magnitude. Climate models can provide insights and should be used for long-term power planning. In this work we use Gaussian processes to predict power output given wind speeds from a global climate model and compare the aggregated predictions to actual power generation. Analyzing past climate model data supports the u
Medal Matters: Probing LLMs' Failure Cases Through Olympic Rankings
Large language models (LLMs) have achieved remarkable success in natural language processing tasks, yet their internal knowledge structures remain poorly understood. This study examines these structures through the lens of historical Olympic medal tallies, evaluating LLMs on two tasks: (1) retrieving medal counts for specific teams and (2) identifying rankings of each team. While state-of-the-art LLMs excel in recall
Fine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task. While the proposed model achieved state-of-the-art results, high computational demands due to multi-step inference limited its use in many scenarios. In this paper, we show that the perceived inefficiency was caused by a flaw in the infere
The Computational Mechanisms of Detached Mindfulness
This paper investigates the computational mechanisms underlying a type of metacognitive monitoring known as detached mindfulness, a particularly effective therapeutic technique within cognitive psychology. While research strongly supports the capacity of detached mindfulness to reduce depression and anxiety, its cognitive and computational underpinnings remain largely unexplained. We employ a computational model of m
Self-correction is Not An Innate Capability in Language Models
Although there has been growing interest in the self-correction capability of Large Language Models (LLMs), there are varying conclusions about its effectiveness. Prior research has largely concentrated on intrinsic self-correction, extrinsic self-correction, particularly the interplay between internal knowledge and external feedback, remains underexplored. In this paper, we aim to comprehensively investigate the und
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