Record 15052026 · captured 2026-08-25
The world looked up Wes Streeting. 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.
Wesley Paul William Streeting is a British politician who has served as Secretary of State for Defence since 2026. He previously served as Secretary of State for Health and Social Care from 2024 until his resignation in 2026. A member of the Labour Party, he h
The Eurovision Song Contest 2026 was the 70th edition of the Eurovision Song Contest. It consisted of two semi-finals on 12 and 14 May and a final on 16 May 2026, held at Wiener Stadthalle in Vienna, Austria, and presented by Victoria Swarovski and Michael Ost
Vadasseri Damodaran Satheesan is an Indian politician and lawyer who is serving as the 13th Chief Minister of Kerala since May 2026. A member of the Indian National Congress, he has represented Paravur in the Kerala Legislative Assembly since 2001.
Andrew Murray Burnham is a British politician who has served as Prime Minister of the United Kingdom and Leader of the Labour Party since July 2026. He has been Member of Parliament (MP) for Makerfield in Greater Manchester since June 2026, and was Mayor of Gr
Joshua Cameron Simons is a British politician who served as the Member of Parliament (MP) for Makerfield from 2024 to 2026. A member of the Labour Party, he served as Parliamentary Under-Secretary of State for Digital ID from January to March 2026.
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
Off Campus is an American romantic drama television series created by Louisa Levy for Amazon Prime Video. It is based on the Off-Campus book series by Elle Kennedy. The series premiered on May 13, 2026 and received positive reviews. In February 2026, ahead of
Michael Joseph Jackson was an American singer, songwriter, dancer, and philanthropist. Dubbed the "King of Pop", he is widely regarded as one of the most culturally significant figures of the 20th century. His musical achievements broke American racial barrier
The Thucydides Trap refers to the idea that when a rising power threatens to displace an established one, the result is often war. The concept originated with Herman Wouk, the novelist and World War II veteran, who used it in his Admiral Raymond A. Spruance le
Xi Jinping is a Chinese politician who is the paramount leader of China. He has served as the general secretary of the Chinese Communist Party (CCP) and chairman of the Party Central Military Commission (CMC) since 2012, and as the president of China and chair
The fifth and final season of the American satirical superhero television series The Boys, the first series in the franchise based on the comic book series of the same name created by Garth Ennis and Darick Robertson, was developed for television by Eric Kripk
The Punisher: One Last Kill is an American television special directed by Reinaldo Marcus Green and written by Jon Bernthal and Green for the streaming service Disney+, based on Marvel Comics featuring the character Punisher. It is the third Special Presentati
Makerfield is a constituency in Greater Manchester represented in the House of Commons of the UK Parliament. It is currently represented by Andy Burnham, the current Prime Minister, who was elected after Josh Simons' resignation to allow Burnham to run in a by
Barnard, Bishop & Barnards was a manufacturing and general ironmonger, initially an ironmongery started by Charles Barnard (1804-1871) on 9 November 1826 in premises near Norwich Market. By 1840 his retail business had expanded and included iron foundry worksh
Jason Paul Collins was an American professional basketball player who was a center for 13 seasons in the National Basketball Association (NBA). He played college basketball for the Stanford Cardinal, earning third-team All-American honors in 2001. Collins was
.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
Brandon Clarke was a Canadian–American professional basketball player who served as a power forward for the Memphis Grizzlies of the National Basketball Association (NBA). He played college basketball for the San Jose State Spartans and the Gonzaga Bulldogs. C
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
Survivor 50: In the Hands of the Fans
Survivor 50: In the Hands of the Fans is the 50th season of the American competitive reality television series Survivor. It premiered on February 25, 2026, on CBS in the United States, and it is the eighteenth consecutive season to be filmed in the Mamanuca Is
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
Michael is a 2026 biographical film directed by Antoine Fuqua and written by John Logan. It follows the early life of the American singer Michael Jackson, from his time with the Jackson 5 in the 1960s to the Bad World Tour in the late 1980s. Jackson is portray
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
Legends is a British crime thriller television series written and created by Neil Forsyth and produced by his Tannadice Pictures production company. It is a dramatisation of the true story of undercover British customs investigators who infiltrated the drug wo
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
The Boys is an American satirical superhero streaming television series developed by Eric Kripke for Amazon Prime Video. Based on the comic book series of the same name by Garth Ennis and Darick Robertson, it follows the eponymous team of vigilantes as they co
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
Karuppu (transl. Black) is a 2026 Indian Tamil-language fantasy action drama film directed by RJ Balaji from a screenplay he co-wrote with Ashwin Ravichandran, Rahul Raj, T. S. Gopi Krishnan and Karan Aravind Kumar. Produced by Dream Warrior Pictures, the film
Donald Richard Gibb was an American actor, best known for his roles as the hulking, dimwitted fraternity brother Frederick “Ogre” Palowaski in several installments of the Revenge of the Nerds film series, as Kumite fighter Ray Jackson in Bloodsport, and as Les
Chandrasekaran Joseph Vijay is an Indian politician and former actor who is currently serving as the ninth chief minister of Tamil Nadu since May 2026. He is the founder and president of the political party Tamilaga Vettri Kazhagam (TVK). Prior to entering pol
Limonene is a slightly yellow-green liquid aliphatic hydrocarbon classified as a cyclic monoterpene, and is the major component in the fragrance and essential oil of citrus fruit peels, taking its name from Italian limone ("lemon").
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
Vendor-Conditioned Contrastive Learning for Predicting Organizational Cyber Threat Targets
Cyberattacks cause billions of dollars in damage annually, with malicious hackers often sharing exploit code and techniques on underground forums. Identifying which organizations are targeted by these exploits is critical for proactive Cyber Threat Intelligence (CTI). To address that gap, we propose Temporal Representation and Classification of Exploits (TRACE), a vendor-conditioned contrastive learning framework bui
Change of measure through the Legendre transform
PAC-Bayes generalisation bounds are derived via change-of-measure inequalities that transfer concentration properties from a reference measure to all posterior measures. The specific choice of change of measure determines the assumptions required on the empirical risk; in particular, the classical Donsker--Varadhan theorem leads to bounds relying on bounded exponential moments. We study change-of-measure inequalities
Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions
We propose a novel backpropagation algorithm for training spiking neural networks (SNNs) that encodes information in the relative multiple spike timing of individual neurons without single-spike restrictions. The proposed algorithm inherits the advantages of conventional timing-based methods in that it computes accurate gradients with respect to spike timing, which promotes ideal temporal coding. Unlike conventional
Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface
In the field of high-energy physics, deep learning algorithms continue to gain in relevance and provide performance improvements over traditional methods, for example when identifying rare signals or finding complex patterns. From an analyst's perspective, obtaining highest possible performance is desirable, but recently, some attention has been shifted towards studying robustness of models to investigate how wel
Nonsmooth composite optimization with orthogonality constraints has a wide range of applications in statistical learning and data science. However, this problem is challenging due to its nonsmooth objective and computationally expensive nonconvex constraints. In this paper, we propose a new approach called \textbf{OBCD}, which leverages block coordinate descent to address these challenges. \textbf{OBCD} is a feasible
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics. However, a closer examination reveals that the state-of-the-art methods for high-dimensional Bayesian optimization (HDBO) suffer from degrading performance as
Distributional Principal Autoencoders
Dimension reduction techniques usually lose information in the sense that reconstructed data are not identical to the original data. However, we argue that it is possible to have reconstructed data identically distributed as the original data, irrespective of the retained dimension or the specific mapping. This can be achieved by learning a distributional model that matches the conditional distribution of data given
DAPL: Integration of Positive and Negative Descriptions in Text-Based Person Search
Text-based person search (TBPS) aims to retrieve specific images of individuals from large datasets using textual descriptions. Existing TBPS methods focus primarily on identifying explicit positive attributes, often neglecting the critical role of negative descriptions. This oversight can lead to false positives, where images that should be excluded based on negative descriptions are incorrectly included, due to par
Sequential Resource Trading Using Comparison-Based Gradient Estimation
We study sequential multi-issue trading between two greedily rational agents who exchange resources from a finite set of categories. Each agent's utility depends on its allocation, but the offering agent does not know the responding agent's utility function and receives only accept or reject feedback. We propose a comparison-based algorithm that interprets acceptance and rejection responses as pairwise state
Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes
Automatic controller tuning is attractive for robotics and mechatronic systems whose dynamics are difficult to model accurately, but direct black-box optimization can be unsafe because each query is executed on the physical plant. Existing safe Bayesian optimization (BO) methods provide high-probability safety guarantees, yet their practical use in multi-loop control is limited by two coupled difficulties: the contro
Large Language Models (LLMs) excel at many tasks but often falter on complex problems that require structured, multi-step reasoning. We introduce the Diagram of Thought (DoT), a framework that enables a single LLM to build and navigate a mental map of its reasoning. Instead of thinking in a straight line, the model constructs a dynamic diagram of ideas, where it can propose different lines of thought, critique its ow
Manikin-Recorded Cardiopulmonary Sounds Dataset Using Digital Stethoscope
Heart and lung sounds are crucial for healthcare monitoring. Recent improvements in stethoscope technology have made it possible to capture patient sounds with enhanced precision. In this dataset, we used a digital stethoscope to capture both heart and lung sounds, including individual and mixed recordings. To our knowledge, this is the first dataset to offer both separate and mixed cardiorespiratory sounds. The reco
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
Accurate uncertainty quantification in large language models (LLMs) is essential for reliable confidence estimation, yet fine-tuned LLMs often become overconfident under limited adaptation data. Existing uncertainty methods for PEFT-based LLMs are largely post hoc, estimating uncertainty after fine-tuning rather than improving how adapters specialize to task-specific input-output relationships. We propose Functional-
Conformal Prediction for Multimodal Regression
This paper introduces multimodal conformal regression. Traditionally confined to scenarios with solely numerical input features, conformal prediction is now extended to multimodal contexts through our methodology, which harnesses internal features from complex neural network architectures processing images and unstructured text. Our findings highlight the potential for internal neural network features, extracted from
Training and Evaluating Language Models with Template-based Data Generation
The rapid advancement of large language models (LLMs) such as GPT-3, PaLM, and Llama has significantly transformed natural language processing, showcasing remarkable capabilities in understanding and generating language. However, a fundamental bottleneck persists: these models often struggle with tasks requiring complex, multi-step reasoning, particularly in mathematical problem-solving. This deficiency stems from th
How well behaved is finite dimensional Diffusion Maps?
Under a set of assumptions on a family of submanifolds $\subset {\mathbb R}^D$, we derive a series of geometric properties that remain valid after finite-dimensional and almost isometric Diffusion Maps (DM), including almost uniform density, finite polynomial approximation and reach. Leveraging these properties, we establish rigorous bounds on the embedding errors introduced by the DM algorithm is $O\left((\frac{\log
We present a novel class of projected gradient (PG) methods for minimizing a smooth but not necessarily convex function over a convex compact set. We first provide a novel analysis of the constant-stepsize PG method, achieving the best-known iteration complexity for finding an approximate stationary point of the problem. We then develop an "auto-conditioned" projected gradient (AC-PG) variant that achieves th
The Potential of Convolutional Neural Networks for Cancer Detection
Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different cancers have been identified in most of these advances that effectively use CNNs (Convolutional Neural Networks) for classification. The distinct architectures of CNNs used in each study concentrate on pattern recognition for different types of cancer across various datas
Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)
As foundation AI models continue to increase in size, an important question arises - is massive scale the only path forward? This survey of about 160 papers presents a family of Small Language Models (SLMs) in the 1 to 8 billion parameter range that demonstrate smaller models can perform as well, or even outperform large models. We explore task agnostic, general purpose SLMs, task-specific SLMs and techniques to crea
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model -- Hunyuan3D-DiT, and a large-scale texture synthesis model -- Hunyuan3D-Paint. The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly aligns with a
A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In contrast, information-theoretic acquisition functions aim to reduce uncertainty about the function's optimum and are often considered fundamentally distinct from EI. In this work, we challenge this prevailing perspective by introducing
DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
The performance of an LLM depends heavily on the relevance of its training data to the downstream evaluation task. However, in practice, the data involved in an unseen evaluation task is often unknown (e.g., conversations between an LLM and a user are end-to-end encrypted). Hence, it is unclear what data are relevant for fine-tuning the LLM to maximize its performance on the specific unseen evaluation task. Instead,
Numerical simulations of complex multiphysics systems, such as char combustion considered herein, yield numerous state variables that inherently exhibit physical constraints. This paper presents a new approach to augment Operator Inference -- a methodology within scientific machine learning that enables learning from data a low-dimensional representation of a high-dimensional system governed by nonlinear partial diff
Exploring Exploration in Bayesian Optimization
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches - observation traveling salesman distance and observation entropy - to quantify the exploration characteristic
Understanding High-Dimensional Bayesian Optimization
Recent work reported that simple Bayesian optimization (BO) methods perform well for high-dimensional real-world tasks, seemingly contradicting prior work and tribal knowledge. This paper investigates why. We identify underlying challenges that arise in high-dimensional BO and explain why recent methods succeed. Our empirical analysis shows that vanishing gradients caused by Gaussian process (GP) initialization schem
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