Record 03062026 · captured 2026-08-25
The world looked up Murder of Henry Nowak. 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.
On 3 December 2025, Henry Nowak, an 18‑year‑old university student, was murdered in Southampton, Hampshire, England, by 23‑year‑old Vickrum Singh Digwa. Police bodycam footage showing officers arresting Nowak as he lay dying from stab wounds sparked public out
Backrooms is a 2026 American science fiction psychological horror film directed and co-scored by Kane Parsons, and written by Will Soodik. It is based on Parsons's web series which was inspired by the "Backrooms" creepypasta. In the film, Clark, a furniture st
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 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
.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
Euphoria is an American psychological drama television series created and written by Sam Levinson for HBO. Based on the Israeli miniseries of the same name created by Ron Leshem, the series stars Zendaya as drug-addicted teenager Rue Bennett, who also serves a
William John Pulte is an American businessman who has served as the director of the Federal Housing Finance Agency (FHFA) and the chairman of Fannie Mae and Freddie Mac since 2025. Pulte served as the acting director of national intelligence from June to Augus
Callum Robilliard Turner is a British actor. After working as a fashion model, he began working in film and television. He had lead roles in the drama film Queen and Country (2014) and the mystery drama series Glue (2014), and played Theseus, the brother of Ne
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
Kane Parsons, also known as Kane Pixels, is an American YouTuber, composer, filmmaker and visual effects artist. In January 2022, Parsons began publishing the viral web series Backrooms, based on the creepypasta of the same name, to his YouTube channel. He dir
The Backrooms is a fictional location invented in a 2019 thread on the imageboard website 4chan. The Backrooms are usually portrayed as an impossibly large extradimensional complex of empty rooms, accessed by exiting reality. They are one of the best-known exa
Dua Lipa is an English singer and songwriter. Her accolades include seven Brit Awards and three Grammy Awards.
Richard Leonard Adelman was an American professional basketball player and coach. He coached 23 seasons in the National Basketball Association (NBA). He served as head coach of the Portland Trail Blazers, Golden State Warriors, Sacramento Kings, Houston Rocket
Spencer William Pratt is an American reality television personality. In 2007, he began dating Heidi Montag, a primary cast member of the reality television series The Hills and came to prominence after being cast in the series. A feud between them and Montag's
Streptocarpus teitensis, synonym Saintpaulia teitensis, is a species of Streptocarpus in the section Saintpaulia. It is endemic to 1 square kilometer on Mbololo Hill in the Taita Hills of southern Kenya. The total population is estimated at less than 2,500 ind
Spider-Noir is an American superhero series developed by Oren Uziel for MGM+ and Prime Video. Based on Marvel Comics featuring the character Spider-Man Noir, the series follows an aging private investigator and superhero in 1930s New York City who grapples wit
Šenturška Gora is a small settlement in the Municipality of Cerklje na Gorenjskem in the Upper Carniola region of Slovenia.
Blast is a 2026 Indian Tamil-language action thriller film directed by Subash K. Raj in his debut and produced by AGS Entertainment. The film stars Arjun Sarja, Abhirami and Preity Mukhundhan, with John Kokken, Vivek Prasanna, Arjun Chidambaram, Dileepan and P
Andoni Iraola Sagarna is a Spanish professional football manager and former player who is the head coach of Premier League club Liverpool.
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 third and final season of the American psychological drama television series Euphoria, inspired by Ron Leshem's miniseries of the same name, premiered on HBO on April 12, 2026. Series creator Sam Levinson serves as showrunner for the season. The season cen
Myles Lorenz Garrett is an American professional football outside linebacker for the Los Angeles Rams of the National Football League (NFL). He played college football for the Texas A&M Aggies, earning the Bill Willis Trophy and first-team All-American honors
Jakub Menšík is a Czech professional tennis player. He has a career-high ATP singles ranking of world No. 12, achieved on 2 March 2026, and a doubles ranking of No. 271, achieved on 18 August 2025. Menšík's best result is reaching the semifinal of the 2026 Fre
Arthur Juan Brown is an American professional football wide receiver for the New England Patriots of the National Football League (NFL). He played college football for the Ole Miss Rebels, twice earning first-team All-SEC honors. Brown was selected by the Tenn
Conor Angus Cloud Hickey was an American actor. He was best known for his role as Fezco O'Neill in the HBO drama series Euphoria (2019–2022), and had roles in the films North Hollywood (2021), The Line (2023), Abigail and The Garfield Movie. He also appeared i
The 2026 FIFA World Cup was an international football tournament held in Canada, Mexico, and the United States, from June 11 to July 19, 2026. The 48 national teams involved in the tournament were required to register a squad of up to 26 players, including thr
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
Danielle Fabiola "Inde" Navarrette is an American actress and former online streamer. She began her acting career as a teenager with roles in short films, before landing roles in the Netflix drama series 13 Reasons Why (2020) and The CW's superhero drama serie
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
Jared Isaiah Verse is an American professional football defensive end for the Cleveland Browns of the National Football League (NFL). He played college football for the Albany Great Danes and Florida State Seminoles. Verse was the 2020 CAA Defensive Rookie of
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
On Reductions of Hintikka Sets for Higher-Order Logic
Steen's (2018) Hintikka set properties for Church's type theory based on primitive equality are reduced to the Hintikka set properties of Brown (2007). Using this reduction, a model existence theorem for Steen's properties is derived.
Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models
The choice of \emph{which} tokens to mask is a central, under-examined design decision in masked language modeling (MLM). Standard pretraining masks tokens uniformly at random, but several studies show that more informative masking targets can improve downstream performance. We study masking as a \emph{task-adaptive} component of the fine-tuning pipeline and introduce \textbf{Typhoon}, a masking strategy that uses th
Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy. In this work, we present a probabilistic guarantee for power flow learning and voltage risk estimation, derived through the framework of Gaussian Process (GP) regression. Specifically, we establish a bound on the e
Node Perturbation Can Effectively Train Multi-Layer Neural Networks
Backpropagation (BP) remains the dominant and most successful method for training parameters of deep neural network models. However, BP relies on two computationally distinct phases, does not provide a satisfactory explanation of biological learning, and can be challenging to apply for training of networks with discontinuities or noisy node dynamics. By comparison, node perturbation (NP), also known as activity-pertu
Evaluating the Reversal Curse in Model Editing
Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge. Since retraining LLMs is resource intensive, there has been a growing interest in model editing. Despite the emergence of benchmarks and approaches, existing unidirectional editing and evaluation paradigms have failed to explore the reversal curse. In this paper, we study bidirectional language model editing, aim
Planning with Uncertainty: Symmetries, Policy Inference, and Solution Compression
Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty. It models uncertainty through actions with non-deterministic effects. In this work, we present a collection of techniques that establish explicit best-first policy-space search as a method competitive with the state of the art for solving FOND planning tasks. We study how to define equivalence relat
Evolving Collective Behavior in Self-Organizing Particle Systems
Local interactions drive emergent collective behavior, which pervades biological and social complex systems. But uncovering the interactions that produce a desired behavior remains a core challenge. In this paper, we present EvoSOPS, an evolutionary framework that searches landscapes of stochastic distributed algorithms for those that achieve a mathematically specified target behavior. These algorithms govern self-or
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that multiple annotators, e.g., crowdworkers, typically provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels
Word-specific tonal realizations in Mandarin
The pitch contours of Mandarin two-character words are generally understood as being shaped by the underlying tones of the constituent single-character words, in interaction with articulatory constraints imposed by factors such as speech rate, co-articulation with adjacent tones, segmental make-up, and predictability. This study shows that tonal realization is also partially determined by words' meanings. We firs
Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+
Semidefinite programs (SDPs) and their solvers are powerful tools with many applications in machine learning and data science. Designing scalable SDP solvers is challenging because by standard the positive semidefinite decision variable is an $n \times n$ dense matrix, even though the input is often an $n \times n$ sparse matrix. However, the solution may not require a full-rank matrix, as shown by Barvinok and Patak
GS-ROR$^2$: Bidirectional-guided 3DGS and SDF for Reflective Object Relighting and Reconstruction
3D Gaussian Splatting (3DGS) has shown a powerful capability for novel view synthesis due to its detailed expressive ability and highly efficient rendering speed. Unfortunately, creating relightable 3D assets and reconstructing faithful geometry with 3DGS is still problematic, particularly for reflective objects, as its discontinuous representation raises difficulties in constraining geometries. Volumetric signed dis
An Improved Method for Personalizing Diffusion Models
Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enhance model individualization using specific images. These methods enable generating images of specific objects based on diverse textual contexts. Our proposed approach aims to retain the model's original knowledge during new information integration, resulting in superi
Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilistic inference efficiently using the example of Statistical EL (SEL), a statistical extension of the lightweight Description Logic EL. We provide proofs for runtime and soundness guarantees, and empirically evaluate the runtime and approximati
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
Learning models whose predictions are invariant under multiple environments is a promising approach for out-of-distribution generalization. Such models are trained to extract features $X_{\text{inv}}$ where the conditional distribution $Y \mid X_{\text{inv}}$ of the label given the extracted features does not change across environments. Invariant models are also supposed to generalize to shifts in the marginal distri
PINNfluence: Interpreting PINNs through Influence Functions
Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their behavior remains largely opaque and is typically understood through failure mode analyses rather than explicit interpretability. To address this issue, we introduce PINNfluence, a training data attribution framework for interpreting PINNs base
Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability
Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications. Surrogate Optimization (SO) is a common resolution, yet its proprietary nature introduced by the complexity of surrogate models and the sampling core (e.g., acquisition functions) often leads to a lack of explainability and transparency. While existing literature has primarily
ResCLIP: Residual Attention for Training-free Dense Vision-language Inference
While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-level tasks, and they still struggle with dense predictions. Recent works often attribute such deficiency in dense predictions to the self-attention layers in the final block, and have achieved commendable results by modifying the original query-key attention to self-correlat
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model Enhancement
Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capable aritificial intelligence on mobile devices also arises, such as the AI assistant software. Some efforts try to migrate VLMs to edge devices to expand their application scope. Simplifying the model structure is a common method, but as the model shrinks, the trade-off between
Power- and Fragmentation-aware Online Scheduling for GPU Datacenters
The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental footprint of large-scale computing infrastructures. This work addresses the online scheduling problem in GPU datacenters, which involves scheduling tasks without knowledge of their future arrivals. We f
The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and predictive accuracy. This paper presents an optimization framework that combines Retrieval-Augmented Generation (RAG) with an innovative multi-head early exit architecture to concurrently enhance both aspects. By integrating Graph Convoluti
ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization
Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts. To address this issue, we uncover the Satisficing Generalization Edge, which we validate both theor
Position: Adversarial ML for LLMs Is Not Making Any Progress
In the past decade, considerable research effort has been devoted to securing machine learning (ML) models that operate in adversarial settings. Yet, progress has been slow even for simple "toy" problems (e.g., robustness to small adversarial perturbations) and is often hindered by non-rigorous evaluations. Today, adversarial ML research has shifted towards studying larger, general-purpose language models. In
ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction
Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science. Unlike molecules, crystal structures exhibit infinite periodic arrangements of atoms, requiring methods capable of capturing both local and global information effectively. However, current works fall short of capturing long-range interactions within periodic structures. To address this, we leverage \emp
Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation
Knowledge of the primordial matter density field from which the large-scale structure of the Universe emerged over cosmic time is of fundamental importance for cosmology. However, reconstructing these cosmological initial conditions from late-time observations is a notoriously difficult task, which requires advanced cosmological simulators and sophisticated statistical methods to explore a multi-million-dimensional p
Greed is Good: A Unifying Perspective on Guided Generation
Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of flow/diffusion models. Generally speaking, two families of techniques have emerged for solving this problem for gradient-based guidance: namely, posterior guidance (i.e., guidance via projecting the current sample to the target distribution via the target prediction
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