Record 18032026 · captured 2026-08-25
The world looked up Joe Kent. 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.
Joseph Clay Kent is an American politician, former United States Army warrant officer, and former Central Intelligence Agency paramilitary officer who served as the director of the National Counterterrorism Center from 2025 to 2026. A member of the Republican
Saint Patrick's Day, or the Feast of Saint Patrick, is a religious and cultural holiday held on 17 March, the traditional death date of Saint Patrick, the foremost patron saint of Ireland.
Ali Ardashir Larijani was an Iranian politician, military officer, and philosopher who served as the secretary of the Supreme National Security Council from 2025 until his assassination in 2026. He had previously served in the position from 2005 to 2007. From
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
Michael Bakari Jordan is an American actor, producer, and director. His accolades include an Academy Award, three Actor Awards, and a Producers Guild Award, in addition to nominations for a British Academy Film Award, a Golden Globe Award and two Emmy Awards.
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
Shannon Mary Kent was a United States Navy cryptologic technician and member of JSOC's Intelligence Support Activity who was killed in the 2019 Manbij bombing. She was the wife of Joe Kent, who entered politics in response to her death.
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
Saint Patrick was a fifth-century Romano-British Christian missionary and bishop in Ireland. Known as the "Apostle of Ireland", he is the primary patron saint of Ireland, the other patron saints being Brigid of Kildare and Columba. He is also the patron saint
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.
Benjamin Netanyahu, nicknamed "Bibi", is an Israeli politician and diplomat who has served as Prime Minister of Israel since 2022. Having previously held office from 1996 to 1999 and from 2009 to 2021, Netanyahu is Israel's longest-serving prime minister.
Dune: Part Three is an upcoming American epic space opera film co-produced and directed by Denis Villeneuve, who co-wrote the screenplay with Brian K. Vaughan. Based on the 1969 novel Dune Messiah by Frank Herbert, it is the sequel to Dune: Part Two (2024) and
Amy Marie Madigan is an American actress. Known for her work on stage and screen, her accolades include an Academy Award, an Actor Award, a Golden Globe Award, and a Critics' Choice Award, in addition to a nomination for a Primetime Emmy Award.
Banksy is a pseudonymous England-based street artist, political activist, and film director. He has never publicly confirmed his identity. Active since the 1990s, his satirical street art and subversive epigrams combine dark humour with graffiti executed in a
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
Paul Thomas Anderson, also known by his initials PTA, is an American filmmaker. Often described as one of the preeminent filmmakers of his generation, he is the recipient of numerous accolades, including three Academy Awards, three Golden Globe Awards and four
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
Timothée Hal Chalamet is an American and French actor. Known for his work in a diverse range of blockbusters and independent films, he is the recipient of numerous accolades including an Actor Award, a Golden Globe Award, and two Critics' Choice Awards, in add
Sean Justin Penn is an American actor and filmmaker. He is known for his intense leading man roles in film. His accolades include three Academy Awards, a Golden Globe Award, a British Academy Film Award, and nominations for an Emmy Award and a Grammy Award. He
Matt Clark was an American actor. He was best known for his roles in Western films.
Weapons is a 2025 American supernatural mystery horror film directed, written, produced, and co-scored by Zach Cregger. It stars an ensemble cast including Josh Brolin, Julia Garner, Alden Ehrenreich, Austin Abrams, Cary Christopher, Toby Huss, Benedict Wong,
Chiquita Renee Shepard, known professionally as Kiki Shepard, was an American television host, actress and dancer best known as the co-host of Showtime at the Apollo from 1987 to 2002.
Ryan Kyle Coogler is an American filmmaker. His accolades include an Academy Award, a British Academy Film Award, a Grammy Award, a Golden Globe Award, ten Black Reel Awards, and fourteen NAACP Image Awards.
Zendaya Maree Stoermer Coleman, known mononymously as Zendaya, is an American actress and singer-songwriter. Known for her work in television and blockbusters, her films as a leading actress have grossed over $9.8 billion worldwide. Her accolades include two P
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
A company is a legal entity representing an association of legal persons with a shared objective, such as generating profit or benefiting society. Depending on the jurisdiction, companies can take on various forms, including voluntary associations, nonprofit o
2026 West Bengal Legislative Assembly election
Legislative Assembly elections were held in West Bengal to elect all 294 members of the West Bengal Legislative Assembly in two phases on 23 and 29 April 2026, with the votes counted and results for 293 seats released on 4 May 2026. The election saw the defeat
Dune Messiah is a 1969 science fiction novel by American writer Frank Herbert, the second in his Dune series of six novels. A sequel to Dune (1965), it was originally serialized in Galaxy magazine in 1969, and then published by Putnam the same year.
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
What the people building things argued about, from Hacker News.
Papers submitted to arXiv cs.AI that day.
General Mechanism of Evolution Shared by Proteins and Words
Complex systems, such as life and languages, are governed by principles of evolution. The analogy and comparison between biology and linguistics\cite{alphafold2, RoseTTAFold, lang_virus, cell language, faculty1, language of gene, Protein linguistics, dictionary, Grammar of pro_dom, complexity, genomics_nlp, InterPro, language modeling, Protein language modeling} provide a computational foundation for characterizing a
Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other. By using a squared differential len
An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constants
In this work, we present a new deterministic partition-based global optimization algorithm, HALO (Hybrid Adaptive Lipschitzian Optimization), which uses estimates of the local Lipschitz constants associated with different sub-regions of the objective function's domain to compute lower bounds and guide the search toward global minimizers. These estimates are obtained by adaptively balancing the global and local in
Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks
Heterogeneous molecular entities and their interactions, commonly depicted as a network, are crucial for advancing our systems-level understanding of biology. With recent advancements in high-throughput data generation and a significant improvement in computational power, graph neural networks (GNNs) have demonstrated their effectiveness in predicting biomedical interactions. Since GNNs follow a neighborhood aggregat
Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing
Algorithmic auditing has become central to platform accountability under frameworks such as the AI Act and the Digital Services Act. In practice, this obligation is discharged through dedicated Audit APIs. This architecture creates a paradox: the entity under scrutiny controls the evaluation interface. A platform facing legal sanctions can serve a compliant surrogate model on its Audit API, while running a discrimina
From structure mining to unsupervised exploration of atomic octahedral networks
Understanding the spatial arrangements of atom-centered coordination octahedra is crucial for relating structures to properties for many materials families. Traditional case-by-case inspection becomes a prohibitive task for discovering trends and similarities in large datasets. Here, we operationalize chemical intuition to automate the geometric parsing, quantification, and classification of coordination octahedral n
Images degraded by geometric distortions pose a significant challenge to imaging and computer vision tasks such as object recognition. Deep learning-based imaging models usually fail to give accurate performance for geometrically distorted images. In this paper, we propose the deformation-invariant neural network (DINN), a framework to address the problem of imaging tasks for geometrically distorted images. The DINN
High-Quality Facial Geometry and Appearance Capture at Home
Facial geometry and appearance capture have demonstrated tremendous success in 3D scanning real humans in studios. Recent works propose to democratize this technique while keeping the results high quality. However, they are still inconvenient for daily usage. In addition, they focus on an easier problem of only capturing facial skin. This paper proposes a novel method for high-quality face capture, featuring an easy-
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In this paper, we derive several non-asymptotic upper bound on SMOTE density. From these results, we prove that SMOTE (with default parameter) tends to copy the original minority samples asymptotically. We confirm and illustrate empirically this
For robotic surgical videos, instrument presence annotations are typically recorded with video streams, which offering the potential to reduce the manually annotated costs for segmentation. However, weakly supervised surgical instrument segmentation with only instrument presence labels has been rarely explored in surgical domain due to the highly under-constrained challenges. Temporal properties can enhance represent
Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method
Gaze plays a crucial role in revealing human attention and intention, particularly in hand-object interaction scenarios, where it guides and synchronizes complex tasks that require precise coordination between the brain, hand, and object. Motivated by this, we introduce a novel task: Gaze-Guided Hand-Object Interaction Synthesis, with potential applications in augmented reality, virtual reality, and assistive technol
Agnostic Tomography of Stabilizer Product States
We define a quantum learning task called agnostic tomography, where given copies of an arbitrary state $ρ$ and a class of quantum states $\mathcal{C}$, the goal is to output a succinct description of a state that approximates $ρ$ at least as well as any state in $\mathcal{C}$ (up to some small error $\varepsilon$). This task generalizes ordinary quantum tomography of states in $\mathcal{C}$ and is more challenging be
RGBT tracking draws increasing attention because its robustness in multi-modal warranting (MMW) scenarios, such as nighttime and adverse weather conditions, where relying on a single sensing modality fails to ensure stable tracking results. However, existing benchmarks predominantly contain videos collected in common scenarios where both RGB and thermal infrared (TIR) information are of sufficient quality. This weake
For translational impact, both accurate drug response prediction and biological plausibility of predictive features are needed. We present drGT, a heterogeneous graph deep learning model over drugs, genes, and cell lines that couples prediction with mechanism-oriented interpretability via attention coefficients (ACs). We assess both predictive generalization (random, unseen-drug, unseen-cell, and zero-shot splits) an
Revisiting ASR Error Correction with Specialized Models
Language models play a central role in automatic speech recognition (ASR), yet most methods rely on text-only models unaware of ASR error patterns. Recently, large language models (LLMs) have been applied to ASR correction, but introduce latency and hallucination concerns. We revisit ASR error correction with compact seq2seq models, trained on ASR errors from real and synthetic audio. To scale training, we construct
Convergence Bounds for Sequential Monte Carlo on Multimodal Distributions using Soft Decomposition
We prove bounds on the variance of a function $f$ under the empirical measure of the samples obtained by the Sequential Monte Carlo (SMC) algorithm, with time complexity depending on local rather than global Markov chain mixing dynamics. SMC is a Markov Chain Monte Carlo (MCMC) method, which starts by drawing $N$ particles from a known distribution, and then, through a sequence of distributions, re-weights and re-sam
LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
Greybox fuzzing has achieved success in revealing bugs and vulnerabilities in programs. However, randomized mutation strategies have limited the fuzzer's performance on structured data. Specialized fuzzers can handle complex structured data, but require additional efforts in grammar and suffer from low throughput. In this paper, we explore the potential of utilizing the Large Language Model to enhance greybox fuz
Making Multi-Axis Gaussian Graphical Models Scalable to Millions of Cells
Motivation: Networks underlie the generation and interpretation of many biological datasets: gene networks shed light on the regulatory structure of the genome, and cell networks can capture structure of the tumor micro-environment. However, most methods that learn such networks make the faulty 'independence assumption'; to learn the gene network, they assume that no cell network exists. 'Multi-axis'
SG-DeepONet: Source-generalized deep operator learning for full waveform inversion
Full waveform inversion (FWI) aims to reconstruct subsurface velocity models from observed seismic wavefields and has recently benefited from advances in deep learning (DL). The performance of DL-based FWI critically depends on the diversity of training data, yet existing datasets such as OpenFWI rely on fixed or weakly varying source conditions, limiting their ability to represent realistic seismic scenarios and hin
In Standard Chinese, Tone 3 (the dipping tone) becomes Tone 2 (rising tone) when followed by another Tone 3. Previous studies have noted that this sandhi process may be incomplete, in the sense that the assimilated Tone 3 is still distinct from a true Tone 2. While Mandarin Tone 3 sandhi is widely studied using carefully controlled laboratory speech (Xu, 1997) and more formal registers of Beijing Mandarin (Yuan &
Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) exhibit drug resistance, urging the need to develop new treatments. A key part in anti-seizure medication (ASM) development is the capability of detecting and quantifying epileptic seizures occurring in electroencephalogram (EEG) signals, which is crucial for treatment effi
Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network
Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences. However, contemporary QoS prediction methods frequently encounter data sparsity and cold-start issues, which hinder accurate QoS predictions and limit the ability to ca
EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation
While post-training compression techniques effectively reduce the memory footprint, latency, and power consumption of Large Language Models (LLMs), they often result in noticeable accuracy degradation and remain limited by hardware and kernel constraints that restrict supported compression formats - ultimately reducing flexibility across a wide range of deployment scenarios. In this work, we propose EoRA - a novel $\
A-UTE: Advection Informed, Uncertainty Aware Temperature Emulator
Physics-based Earth system models (ESMs) are essential for attributing climate change and generating scenario projections, yet their reliance on high-resolution numerical integration makes multi-decadal experiments expensive. In parallel, deep learning has delivered strong gains in short-range weather forecasting; however, auto-regressive roll-outs can accumulate error and become unstable when extended to decade-scal
Reconstructing controllable Gaussian splats for articulated objects from monocular video is especially challenging due to its inherently insufficient constraints. Existing methods address this by relying on dense masks and manually defined control signals, limiting their real-world applications. In this paper, we propose an annotation-free method, FreeGaussian, which mathematically disentangles camera egomotion and a
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