{"date":"2026-05-11","schema_version":1,"captured_at":"2026-08-25T23:08:09.074Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"C. Joseph Vijay","views":731537,"rank":3,"extract":"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","url":"https://en.wikipedia.org/wiki/C._Joseph_Vijay","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/d8/JosephVijay.jpg/330px-JosephVijay.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Chief Minister of Tamil Nadu","views":322104,"rank":4,"extract":"The chief minister of Tamil Nadu is the head of government of the Indian state of Tamil Nadu. 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Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs). The algorithm decomposes the time domain into a set of overlapping subdomains, and solves all subproblems defined over subdomains in parallel. The convergence is attained by updating primal-dual information at the boundaries of overlapping subdomains. We show that the algorithm ","authors":["Na, Sen","Shin, Sungho","Anitescu, Mihai","Zavala, Victor M."],"url":"https://arxiv.org/abs/2005.06674","arxiv_id":"2005.06674","published":"2026-05-11"},{"title":"HIVE-COTE 2.0: a new meta ensemble for time series classification","abstract":"The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR ti","authors":["Middlehurst, Matthew","Large, James","Flynn, Michael","Lines, Jason","Bostrom, Aaron","Bagnall, Anthony"],"url":"https://arxiv.org/abs/2104.07551","arxiv_id":"2104.07551","published":"2026-05-11"},{"title":"Multi-Stage Prototype Learning for Interpretable Time Series Classification","abstract":"Deep learning methods are powerful tools in classifying multivariate time series data. Despite their high performance, these methods are hard to interpret, which diminishes their applications in high-risk domains such as healthcare. In this paper, we propose a novel multi-stage prototype learning framework for multivariate time series classification. By design, our framework identifies predictive temporal patterns in","authors":["Kalisetti, Bhavesh","Wang, Vincent","Ghosal, Gaurav R.","Bijanzadeh, Maryam","Abbasi-Asl, Reza"],"url":"https://arxiv.org/abs/2106.09636","arxiv_id":"2106.09636","published":"2026-05-11"},{"title":"Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems","abstract":"This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distributed controller, called $κ$-distributed control, which lets the agents make control decisions based on the state information within distance $κ$ on the underlying graph. This controller can tu","authors":["Shin, Sungho","Lin, Yiheng","Qu, Guannan","Wierman, Adam","Anitescu, Mihai"],"url":"https://arxiv.org/abs/2204.05551","arxiv_id":"2204.05551","published":"2026-05-11"},{"title":"Bake off redux: a review and experimental evaluation of recent time series classification algorithms","abstract":"In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a `bake off', identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of featur","authors":["Middlehurst, Matthew","Schäfer, Patrick","Bagnall, Anthony"],"url":"https://arxiv.org/abs/2304.13029","arxiv_id":"2304.13029","published":"2026-05-11"},{"title":"Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression","abstract":"Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the compar","authors":["Guijo-Rubio, David","Middlehurst, Matthew","Arcencio, Guilherme","Silva, Diego Furtado","Bagnall, Anthony"],"url":"https://arxiv.org/abs/2305.01429","arxiv_id":"2305.01429","published":"2026-05-11"},{"title":"On Diffusion Modeling for Anomaly Detection","abstract":"Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By","authors":["Livernoche, Victor","Jain, Vineet","Hezaveh, Yashar","Ravanbakhsh, Siamak"],"url":"https://arxiv.org/abs/2305.18593","arxiv_id":"2305.18593","published":"2026-05-11"},{"title":"Multispectral Indices for Wildfire Management","abstract":"The increasing frequency and severity of wildfires necessitates advanced methods for effective surveillance and management, as traditional ground-based techniques often struggle to adapt to rapidly changing fire behavior and environmental conditions. This study investigates the use of multispectral aerial and satellite imagery for wildfire management through an assessment of current literature and two practical case ","authors":["Oliveira, Afonso","Matos-Carvalho, João P.","Moutinho, Filipe","Fachada, Nuno"],"url":"https://arxiv.org/abs/2309.01751","arxiv_id":"2309.01751","published":"2026-05-11"},{"title":"Learning quantum Hamiltonians at any temperature in polynomial time","abstract":"We study the problem of learning a local quantum Hamiltonian $H$ given copies of its Gibbs state $ρ= e^{-βH}/\\textrm{tr}(e^{-βH})$ at a known inverse temperature $β>0$. Anshu, Arunachalam, Kuwahara, and Soleimanifar (arXiv:2004.07266) gave an algorithm to learn a Hamiltonian on $n$ qubits to precision $ε$ with only polynomially many copies of the Gibbs state, but which takes exponential time. Obtaining a computati","authors":["Bakshi, Ainesh","Liu, Allen","Moitra, Ankur","Tang, Ewin"],"url":"https://arxiv.org/abs/2310.02243","arxiv_id":"2310.02243","published":"2026-05-11"},{"title":"Causal Unsupervised Semantic Segmentation","abstract":"Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations. With the advent of self-supervised pre-training, various frameworks utilize the pre-trained features to train prediction heads for unsupervised dense prediction. However, a significant challenge in this unsupervised setup is determining the appropriate level of clustering required for segmenting concep","authors":["Kim, Junho","Lee, Byung-Kwan","Ro, Yong Man"],"url":"https://arxiv.org/abs/2310.07379","arxiv_id":"2310.07379","published":"2026-05-11"},{"title":"Active teacher selection for reward learning","abstract":"Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assumption that all feedback comes from a single human teacher, despite gathering feedback from large and heterogeneous populations. We propose the Hidden Utility Bandit (HUB) framework to model differences in teacher rationality, expertise, and costliness, formalizing the pr","authors":["Freedman, Rachel","Svegliato, Justin","Wray, Kyle","Russell, Stuart"],"url":"https://arxiv.org/abs/2310.15288","arxiv_id":"2310.15288","published":"2026-05-11"},{"title":"Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia","abstract":"Few studies have investigated the diagnostic utilities of biomarkers for predicting bacteremia among septic patients admitted to intensive care units (ICU). Therefore, this study evaluated the prediction power of laboratory biomarkers to utilize those markers with high performance to optimize the predictive model for bacteremia. This retrospective cross-sectional study was conducted at the ICU department of Gyeongsan","authors":["Baek, Sangwon","Lee, Seung Jun"],"url":"https://arxiv.org/abs/2311.08433","arxiv_id":"2311.08433","published":"2026-05-11"},{"title":"Structure learning of Hamiltonians from real-time evolution","abstract":"We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply $e^{-\\mathrm{i} Ht}$ for an unknown local Hamiltonian $H = \\sum_{a = 1}^m λ_a E_a$ on $n$ qubits, the goal is to recover $H$. This problem is already well-understood under the assumption that the interaction terms, $E_a$, are given, and only the interaction strengths, $λ_a$, are unknown. But how efficiently can","authors":["Bakshi, Ainesh","Liu, Allen","Moitra, Ankur","Tang, Ewin"],"url":"https://arxiv.org/abs/2405.00082","arxiv_id":"2405.00082","published":"2026-05-11"},{"title":"Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference","abstract":"Urban land use inference is a critically important task that aids in city planning and policy-making. Recently, the increased use of sensor and location technologies has facilitated the collection of multi-modal mobility data, offering valuable insights into daily activity patterns. Many studies have adopted advanced data-driven techniques to explore the potential of these multi-modal mobility data in land use infere","authors":["Zhai, Xuehao","Jiang, Junqi","Dejl, Adam","Rago, Antonio","Guo, Fangce","Toni, Francesca"],"url":"https://arxiv.org/abs/2406.13724","arxiv_id":"2406.13724","published":"2026-05-11"},{"title":"Annealing-based approach to solving partial differential equations","abstract":"Solving partial differential equations (PDEs) using an annealing-based approach involves solving generalized eigenvalue problems. Discretizing a PDE yields a system of linear equations (SLE). Solving an SLE can be formulated as a general eigenvalue problem, which can be transformed into an optimization problem with an objective function given by a generalized Rayleigh quotient. The proposed algorithm requires iterati","authors":["Kudo, Kazue"],"url":"https://arxiv.org/abs/2406.17364","arxiv_id":"2406.17364","published":"2026-05-11"},{"title":"Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms","abstract":"Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms. Is providing LLMs with community rules enough for models to follow these norms? We evaluate LLMs' capacity to detect (Task 1) and correct (Task 2) biased Wikipedia edits according to Wikipedia's Neutral Point of View (NPOV) policy. LLMs struggled with bias detection, achieving only 64% accuracy on a ","authors":["Ashkinaze, Joshua","Guan, Ruijia","Kurek, Laura","Adar, Eytan","Budak, Ceren","Gilbert, Eric"],"url":"https://arxiv.org/abs/2407.04183","arxiv_id":"2407.04183","published":"2026-05-11"},{"title":"Proximal Policy Distillation","abstract":"We introduce Proximal Policy Distillation (PPD), a novel policy distillation method that integrates student-driven distillation and Proximal Policy Optimization (PPO) to increase sample efficiency and to leverage the additional rewards that the student policy collects during distillation. To assess the efficacy of our method, we compare PPD with two common alternatives, student-distill and teacher-distill, over a wid","authors":["Spigler, Giacomo"],"url":"https://arxiv.org/abs/2407.15134","arxiv_id":"2407.15134","published":"2026-05-11"},{"title":"ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation","abstract":"Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However, we observe that when adopting CLIP to such a pixel-level understanding task, unexpected bias (including class-preference bias and space-preference bias) occurs. Previous works don't explicitly model the bias, which largely constrains the segmentation performanc","authors":["Wang, Jingyun","Kang, Guoliang"],"url":"https://arxiv.org/abs/2408.06747","arxiv_id":"2408.06747","published":"2026-05-11"},{"title":"Optimising MFCC parameters for the automatic detection of respiratory diseases","abstract":"Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respiratory diseases. Among the employed acoustic features, Mel Frequency Cepstral Coefficients (MFCC) is widely used for automatic analysis, with MFCC extraction commonly relying on default parameters. However, no comprehensive study has systematically investigated the impact of MFCC ","authors":["Yan, Yuyang","Simons, Sami O.","van Bemmel, Loes","Reinders, Lauren","Franssen, Frits M. E.","Urovi, Visara"],"url":"https://arxiv.org/abs/2408.07522","arxiv_id":"2408.07522","published":"2026-05-11"},{"title":"Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise","abstract":"Inspired by the idea of Positive-incentive Noise (Pi-Noise or $π$-Noise) that aims at learning the reliable noise beneficial to tasks, we scientifically investigate the connection between contrastive learning and $π$-noise in this paper. By converting the contrastive loss to an auxiliary Gaussian distribution to quantitatively measure the difficulty of the specific contrastive model under the information theory frame","authors":["Zhang, Hongyuan","Xu, Yanchen","Huang, Sida","Li, Xuelong"],"url":"https://arxiv.org/abs/2408.09929","arxiv_id":"2408.09929","published":"2026-05-11"},{"title":"Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature","abstract":"Physics seeks to uncover the laws of Nature and express them through mathematical equations. Despite the vast diversity of natural phenomena, physical equations exhibit structural regularities that set them apart from arbitrary mathematical expressions. While principles such as dimensional analysis have long guided the formulation of physical models, the exploration of more subtle statistical patterns within the equa","authors":["Constantin, Andrei","Bartlett, Deaglan","Desmond, Harry","Ferreira, Pedro G."],"url":"https://arxiv.org/abs/2408.11065","arxiv_id":"2408.11065","published":"2026-05-11"},{"title":"UNA: A Unified Supervised Framework for Efficient LLM Alignment Across Feedback Types","abstract":"RL alignment methods, including RLHF and DPO, are primarily based on pairwise preference data. Although scalar or score-based feedback has been collected in some settings, it is rarely used directly, and preference magnitude information is typically ignored. Furthermore, current alignment frameworks offer limited capability for unifying heterogeneous supervision signals, making it difficult to jointly leverage divers","authors":["Wang, Zhichao","Bi, Bin","Huang, Can","Pentyala, Shiva Kumar","Zhu, Zixu James","Asur, Sitaram"],"url":"https://arxiv.org/abs/2408.15339","arxiv_id":"2408.15339","published":"2026-05-11"},{"title":"How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?","abstract":"Message-passing graph neural networks (MPGNNs) are commonly compared with the Weisfeiler-Lehman (WL) color-refinement procedure, but this comparison does not quantify the resource parameters a network needs to realize color refinement with bounded-size messages and finite numerical precision. We study the cost of simulating a single color-refinement step on unattributed graphs. We distinguish input-independent, or ob","authors":["Cui, Guanyu","Guo, Yuhe","Wei, Zhewei","Su, Hsin-Hao"],"url":"https://arxiv.org/abs/2410.01308","arxiv_id":"2410.01308","published":"2026-05-11"},{"title":"A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection","abstract":"Graph neural networks (GNNs) are becoming increasingly popular for EEG-based depression detection. However, previous GNN-based methods fail to sufficiently consider the characteristics of depression, thus limiting their performance. Firstly, studies in neuroscience indicate that depression patients exhibit both common and individualized brain abnormal patterns. Previous GNN-based approaches typically focus either on ","authors":["Wang, Yiye","Zheng, Wenming","Li, Yang","Yang, Hao"],"url":"https://arxiv.org/abs/2410.18103","arxiv_id":"2410.18103","published":"2026-05-11"},{"title":"ChatSearch: a Dataset and a Generative Retrieval Model for General Conversational Image Retrieval","abstract":"In this paper, we investigate the task of general conversational image retrieval on open-domain images. The objective is to search for images based on interactive conversations between humans and computers. To advance this task, we curate a dataset called ChatSearch. This dataset includes a multi-round multimodal conversational context query for each target image, thereby requiring the retrieval system to find the ac","authors":["Zhao, Zijia","Guo, Longteng","Yue, Tongtian","Hu, Erdong","Shao, Shuai","Yuan, Zehuan"],"url":"https://arxiv.org/abs/2410.18715","arxiv_id":"2410.18715","published":"2026-05-11"}],"history":{"events":[{"year":2022,"text":"Myanmar civil war: Government troops killed 37 unarmed civilians in Mondaingbin.","url":"https://en.wikipedia.org/wiki/Myanmar_civil_war_(2021%E2%80%93present)"},{"year":2022,"text":"Palestinian-American journalist Shireen Abu Akleh was shot and killed while reporting on an Israel Defense Forces raid on the Jenin refugee camp.","url":"https://en.wikipedia.org/wiki/Shireen_Abu_Akleh"},{"year":2013,"text":"Two car bombs by unknown perpetrators exploded in Reyhanlı, Turkey, resulting in 52 killed and 140 injured.","url":"https://en.wikipedia.org/wiki/2013_Reyhanl%C4%B1_car_bombings"},{"year":2011,"text":"An earthquake registering Mw 5.1, the worst to hit the region for more than 50 years, struck near Lorca, Spain.","url":"https://en.wikipedia.org/wiki/2011_Lorca_earthquake"},{"year":2010,"text":"David Cameron took office as Prime Minister of the United Kingdom as the Conservatives and Liberal Democrats formed the country's first coalition government since the Second World War.","url":"https://en.wikipedia.org/wiki/David_Cameron"},{"year":2010,"text":"Gordon Brown resigned as Prime Minister of the United Kingdom and Leader of the Labour Party after failing to strike a coalition agreement with the Liberal Democrats.","url":"https://en.wikipedia.org/wiki/Gordon_Brown"},{"year":1998,"text":"India began the Pokhran-II nuclear-weapons test, its first since the Smiling Buddha test 24 years earlier.","url":"https://en.wikipedia.org/wiki/Pokhran-II"},{"year":1997,"text":"Deep Blue defeated Garry Kasparov in six games to become the first chess computer to win a match against a world champion.","url":"https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)"},{"year":1981,"text":"Andrew Lloyd Webber's Cats (performers pictured), opened at the New London Theatre.","url":"https://en.wikipedia.org/wiki/Andrew_Lloyd_Webber"},{"year":1970,"text":"Lubbock, Texas, was struck by a tornado that left 26 people dead.","url":"https://en.wikipedia.org/wiki/Lubbock%2C_Texas"},{"year":1963,"text":"African Americans rioted in Birmingham, Alabama, in response to two bombings, perceiving local police to be complicit with the perpetrators.","url":"https://en.wikipedia.org/wiki/Birmingham_riot_of_1963"},{"year":1928,"text":"After a week-long standoff punctuated by military clashes, Japanese forces captured the city of Jinan, Shandong in China.","url":"https://en.wikipedia.org/wiki/Jinan_incident"},{"year":1910,"text":"Glacier National Park was established in the U.S. state of Montana.","url":"https://en.wikipedia.org/wiki/Glacier_National_Park_(U.S.)"},{"year":1894,"text":"In response to a 28-percent wage cut, 4,000 Pullman Palace Car Company workers went on strike in Illinois, bringing rail traffic west of Chicago to a halt.","url":"https://en.wikipedia.org/wiki/Pullman_Company"},{"year":1889,"text":"Bandits attacked a U.S. Army paymaster's escort in the Arizona Territory, stealing more than $28,000.","url":"https://en.wikipedia.org/wiki/Wham_Paymaster_robbery"},{"year":1880,"text":"A land dispute between the Southern Pacific Railroad and settlers in Hanford, California, turned deadly when a gun battle broke out, leaving seven dead.","url":"https://en.wikipedia.org/wiki/Southern_Pacific_Railroad"},{"year":1858,"text":"Minnesota was carved out of the eastern half of the Minnesota Territory and admitted as the 32nd U.S. state.","url":"https://en.wikipedia.org/wiki/Minnesota"},{"year":1820,"text":"HMS Beagle (pictured), the ship that would take Charles Darwin on his voyage, was launched.","url":"https://en.wikipedia.org/wiki/HMS_Beagle"}],"births":[{"year":2006,"text":"Konsta Helenius, Finnish ice hockey player","url":"https://en.wikipedia.org/wiki/Konsta_Helenius"},{"year":2003,"text":"Fermín López, Spanish footballer","url":"https://en.wikipedia.org/wiki/Ferm%C3%ADn_L%C3%B3pez"},{"year":2000,"text":"Yūki Tsunoda, Japanese racing driver","url":"https://en.wikipedia.org/wiki/Yuki_Tsunoda"},{"year":2000,"text":"Wang Chuqin, Chinese table tennis player","url":"https://en.wikipedia.org/wiki/Wang_Chuqin"},{"year":1999,"text":"Sabrina Carpenter, American singer and actress","url":"https://en.wikipedia.org/wiki/Sabrina_Carpenter"},{"year":1998,"text":"Viktória Kužmová, Slovak tennis player","url":"https://en.wikipedia.org/wiki/Vikt%C3%B3ria_Hrun%C4%8D%C3%A1kov%C3%A1"},{"year":1997,"text":"Coi Leray, American rapper and singer","url":"https://en.wikipedia.org/wiki/Coi_Leray"},{"year":1997,"text":"Lana Condor, American actress","url":"https://en.wikipedia.org/wiki/Lana_Condor"}],"deaths":[{"year":2026,"text":"Brandon Clarke, Canadian-American basketball player (born 1996)","url":"https://en.wikipedia.org/wiki/Brandon_Clarke"},{"year":2024,"text":"Susan Backlinie, American actress and stuntwoman (born 1946)","url":"https://en.wikipedia.org/wiki/Susan_Backlinie"},{"year":2021,"text":"Colt Brennan, American quarterback (born 1983)","url":"https://en.wikipedia.org/wiki/Colt_Brennan"},{"year":2021,"text":"Norman Lloyd, American actor, producer and director (born 1914)","url":"https://en.wikipedia.org/wiki/Norman_Lloyd"},{"year":2020,"text":"Jerry Stiller, American comedian, actor (born 1927)","url":"https://en.wikipedia.org/wiki/Jerry_Stiller"},{"year":2019,"text":"Peggy Lipton, American actress, model, and singer (born 1946)","url":"https://en.wikipedia.org/wiki/Peggy_Lipton"},{"year":2019,"text":"Thomas Silverstein, American murderer (born 1952)","url":"https://en.wikipedia.org/wiki/Thomas_Silverstein"},{"year":2011,"text":"Robert Traylor, American basketball player (born 1977)","url":"https://en.wikipedia.org/wiki/Robert_Traylor"}]},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}