{"date":"2026-05-06","schema_version":1,"captured_at":"2026-08-25T23:07:37.074Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"C. Joseph Vijay","views":519672,"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). 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While these innovative and ground-breaking applications can be considered as a boon, at the same time they raise significant privacy concerns. In fact, recent GDPR (General Data Protection Regulation) legislation has highlighted and become an incentive for ","authors":["Yamac, Mehmet","Ahishali, Mete","Passalis, Nikolaos","Raitoharju, Jenni","Sankur, Bulent","Gabbouj, Moncef"],"url":"https://arxiv.org/abs/1906.08713","arxiv_id":"1906.08713","published":"2026-05-06"},{"title":"Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing","abstract":"Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing with SE problem are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from ","authors":["Yamac, Mehmet","Ahishali, Mete","Kiranyaz, Serkan","Gabbouj, Moncef"],"url":"https://arxiv.org/abs/2003.00768","arxiv_id":"2003.00768","published":"2026-05-06"},{"title":"Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images","abstract":"Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December 2019. It has already caused thousands of causalities and infected several millions worldwide. Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance. The main tools practitioners currently use to diagnose Covid-19 are Reverse Tra","authors":["Yamac, Mehmet","Ahishali, Mete","Degerli, Aysen","Kiranyaz, Serkan","Chowdhury, Muhammad E. H.","Gabbouj, Moncef"],"url":"https://arxiv.org/abs/2005.04014","arxiv_id":"2005.04014","published":"2026-05-06"},{"title":"Advance Warning Methodologies for COVID-19 using Chest X-Ray Images","abstract":"Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection ar","authors":["Ahishali, Mete","Degerli, Aysen","Yamac, Mehmet","Kiranyaz, Serkan","Chowdhury, Muhammad E. H.","Hameed, Khalid"],"url":"https://arxiv.org/abs/2006.05332","arxiv_id":"2006.05332","published":"2026-05-06"},{"title":"Metric-valued regression","abstract":"We propose an efficient algorithm for learning mappings between two metric spaces, $\\X$ and $\\Y$. Our procedure is strongly Bayes-consistent whenever $\\X$ and $\\Y$ are topologically separable and $\\Y$ is "bounded in expectation" (our term; the separability assumption can be somewhat weakened). At this level of generality, ours is the first such learnability result for unbounded loss in the agnostic setting. O","authors":["Cohen, Dan Tsir","Kontorovich, Aryeh"],"url":"https://arxiv.org/abs/2202.03045","arxiv_id":"2202.03045","published":"2026-05-06"},{"title":"Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks","abstract":"To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia, and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the c","authors":["Nauck, Christian","Lindner, Michael","Schürholt, Konstantin","Hellmann, Frank"],"url":"https://arxiv.org/abs/2206.06369","arxiv_id":"2206.06369","published":"2026-05-06"},{"title":"Finding Minimum-Cost Explanations for Predictions made by Tree Ensembles","abstract":"The ability to explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations. In this paper, we aim at finding explanations for predictions made by tree ensembles that are not only minimal, but also mini","authors":["Törnblom, John","Karlsson, Emil","Nadjm-Tehrani, Simin"],"url":"https://arxiv.org/abs/2303.09271","arxiv_id":"2303.09271","published":"2026-05-06"},{"title":"Improved Active Fire Detection using Operational U-Nets","abstract":"As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can cause quickly spreading fires and make them harder to control; therefore, early detection and accurate locating of active fires are crucial in environmental monitoring. Using satellite imagery to monitor and detect active fires has been crit","authors":["Devecioglu, Ozer Can","Ahishali, Mete","Sohrab, Fahad","Ince, Turker","Gabbouj, Moncef"],"url":"https://arxiv.org/abs/2304.09721","arxiv_id":"2304.09721","published":"2026-05-06"},{"title":"Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification","abstract":"Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spectral information from recent HSI sensors, analyzing the acquired images is challenging using traditional Machine Learning (ML) methods. As the number of frequency bands increases, the required number o","authors":["Kilickaya, Sertac","Ahishali, Mete","Sohrab, Fahad","Ince, Turker","Gabbouj, Moncef"],"url":"https://arxiv.org/abs/2304.09730","arxiv_id":"2304.09730","published":"2026-05-06"},{"title":"Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer","abstract":"Detection Transformers (DETR) have recently set new benchmarks in object detection. However, their performance in detecting rotated objects lags behind established oriented object detectors. Our analysis identifies a key observation: the boundary discontinuity and square-like problem in bipartite matching poses an issue with assigning appropriate ground truths to predictions, leading to duplicate low-confidence predi","authors":["Lee, Hakjin","Song, MinKi","Koo, Jamyoung","Seo, Junghoon"],"url":"https://arxiv.org/abs/2305.07598","arxiv_id":"2305.07598","published":"2026-05-06"},{"title":"Can Blockchains Reliably Train Machine Learning Models?","abstract":"Large proof of work (PoW) networks allow anyone to earn rewards by running computation-intensive hash puzzles for profit, yet they typically consume electricity comparable to that of medium-sized countries. Repurposing computing resources from hash puzzles to machine learning training can benefit the energy sector as a whole, since this computing power is no longer wasted on solving hash puzzles but is instead used t","authors":["Li, Peihao","Dahmani, Nadia"],"url":"https://arxiv.org/abs/2307.07066","arxiv_id":"2307.07066","published":"2026-05-06"},{"title":"Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks","abstract":"The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms for deep artificial and spiking neural networks are unable to adequately auto-regulate the limited resources in the network, which leads to performance drop along with energy consumption rise as the increase of tasks. In this paper, we prop","authors":["Han, Bing","Zhao, Feifei","Pan, Wenxuan","Zhao, Zhuoya","Li, Xianqi","Kong, Qingqun"],"url":"https://arxiv.org/abs/2309.09550","arxiv_id":"2309.09550","published":"2026-05-06"},{"title":"Recent Advances in Generative AI for Healthcare Applications","abstract":"The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentat","authors":["Shokrollahi, Yasin","Colmenarez, Jose","Liu, Wenxi","Yarmohammadtoosky, Sahar","Nikahd, Matthew M.","Dong, Pengfei"],"url":"https://arxiv.org/abs/2310.00795","arxiv_id":"2310.00795","published":"2026-05-06"},{"title":"A Scoping Review of Deep Learning Methods for Photoplethysmography Data","abstract":"Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information, with broad deployment in both clinical monitoring systems and wearable devices. In recent years, the integration of deep learning has substantially advanced PPG signal analysis and expanded its applications across healthcare and non-healthcare domains. Methods: We conducted a comprehensiv","authors":["Nie, Guangkun","Zhu, Jiabao","Tang, Gongzheng","Zhang, Deyun","Geng, Shijia","Zhao, Qinghao"],"url":"https://arxiv.org/abs/2401.12783","arxiv_id":"2401.12783","published":"2026-05-06"},{"title":"Dropout Concrete Autoencoder for Band Selection on HSI Scenes","abstract":"Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies. However, the existing deep learning-based methods either need additional post-processing strategies to select the descriptive bands or optimize the model indirectly, due to the parameterization inability of discrete variables for the selection ","authors":["Xu, Lei","Ahishali, Mete","Gabbouj, Moncef"],"url":"https://arxiv.org/abs/2401.16522","arxiv_id":"2401.16522","published":"2026-05-06"},{"title":"Improved Hardness Results for Learning Intersections of Halfspaces","abstract":"We show strong (and surprisingly simple) lower bounds for weakly learning intersections of halfspaces in the improper setting. Strikingly little is known about this problem. For instance, it is not even known if there is a polynomial-time algorithm for learning the intersection of only two halfspaces. On the other hand, lower bounds based on well-established assumptions (such as approximating worst-case lattice probl","authors":["Tiegel, Stefan"],"url":"https://arxiv.org/abs/2402.15995","arxiv_id":"2402.15995","published":"2026-05-06"},{"title":"A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches","abstract":"Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current approaches for tabular data often fail to preserve feature correlations and distributions during training, struggle with multi-metric hyperparameter selection, and lack comprehensive evaluation protocols. We address this gap with a unified ","authors":["Vu, Minh H.","Edler, Daniel","Wibom, Carl","Löfstedt, Tommy","Melin, Beatrice","Rosvall, Martin"],"url":"https://arxiv.org/abs/2405.16971","arxiv_id":"2405.16971","published":"2026-05-06"},{"title":"Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning","abstract":"Due to the increasing share of renewables, the analysis of the dynamical behavior of power grids gains importance. Effective risk assessments necessitate the analysis of large number of fault scenarios. The computational costs inherent in dynamic simulations impose constraints on the number of configurations that can be analyzed. Machine Learning (ML) has proven to efficiently predict complex power grid properties. H","authors":["Nauck, Christian","Büttner, Anna","Liemann, Sebastian","Hellmann, Frank","Lindner, Michael"],"url":"https://arxiv.org/abs/2406.08917","arxiv_id":"2406.08917","published":"2026-05-06"},{"title":"psifx -- Psychological and Social Interactions Feature Extraction Package","abstract":"psifx is a plug-and-play multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning techniques for human sciences research. It is motivated by a need (a) to automate and standardize data annotation processes that typically require expensive, lengthy, and inconsistent human labour; (b) to develop and distribute open-source community-driven psychology resea","authors":["Rochette, Guillaume","Rochat, Mathieu","Michaud, Nizar","Vowels, Matthew J."],"url":"https://arxiv.org/abs/2407.10266","arxiv_id":"2407.10266","published":"2026-05-06"},{"title":"Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft","abstract":"The symmetry of dynamical systems can be exploited for state-transition prediction and to facilitate control policy optimization. This paper leverages system symmetry to develop sample-efficient offline reinforcement learning (RL) approaches. Under the symmetry assumption for a Markov Decision Process (MDP), a symmetric data augmentation method is proposed. The augmented samples are integrated into the dataset of Dee","authors":["Li, Yifei","van Kampen, Erik-Jan"],"url":"https://arxiv.org/abs/2407.11077","arxiv_id":"2407.11077","published":"2026-05-06"},{"title":"Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions","abstract":"The geometry of a graph is encoded in dynamical processes on the graph. Many graph neural network (GNN) architectures are inspired by such dynamical systems, typically based on the graph Laplacian. Here, we introduce Dirac--Bianconi GNNs (DBGNNs), which are based on the topological Dirac equation recently proposed by Bianconi. Based on the graph Laplacian, we demonstrate that DBGNNs explore the geometry of the graph ","authors":["Nauck, Christian","Gorantla, Rohan","Lindner, Michael","Schürholt, Konstantin","Mey, Antonia S. J. S.","Hellmann, Frank"],"url":"https://arxiv.org/abs/2407.12419","arxiv_id":"2407.12419","published":"2026-05-06"},{"title":"Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise","abstract":"The Sparse Vector Technique (SVT) is one of the most fundamental tools in differential privacy (DP). It works as a backbone for adaptive data analysis by answering a sequence of queries on a given dataset, and gleaning useful information in a privacy-preserving manner. Unlike the typical private query releases that directly publicize the noisy query results, SVT is less informative -- it keeps the noisy query results","authors":["Liu, Yuhan","Wang, Sheng","Liu, Yixuan","Li, Feifei","Chen, Hong"],"url":"https://arxiv.org/abs/2407.20068","arxiv_id":"2407.20068","published":"2026-05-06"},{"title":"The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence","abstract":"Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet researchers, policymakers, and technology companies lack shared terminology for discussing AI risks. Consider "privacy": one framework uses this term to describe a model's ability to leak sensitive training data, while another uses it to mean freedom from government surv","authors":["Slattery, Peter","Saeri, Alexander K.","Grundy, Emily A. C.","Graham, Jess","Noetel, Michael","Uuk, Risto"],"url":"https://arxiv.org/abs/2408.12622","arxiv_id":"2408.12622","published":"2026-05-06"},{"title":"Power-Softmax: Towards Secure LLM Inference over Encrypted Data","abstract":"Modern cryptographic methods for implementing privacy-preserving LLMs such as \\gls{HE} require the LLMs to have a polynomial form. Forming such a representation is challenging because transformers include non-polynomial components, such as \\Softmax and layer normalization. Previous approaches have either directly approximated pre-trained models with large-degree polynomials, which are less efficient over HE, or repla","authors":["Zimerman, Itamar","Adir, Allon","Aharoni, Ehud","Avitan, Matan","Baruch, Moran","Drucker, Nir"],"url":"https://arxiv.org/abs/2410.09457","arxiv_id":"2410.09457","published":"2026-05-06"},{"title":"On Verbalized Confidence Scores for LLMs","abstract":"The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness. Uncertainty quantification for LLMs can establish more human trust into their responses, but also allows LLM agents to make more informed decisions based on each other's uncertainty. To estimate the uncertainty in a response, internal token logits, task-spec","authors":["Yang, Daniel","Tsai, Yao-Hung Hubert","Yamada, Makoto"],"url":"https://arxiv.org/abs/2412.14737","arxiv_id":"2412.14737","published":"2026-05-06"}],"history":{"events":[{"year":2023,"text":"King Charles III and Queen Camilla were crowned at Westminster Abbey in London.","url":"https://en.wikipedia.org/wiki/Charles_III"},{"year":2013,"text":"Amanda Berry escaped from the Cleveland, Ohio, home of her captor, Ariel Castro, having been held there with two other women for ten years.","url":"https://en.wikipedia.org/wiki/Cleveland"},{"year":2008,"text":"British barrister Mark Saunders was shot dead by police after a five-hour siege at his home in Chelsea, London.","url":"https://en.wikipedia.org/wiki/Barrister"},{"year":2004,"text":"The final episode of the television sitcom Friends was aired.","url":"https://en.wikipedia.org/wiki/The_Last_One_(Friends)"},{"year":1998,"text":"Steve Jobs unveiled the iMac G3' personal computer.","url":"https://en.wikipedia.org/wiki/Steve_Jobs"},{"year":1991,"text":"Time magazine published the article \"The Thriving Cult of Greed and Power\" by Richard Behar criticizing the Church of Scientology, leading to years of legal conflict.","url":"https://en.wikipedia.org/wiki/Time_(magazine)"},{"year":1988,"text":"Widerøe Flight 710 crashed into the fog-covered mountain of Torghatten in Brønnøy, Norway, killing all 36 people on board.","url":"https://en.wikipedia.org/wiki/Wider%C3%B8e_Flight_710"},{"year":1976,"text":"An earthquake struck Northern Italy, killing 990 people and injuring up to 3,000.","url":"https://en.wikipedia.org/wiki/1976_Friuli_earthquake"},{"year":1975,"text":"About 100,000 Armenians gathered during a lull in fighting to mark the 60th anniversary of the Armenian genocide.","url":"https://en.wikipedia.org/wiki/60th_anniversary_of_the_Armenian_genocide_commemorations_in_Beirut"},{"year":1954,"text":"At Oxford's Iffley Road Track, English runner Roger Bannister became the first person to run the mile in under four minutes.","url":"https://en.wikipedia.org/wiki/Oxford"},{"year":1942,"text":"World War II: Japanese troops overcame fierce American and Philippine resistance to win the Battle of Corregidor.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1941,"text":"American entertainer Bob Hope performed his first show with the United Service Organizations, beginning a 50-year involvement with them.","url":"https://en.wikipedia.org/wiki/Bob_Hope"},{"year":1937,"text":"The German airship Hindenburg caught fire and was destroyed during an attempt to dock at Lakehurst Naval Air Station in New Jersey, killing 36 people.","url":"https://en.wikipedia.org/wiki/LZ_129_Hindenburg"},{"year":1930,"text":"Vita Sackville-West and her husband Harold Nicolson purchased the Kent property they would transform into Sissinghurst Castle Garden.","url":"https://en.wikipedia.org/wiki/Vita_Sackville-West"},{"year":1915,"text":"Imperial Trans-Antarctic Expedition: SY Aurora, anchored in McMurdo Sound, broke loose during a gale, beginning a 312-day ordeal in the Ross Sea and Southern Ocean for her 18-man crew.","url":"https://en.wikipedia.org/wiki/Imperial_Trans-Antarctic_Expedition"},{"year":1882,"text":"Irish civil servants Thomas Henry Burke and Lord Frederick Cavendish were stabbed to death by members of the radical Irish National Invincibles in Phoenix Park, Dublin.","url":"https://en.wikipedia.org/wiki/Thomas_Henry_Burke_(civil_servant)"},{"year":1882,"text":"U.S. president Chester A. Arthur signed the Chinese Exclusion Act into law (cartoon pictured), implementing a ban on Chinese immigration to the United States that remained for 61 years.","url":"https://en.wikipedia.org/wiki/Chester_A._Arthur"},{"year":1801,"text":"French Revolutionary Wars: The 32-gun Spanish frigate El Gamo was captured by the outmanned and outgunned HMS Speedy.","url":"https://en.wikipedia.org/wiki/French_Revolutionary_Wars"}],"births":[{"year":2019,"text":"Prince Archie of Sussex","url":"https://en.wikipedia.org/wiki/Prince_Archie_of_Sussex"},{"year":2006,"text":"Sadie Sandler, American actress","url":"https://en.wikipedia.org/wiki/Sadie_Sandler"},{"year":2002,"text":"Cole Palmer, English footballer","url":"https://en.wikipedia.org/wiki/Cole_Palmer"},{"year":2002,"text":"Angel Reese, American basketball player","url":"https://en.wikipedia.org/wiki/Angel_Reese"},{"year":1999,"text":"Pato O'Ward, Mexican racing driver","url":"https://en.wikipedia.org/wiki/Pato_O'Ward"},{"year":1998,"text":"Luigi Mangione, suspect in the killing of UnitedHealthcare CEO Brian Thompson","url":"https://en.wikipedia.org/wiki/Luigi_Mangione"},{"year":1997,"text":"Maymay Entrata, Filipino model, entertainer and singer-songwriter","url":"https://en.wikipedia.org/wiki/Maymay_Entrata"},{"year":1997,"text":"Ranz Kyle, Filipino social media personality and entertainer","url":"https://en.wikipedia.org/wiki/Ranz_Kyle"}],"deaths":[{"year":2026,"text":"Ted Turner, American media mogul (born 1938)","url":"https://en.wikipedia.org/wiki/Ted_Turner"},{"year":2024,"text":"Bernard Pivot, French journalist, interviewer and host (born 1935)","url":"https://en.wikipedia.org/wiki/Bernard_Pivot"},{"year":2024,"text":"Brian Wenzel, Australian actor (born 1929)","url":"https://en.wikipedia.org/wiki/Brian_Wenzel"},{"year":2022,"text":"George Pérez, American comic book artist and writer (born 1954)","url":"https://en.wikipedia.org/wiki/George_P%C3%A9rez"},{"year":2021,"text":"Kentaro Miura, Japanese manga artist (born 1966)","url":"https://en.wikipedia.org/wiki/Kentaro_Miura"},{"year":2016,"text":"Patrick Ekeng, Cameroonian footballer (born 1990)","url":"https://en.wikipedia.org/wiki/Patrick_Ekeng"},{"year":2016,"text":"Reg Grundy, Australian businessman (born 1923)","url":"https://en.wikipedia.org/wiki/Reg_Grundy"},{"year":2015,"text":"Novera Ahmed, Bangladeshi sculptor (born 1930)","url":"https://en.wikipedia.org/wiki/Novera_Ahmed"}]},"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."}