{"date":"2026-01-22","schema_version":1,"captured_at":"2026-08-25T23:25:04.474Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":17},"attention":[{"title":"Nicola Peltz","views":292333,"rank":3,"extract":"Nicola Anne Peltz Beckham is an American actress. 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loss","points":169,"comments":111,"url":"https://pdfa.org/want-to-make-your-pdfs-20-smaller-for-free/","discussion":"https://news.ycombinator.com/item?id=46717507"},{"title":"From stealth blackout to whitelisting: Inside the Iranian shutdown","points":163,"comments":130,"url":"https://www.kentik.com/blog/from-stealth-blackout-to-whitelisting-inside-the-iranian-shutdown/","discussion":"https://news.ycombinator.com/item?id=46713444"}],"research":[{"title":"Learning from Discriminatory Training Data","abstract":"Supervised learning systems are trained using historical data and, if the data was tainted by discrimination, they may unintentionally learn to discriminate against protected groups. We propose that fair learning methods, despite training on potentially discriminatory datasets, shall perform well on fair test datasets. Such dataset shifts crystallize application scenarios for specific fair learning methods. For insta","authors":["Grabowicz, Przemyslaw A.","Perello, Nicholas","Takatsu, Kenta"],"url":"https://arxiv.org/abs/1912.08189","arxiv_id":"1912.08189","published":"2026-01-22"},{"title":"Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic","abstract":"Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning LTL formulas for classifying traces; despite a growing interest of the research community, existing solutions suffer from two limitations: they do not scale beyond small formulas, and the","authors":["Raha, Ritam","Roy, Rajarshi","Fijalkow, Nathanaël","Neider, Daniel"],"url":"https://arxiv.org/abs/2110.06726","arxiv_id":"2110.06726","published":"2026-01-22"},{"title":"Continual Learning for Monolingual End-to-End Automatic Speech Recognition","abstract":"Adapting Automatic Speech Recognition (ASR) models to new domains results in a deterioration of performance on the original domain(s), a phenomenon called Catastrophic Forgetting (CF). Even monolingual ASR models cannot be extended to new accents, dialects, topics, etc. without suffering from CF, making them unable to be continually enhanced without storing all past data. Fortunately, Continual Learning (CL) methods,","authors":["Eeckt, Steven Vander","Van hamme, Hugo"],"url":"https://arxiv.org/abs/2112.09427","arxiv_id":"2112.09427","published":"2026-01-22"},{"title":"Impartial Games: A Challenge for Reinforcement Learning","abstract":"AlphaZero-style reinforcement learning (RL) algorithms have achieved superhuman performance in many complex board games such as Chess, Shogi, and Go. However, we showcase that these algorithms encounter significant and fundamental challenges when applied to impartial games, a class where players share game pieces and optimal strategy often relies on abstract mathematical principles. Specifically, we utilise the game ","authors":["Zhou, Bei","Riis, Søren"],"url":"https://arxiv.org/abs/2205.12787","arxiv_id":"2205.12787","published":"2026-01-22"},{"title":"Reinforced Inverse Scattering","abstract":"Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves. In order to collect information, sensors are put in different locations to send and receive waves from each other. The choice of sensor positions and incident wave frequencies determines the reconstruction quality of scatterer properties. This paper introduces reinforcement learning to develop","authors":["Jiang, Hanyang","Khoo, Yuehaw","Yang, Haizhao"],"url":"https://arxiv.org/abs/2206.04186","arxiv_id":"2206.04186","published":"2026-01-22"},{"title":"Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification","abstract":"Deep learning has demonstrated significant potential in medical imaging; however, the opacity of "black-box" models hinders clinical trust, while segmentation tasks typically necessitate labourious, hard-to-obtain pixel-wise annotations. To address these challenges simultaneously, this paper introduces a framework for three inherently explainable classifiers (GP-UNet, GP-ShuffleUNet, and GP-ReconResNet). By i","authors":["Chatterjee, Soumick","Yassin, Hadya","Dubost, Florian","Nürnberger, Andreas","Speck, Oliver"],"url":"https://arxiv.org/abs/2206.05148","arxiv_id":"2206.05148","published":"2026-01-22"},{"title":"Semantic Image Synthesis via Diffusion Models","abstract":"Denoising Diffusion Probabilistic Models (DDPMs) have achieved remarkable success in various image generation tasks compared with Generative Adversarial Nets (GANs). Recent work on semantic image synthesis mainly follows the de facto GAN-based approaches, which may lead to unsatisfactory quality or diversity of generated images. In this paper, we propose a novel framework based on DDPM for semantic image synthesis. U","authors":["Zhou, Wengang","Wang, Weilun","Bao, Jianmin","Chen, Dongdong","Chen, Dong","Yuan, Lu"],"url":"https://arxiv.org/abs/2207.00050","arxiv_id":"2207.00050","published":"2026-01-22"},{"title":"Machine Learning Decoder for 5G NR PUCCH Format 0","abstract":"5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physical Uplink Control Channel Format 0. We use fully connected n","authors":["Yerrapragada, Anil Kumar","S, Jeeva Keshav","Gautam, Ankit","Ganti, Radha Krishna"],"url":"https://arxiv.org/abs/2209.07861","arxiv_id":"2209.07861","published":"2026-01-22"},{"title":"Weight Averaging: A Simple Yet Effective Method to Overcome Catastrophic Forgetting in Automatic Speech Recognition","abstract":"Adapting a trained Automatic Speech Recognition (ASR) model to new tasks results in catastrophic forgetting of old tasks, limiting the model's ability to learn continually and to be extended to new speakers, dialects, languages, etc. Focusing on End-to-End ASR, in this paper, we propose a simple yet effective method to overcome catastrophic forgetting: weight averaging. By simply taking the average of the previou","authors":["Eeckt, Steven Vander","Van hamme, Hugo"],"url":"https://arxiv.org/abs/2210.15282","arxiv_id":"2210.15282","published":"2026-01-22"},{"title":"Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent","abstract":"With the fast development of big data, learning the optimal decision rule by recursively updating it and making online decisions has been easier than before. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for an online and adaptive data collection environment that can update decision rules via weighted stocha","authors":["Chang, Xiangyu","Chen, Xi","Lai, Zehua","Li, He","Liu, Zhihong","Zhang, Yichen"],"url":"https://arxiv.org/abs/2212.14883","arxiv_id":"2212.14883","published":"2026-01-22"},{"title":"Reassessing feature-based Android malware detection in a contemporary context","abstract":"We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a contemporary Android environment and a balanced dataset of 124,000 applications. Our findings show that feature-based approaches can still achieve detection accuracies beyond 98%, d","authors":["Muzaffar, Ali","Hassen, Hani Ragab","Zantout, Hind","Lones, Michael A"],"url":"https://arxiv.org/abs/2301.12778","arxiv_id":"2301.12778","published":"2026-01-22"},{"title":"Finite Expression Methods for Discovering Physical Laws from Data","abstract":"Nonlinear dynamics is a pervasive phenomenon observed in scientific and engineering disciplines. However, the task of deriving analytical expressions to describe nonlinear dynamics from limited data remains challenging. In this paper, we shall present a novel deep symbolic learning method called the "finite expression method" (FEX) to discover governing equations within a function space containing a finite se","authors":["Jiang, Zhongyi","Wang, Chunmei","Yang, Haizhao"],"url":"https://arxiv.org/abs/2305.08342","arxiv_id":"2305.08342","published":"2026-01-22"},{"title":"Benchmarking the Influence of Pre-training on Explanation Performance in MR Image Classification","abstract":"Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of "explainable" artificial intelligen","authors":["Oliveira, Marta","Wilming, Rick","Clark, Benedict","Budding, Céline","Eitel, Fabian","Ritter, Kerstin"],"url":"https://arxiv.org/abs/2306.12150","arxiv_id":"2306.12150","published":"2026-01-22"},{"title":"A Finite Expression Method for Solving High-Dimensional Committor Problems","abstract":"Transition path theory (TPT) is a mathematical framework for quantifying rare transition events between a pair of selected metastable states $A$ and $B$. Central to TPT is the committor function, which describes the probability to hit the metastable state $B$ prior to $A$ from any given starting point of the phase space. Once the committor is computed, the transition channels and the transition rate can be readily fo","authors":["Song, Zezheng","Cameron, Maria K.","Yang, Haizhao"],"url":"https://arxiv.org/abs/2306.12268","arxiv_id":"2306.12268","published":"2026-01-22"},{"title":"Learning minimal representations of stochastic processes with variational autoencoders","abstract":"Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are, however, difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic process. Our method build","authors":["Fernández-Fernández, Gabriel","Manzo, Carlo","Lewenstein, Maciej","Dauphin, Alexandre","Muñoz-Gil, Gorka"],"url":"https://arxiv.org/abs/2307.11608","arxiv_id":"2307.11608","published":"2026-01-22"},{"title":"Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems","abstract":"We propose that future AI transparency and accountability regulations are based on an open global standard for exchanging information about AI systems, which allows co-existence of potentially conflicting local regulations. Then, we discuss key components of a lightweight and effective AI transparency and/or accountability regulation. To prevent overregulation, the proposed approach encourages collaboration between r","authors":["Buckley, Warren","Byrne, Adrian","Perello, Nicholas","Cousins, Cyrus","Yasseri, Taha","Zick, Yair"],"url":"https://arxiv.org/abs/2307.13658","arxiv_id":"2307.13658","published":"2026-01-22"},{"title":"GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention","abstract":"Graph invariant learning (GIL) seeks invariant relations between graphs and labels under distribution shifts. Recent works try to extract an invariant subgraph to improve out-of-distribution (OOD) generalization, yet existing approaches either lack explicit control over compactness or rely on hard top-$k$ selection that shrinks the solution space and is only partially differentiable. In this paper, we provide an in-d","authors":["Yan, Junchi","Ding, Fangyu","Sun, Jiawei","Hu, Zhaoping","Zhou, Yunyi","Zhu, Lei"],"url":"https://arxiv.org/abs/2402.07191","arxiv_id":"2402.07191","published":"2026-01-22"},{"title":"Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching","abstract":"Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is crucial for accurately identifying a correct match, thereby enhancing the overall robustness and reliability of the matching process. Commonly used shape descriptors have sev","authors":["Bastico, Matteo","Decencière, Etienne","Corté, Laurent","Tillier, Yannick","Ryckelynck, David"],"url":"https://arxiv.org/abs/2402.17372","arxiv_id":"2402.17372","published":"2026-01-22"},{"title":"Adaptive Task Balancing for Visual Instruction Tuning via Inter-Task Contribution and Intra-Task Difficulty","abstract":"Visual instruction tuning is a key training stage of large multimodal models. However, when learning multiple visual tasks simultaneously, this approach often results in suboptimal and imbalanced overall performance due to latent knowledge conflicts across tasks. To mitigate this issue, we propose a novel Adaptive Task Balancing approach tailored for visual instruction tuning (VisATB). Specifically, we measure two cr","authors":["Dai, Yanqi","Wang, Yong","You, Zebin","Jing, Dong","Chu, Xiangxiang","Lu, Zhiwu"],"url":"https://arxiv.org/abs/2403.04343","arxiv_id":"2403.04343","published":"2026-01-22"},{"title":"Sora as a World Model? A Complete Survey on Text-to-Video Generation","abstract":"The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far text-to-video generation technology supports essential requirements in world modeling. We curate 250+ studies on text-based video synthesis and world modeling. We then observe that recent models increasingly support spatial, action, and strat","authors":["Puspitasari, Fachrina Dewi","Zhang, Chaoning","Cho, Joseph","Haider, Adnan","Eman, Noor Ul","Amin, Omer"],"url":"https://arxiv.org/abs/2403.05131","arxiv_id":"2403.05131","published":"2026-01-22"},{"title":"Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code","abstract":"The rise of Large Language Models (LLMs) has significantly advanced various applications on software engineering tasks, particularly in code generation. Despite the promising performance, LLMs are prone to generate hallucinations, which means LLMs might produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misaligned with the real-world knowledge, making the deployment of LLMs pote","authors":["Liu, Fang","Liu, Yang","Shi, Lin","Yang, Zhen","Zhang, Li","Lian, Xiaoli"],"url":"https://arxiv.org/abs/2404.00971","arxiv_id":"2404.00971","published":"2026-01-22"},{"title":"Dynamic angular synchronization under smoothness constraints","abstract":"Given an undirected measurement graph $\\mathcal{H} = ([n], \\mathcal{E})$, the classical angular synchronization problem consists of recovering unknown angles $θ_1^*,\\dots,θ_n^*$ from a collection of noisy pairwise measurements of the form $(θ_i^* - θ_j^*) \\mod 2π$, for all $\\{i,j\\} \\in \\mathcal{E}$. This problem arises in a variety of applications, including computer vision, time synchronization of distributed networ","authors":["Araya, Ernesto","Cucuringu, Mihai","Tyagi, Hemant"],"url":"https://arxiv.org/abs/2406.04071","arxiv_id":"2406.04071","published":"2026-01-22"},{"title":"BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling","abstract":"Human cognition excels at transcending sensory input and forming latent representations that structure our understanding of the world. While Large Language Model (LLM) agents demonstrate emergent reasoning and decision-making abilities, they lack a principled framework for capturing latent structures and modeling uncertainty. In this work, we explore for the first time how to bridge LLM agents with probabilistic grap","authors":["Huang, Hengguan","Shen, Xing","Wang, Songtao","Meng, Lingfa","Liu, Dianbo","Duchene, David Alejandro"],"url":"https://arxiv.org/abs/2406.05516","arxiv_id":"2406.05516","published":"2026-01-22"},{"title":"GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations","abstract":"Large pre-trained language models have become a crucial backbone for many downstream tasks in natural language processing (NLP), and while they are trained on a plethora of data containing a variety of biases, such as gender biases, it has been shown that they can also inherit such biases in their weights, potentially affecting their prediction behavior. However, it is unclear to what extent these biases also affect ","authors":["Wilming, Rick","Dox, Artur","Schulz, Hjalmar","Oliveira, Marta","Clark, Benedict","Haufe, Stefan"],"url":"https://arxiv.org/abs/2406.11547","arxiv_id":"2406.11547","published":"2026-01-22"},{"title":"Targeted Deep Learning System Boundary Testing","abstract":"Evaluating the behavioral boundaries of deep learning (DL) systems is crucial for understanding their reliability across diverse, unseen inputs. Existing solutions fall short as they rely on untargeted random, model- or latent-based perturbations, due to difficulties in generating controlled input variations. In this work, we introduce Mimicry, a novel black-box test generator for fine-grained, targeted exploration o","authors":["Weißl, Oliver","Abdellatif, Amr","Chen, Xingcheng","Merabishvili, Giorgi","Riccio, Vincenzo","Kacianka, Severin"],"url":"https://arxiv.org/abs/2408.06258","arxiv_id":"2408.06258","published":"2026-01-22"}],"history":{"events":[{"year":2012,"text":"Croatia held a referendum in which it voted to become a member of the European Union.","url":"https://en.wikipedia.org/wiki/2012_Croatian_European_Union_membership_referendum"},{"year":2006,"text":"Evo Morales was inaugurated as President of Bolivia, becoming the country's first indigenous president.","url":"https://en.wikipedia.org/wiki/Evo_Morales"},{"year":1987,"text":"After being convicted of receiving bribes, Pennsylvania state treasurer R. Budd Dwyer shot and killed himself in front of television cameras during a press conference.","url":"https://en.wikipedia.org/wiki/Bribery"},{"year":1984,"text":"During Super Bowl XVIII, Apple Computer introduced the Macintosh, the first successful personal computer to use a graphical user interface, with the television commercial \"1984\".","url":"https://en.wikipedia.org/wiki/Super_Bowl_XVIII"},{"year":1979,"text":"After surrounding Mutukula the previous day, Tanzanian forces attacked the town and caused Ugandan forces to flee.","url":"https://en.wikipedia.org/wiki/Mutukula%2C_Uganda"},{"year":1973,"text":"The U.S. Supreme Court's landmark decision in Roe v. Wade struck down laws restricting abortion during the first two trimesters of pregnancy.","url":"https://en.wikipedia.org/wiki/Supreme_Court_of_the_United_States"},{"year":1970,"text":"The Boeing 747, the world's first wide-body commercial airliner, entered service for Pan Am on the New York–London route.","url":"https://en.wikipedia.org/wiki/Boeing_747"},{"year":1969,"text":"Soviet leader Leonid Brezhnev (pictured) survived what was seen as an assassination attempt, an incident that was not revealed to the public until after the fall of the Soviet Union.","url":"https://en.wikipedia.org/wiki/Leonid_Brezhnev"},{"year":1968,"text":"Apollo 5 (pictured), the first flight of NASA's Lunar Module, lifted off from Cape Kennedy Air Force Station.","url":"https://en.wikipedia.org/wiki/Apollo_5"},{"year":1957,"text":"New York City police arrested George Metesky, better known as the \"Mad Bomber\", for planting over 30 bombs over 16 years throughout the city.","url":"https://en.wikipedia.org/wiki/New_York_City_Police_Department"},{"year":1943,"text":"World War II: The Battle of Buna–Gona on New Guinea ended with an Allied victory after two months of fighting in which the Japanese fought with a resolve and tenacity not previously encountered.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1924,"text":"Ramsay MacDonald took office as the first prime minister of the United Kingdom from the Labour Party.","url":"https://en.wikipedia.org/wiki/Ramsay_MacDonald"},{"year":1906,"text":"The SS Valencia was wrecked off the coast of Vancouver Island, Canada, in a location so treacherous it was known as the Graveyard of the Pacific.","url":"https://en.wikipedia.org/wiki/SS_Valencia"},{"year":1879,"text":"Anglo-Zulu War: The Zulu forces of King Cetshwayo (pictured) achieved a decisive victory at the Battle of Isandlwana.","url":"https://en.wikipedia.org/wiki/Anglo-Zulu_War"},{"year":1689,"text":"The Convention Parliament met to decide the fate of the throne after James II, the last Catholic monarch of England, fled to France following the Glorious Revolution.","url":"https://en.wikipedia.org/wiki/Convention_Parliament_(1689)"},{"year":1273,"text":"Muhammad II became Sultan of Granada after his father's death in a riding accident.","url":"https://en.wikipedia.org/wiki/Muhammad_II_of_Granada"},{"year":565,"text":"Eutychius of Constantinople was arrested after he refused Byzantine emperor Justinian I's order to adopt the tenets of the Aphthartodocetae, a sect of non-Chalcedonian Christians.","url":"https://en.wikipedia.org/wiki/Eutychius_of_Constantinople"}],"births":[{"year":2025,"text":"Athena Mapelli Mozzi, daughter of Princess Beatrice, born 11th in line of succession to the British throne","url":"https://en.wikipedia.org/wiki/Princess_Beatrice"},{"year":2007,"text":"Pau Cubarsí, Spanish footballer","url":"https://en.wikipedia.org/wiki/Pau_Cubars%C3%AD"},{"year":2002,"text":"Caitlin Clark, American basketball player","url":"https://en.wikipedia.org/wiki/Caitlin_Clark"},{"year":2000,"text":"Laia Codina, Spanish footballer","url":"https://en.wikipedia.org/wiki/Laia_Codina"},{"year":1999,"text":"Andrew Thomas, American football player","url":"https://en.wikipedia.org/wiki/Andrew_Thomas_(American_football)"},{"year":1998,"text":"Walid Cheddira, Moroccan footballer","url":"https://en.wikipedia.org/wiki/Walid_Cheddira"},{"year":1997,"text":"Fan Zhendong, Chinese table tennis player","url":"https://en.wikipedia.org/wiki/Fan_Zhendong"},{"year":1996,"text":"Dillon Brooks, Canadian basketball player","url":"https://en.wikipedia.org/wiki/Dillon_Brooks"}],"deaths":[{"year":2026,"text":"Hifumi Katō, Japanese professional shogi player (born 1940)","url":"https://en.wikipedia.org/wiki/Hifumi_Kat%C5%8D"},{"year":2023,"text":"Lin Brehmer, American disc jockey (born 1954)","url":"https://en.wikipedia.org/wiki/Lin_Brehmer"},{"year":2022,"text":"Thích Nhất Hạnh, Vietnamese Thiền Buddhist monk, peace activist, and founder of the Plum Village Tradition (born 1926)","url":"https://en.wikipedia.org/wiki/Th%C3%ADch_Nh%E1%BA%A5t_H%E1%BA%A1nh"},{"year":2021,"text":"Hank Aaron, American baseball player (born 1934)","url":"https://en.wikipedia.org/wiki/Hank_Aaron"},{"year":2018,"text":"Ursula K. Le Guin, American sci-fi and fantasy novelist (born 1929)","url":"https://en.wikipedia.org/wiki/Ursula_K._Le_Guin"},{"year":2018,"text":"William B. Jordan, American art historian (born 1940)","url":"https://en.wikipedia.org/wiki/William_B._Jordan"},{"year":2017,"text":"Masaya Nakamura, Japanese businessman (born 1925)","url":"https://en.wikipedia.org/wiki/Masaya_Nakamura_(businessman)"},{"year":2017,"text":"Yordano Ventura, Dominican baseball player (born 1991)","url":"https://en.wikipedia.org/wiki/Yordano_Ventura"}]},"census":null,"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."}