{"date":"2025-12-30","schema_version":1,"captured_at":"2026-08-25T23:06:44.365Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Brigitte Bardot","views":758092,"rank":3,"extract":"Brigitte Anne-Marie Bardot, often referred to by her initials B.B., was a French actress, singer, model, and animal rights activist. 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Therefore, a crop yield estimation model which can predict pre-harvest yield is required for food security. The study is conducted on tea forms operating under National Tea Research Institute, Pakistan. The data is recorded on monthly basis for ten years period. The parameters collected are minimum and maximum temperature,","authors":["Ahmed, Nisar","Asif, Hafiz Muhammad Shahzad","Saleem, Gulshan","Younus, Muhammad Usman"],"url":"https://arxiv.org/abs/2102.05755","arxiv_id":"2102.05755","published":"2025-12-30"},{"title":"Recent Advances and Challenges in Deep Audio-Visual Correlation Learning","abstract":"Audio-visual correlation learning aims to capture essential correspondences and understand natural phenomena between audio and video. With the rapid growth of deep learning, an increasing amount of attention has been paid to this emerging research issue. Through the past few years, various methods and datasets have been proposed for audio-visual correlation learning, which motivate us to conclude a comprehensive surv","authors":["Vilaça, Luís","Yu, Yi","Viana, Paula"],"url":"https://arxiv.org/abs/2202.13673","arxiv_id":"2202.13673","published":"2025-12-30"},{"title":"An Efficient Minimax Optimal Estimator For Multivariate Convex Regression","abstract":"This work studies the computational aspects of multivariate convex regression in dimensions $d \\ge 5$. Our results include the \\emph{first} estimators that are minimax optimal (up to logarithmic factors) with polynomial runtime in the sample size for both $L$-Lipschitz convex regression, and $Γ$-bounded convex regression under polytopal support. Our analysis combines techniques from empirical process theory, stochast","authors":["Kur, Gil","Putterman, Eli"],"url":"https://arxiv.org/abs/2205.03368","arxiv_id":"2205.03368","published":"2025-12-30"},{"title":"Recursive Neural Programs: Variational Learning of Image Grammars and Part-Whole Hierarchies","abstract":"Human vision involves parsing and representing objects and scenes using structured representations based on part-whole hierarchies. Computer vision and machine learning researchers have recently sought to emulate this capability using capsule networks, reference frames and active predictive coding, but a generative model formulation has been lacking. We introduce Recursive Neural Programs (RNPs), which, to our knowle","authors":["Fisher, Ares","Rao, Rajesh P. N."],"url":"https://arxiv.org/abs/2206.08462","arxiv_id":"2206.08462","published":"2025-12-30"},{"title":"Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework","abstract":"Decentralized execution is one core demand in multi-agent reinforcement learning (MARL). Recently, most popular MARL algorithms have adopted decentralized policies to enable decentralized execution, and use gradient descent as the optimizer. However, there is hardly any theoretical analysis of these algorithms taking the optimization method into consideration, and we find that various popular MARL algorithms with dec","authors":["Ye, Jianing","Li, Chenghao","Dou, Yongqiang","Wang, Jianhao","Yang, Guangwen","Zhang, Chongjie"],"url":"https://arxiv.org/abs/2207.11143","arxiv_id":"2207.11143","published":"2025-12-30"},{"title":"Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning","abstract":"Predictive coding has emerged as a prominent model of how the brain learns through predictions, anticipating the importance accorded to predictive learning in recent AI architectures such as transformers. Here we propose a new framework for predictive coding called active predictive coding which can learn hierarchical world models and solve two radically different open problems in AI: (1) how do we learn compositiona","authors":["Rao, Rajesh P. N.","Gklezakos, Dimitrios C.","Sathish, Vishwas"],"url":"https://arxiv.org/abs/2210.13461","arxiv_id":"2210.13461","published":"2025-12-30"},{"title":"Annotated History of Modern AI and Deep Learning","abstract":"Machine learning (ML) is the science of credit assignment. It seeks to find patterns in observations that explain and predict the consequences of events and actions. This then helps to improve future performance. Minsky's so-called "fundamental credit assignment problem" (1963) surfaces in all sciences including physics (why is the world the way it is?) and history (which persons/ideas/actions have shaped","authors":["Schmidhuber, Juergen"],"url":"https://arxiv.org/abs/2212.11279","arxiv_id":"2212.11279","published":"2025-12-30"},{"title":"An extended method for Statistical Signal Characterization using moments and cumulants, as a fast and accurate pre-processing stage of simple ANNs applied to the recognition of pattern alterations in pulse-like waveforms","abstract":"We propose a feature-extraction procedure based on the statistical characterization of waveforms, applied as a fast pre-processing stage in a pattern recognition task using simple artificial neural network models. This procedure involves measuring a set of 30 parameters, including moments and cumulants obtained from the waveform, its derivative, and its integral. The technique is presented as an extension of the Stat","authors":["Bustos, G. H.","Segnorile, H. H."],"url":"https://arxiv.org/abs/2212.14783","arxiv_id":"2212.14783","published":"2025-12-30"},{"title":"Data-driven multiscale modeling for correcting dynamical systems","abstract":"We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information in both fine-to-coarse and coarse-to-fine directions. We envision this method being generally applicable to problems with significant self-similarity or in which the prediction task is challenging and where stability of a learned model's impact on the target dynamical system is imp","authors":["Otness, Karl","Zanna, Laure","Bruna, Joan"],"url":"https://arxiv.org/abs/2303.17496","arxiv_id":"2303.17496","published":"2025-12-30"},{"title":"Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge","abstract":"Fine-tuning pre-trained language models (PLMs), e.g., SciBERT, generally requires large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining the fine-tune data for scientific NLP task is still challenging and expensive. Inspired by recent advancement in prompt learning, in this paper, we propose the Mix Prompt Tuning (MPT), which is a s","authors":["Liu, Jiawei","Xiong, Zi","Jiang, Yi","Ma, Yongqiang","Lu, Wei","Huang, Yong"],"url":"https://arxiv.org/abs/2305.03287","arxiv_id":"2305.03287","published":"2025-12-30"},{"title":"Enhance Multi-Scale Spatial-Temporal Coherence for Configurable Video Anomaly Detection","abstract":"The development of unsupervised Video Anomaly Detection (VAD) relies on technologies in the field of signal processing. Since the anomaly is quite ambiguous and unbounded, different detection demands may often be raised even in one scenario. Thus, we propose to design the configurable VAD with flexible solutions targeting to solve the issue that previous methods have to train their models from scratch and waste resou","authors":["Cheng, Kai","Li, Xinzhe","Che, Lijuan"],"url":"https://arxiv.org/abs/2305.07328","arxiv_id":"2305.07328","published":"2025-12-30"},{"title":"Prompt Injection attack against LLM-integrated Applications","abstract":"Large Language Models (LLMs), renowned for their superior proficiency in language comprehension and generation, stimulate a vibrant ecosystem of applications around them. However, their extensive assimilation into various services introduces significant security risks. This study deconstructs the complexities and implications of prompt injection attacks on actual LLM-integrated applications. Initially, we conduct an ","authors":["Liu, Yi","Deng, Gelei","Li, Yuekang","Wang, Kailong","Wang, Zihao","Wang, Xiaofeng"],"url":"https://arxiv.org/abs/2306.05499","arxiv_id":"2306.05499","published":"2025-12-30"},{"title":"TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage","abstract":"With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks which necessitate a combination of task planning and the usage of external tools. In this paper, we first propose a structured framework tailored for ","authors":["Ruan, Jingqing","Chen, Yihong","Zhang, Bin","Xu, Zhiwei","Bao, Tianpeng","Du, Guoqing"],"url":"https://arxiv.org/abs/2308.03427","arxiv_id":"2308.03427","published":"2025-12-30"},{"title":"Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web","abstract":"Data on the Web has fueled much of the recent progress in AI. As more high-quality data becomes difficult to access, synthetic data is emerging as a promising solution for privacy-friendly data release and complementing real datasets in developing robust and safe AI. But there is limited work on decentralised, scalable and contributor-centric synthetic data generation systems. A recent proposal, called Libertas, allo","authors":["Ramesh, Vishal","Zhao, Rui","Goel, Naman"],"url":"https://arxiv.org/abs/2310.20062","arxiv_id":"2310.20062","published":"2025-12-30"},{"title":"Sequential learning on a Tensor Network Born machine with Trainable Token Embedding","abstract":"Generative models aim to learn the probability distributions underlying data, enabling the generation of new, realistic samples. Quantum inspired generative models, such as Born machines based on the matrix product state framework, have demonstrated remarkable capabilities in unsupervised learning tasks. This study advances the Born machine paradigm by introducing trainable token embeddings through positive operator ","authors":["Hou, Wanda","Li, Miao","You, Yi-Zhuang"],"url":"https://arxiv.org/abs/2311.05050","arxiv_id":"2311.05050","published":"2025-12-30"},{"title":"Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs","abstract":"Recent advancements in multimodal large language models (MLLMs) have achieved significant multimodal generation capabilities, akin to GPT-4. These models predominantly map visual information into language representation space, leveraging the vast knowledge and powerful text generation abilities of LLMs to produce multimodal instruction-following responses. We could term this method as LLMs for Vision because of its e","authors":["Li, Yunxin","Liu, Zhenyu","Hu, Baotian","Wang, Wei","Ding, Yuxin","Cao, Xiaochun"],"url":"https://arxiv.org/abs/2311.15759","arxiv_id":"2311.15759","published":"2025-12-30"},{"title":"Robust Point Matching with Distance Profiles","abstract":"Computational difficulty of quadratic matching and the Gromov-Wasserstein distance has led to various approximation and relaxation schemes. One of such methods, relying on the notion of distance profiles, has been widely used in practice, but its theoretical understanding is limited. By delving into the statistical complexity of the previously proposed method based on distance profiles, we show that it suffers from t","authors":["Hur, YoonHaeng","Khoo, Yuehaw"],"url":"https://arxiv.org/abs/2312.12641","arxiv_id":"2312.12641","published":"2025-12-30"},{"title":"A Survey of Reinforcement Learning from Human Feedback","abstract":"Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning (RL) that learns from human feedback instead of relying on an engineered reward function. Building on prior work on the related setting of preference-based reinforcement learning (PbRL), it stands at the intersection of artificial intelligence and human-computer interaction. This positioning provides a promising approach to enhan","authors":["Kaufmann, Timo","Weng, Paul","Bengs, Viktor","Hüllermeier, Eyke"],"url":"https://arxiv.org/abs/2312.14925","arxiv_id":"2312.14925","published":"2025-12-30"},{"title":"Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning","abstract":"Constructing a universal moral code for artificial intelligence (AI) is difficult or even impossible, given that different human cultures have different definitions of morality and different societal norms. We therefore argue that the value system of an AI should be culturally attuned: just as a child raised in a particular culture learns the specific values and norms of that culture, we propose that an AI agent oper","authors":["Oliveira, Nigini","Li, Jasmine","Khalvati, Koosha","Barragan, Rodolfo Cortes","Reinecke, Katharina","Meltzoff, Andrew N."],"url":"https://arxiv.org/abs/2312.17479","arxiv_id":"2312.17479","published":"2025-12-30"},{"title":"Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection","abstract":"In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of fields. By leveraging the complementary properties between RGB and IR images, the object detection task can achieve reliable and robust object localization across a variety of lighting conditions, from daytime to nighttime environme","authors":["Zhao, Tianyi","Yuan, Maoxun","Jiang, Feng","Wang, Nan","Wei, Xingxing"],"url":"https://arxiv.org/abs/2401.10731","arxiv_id":"2401.10731","published":"2025-12-30"},{"title":"NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction","abstract":"Typical quantitative MRI (qMRI) methods estimate parameter maps in a two-step pipeline that first reconstructs images from undersampled k-space data and then performs model fitting, which is prone to biases and error propagation. We propose NLCG-Net, a model-based nonlinear conjugate gradient (NLCG) framework for joint T2/T1 estimation that incorporates a U-Net regularizer trained in a scan-specific, zero-shot fashio","authors":["Jiang, Xinrui","Jun, Yohan","Cho, Jaejin","Gao, Mengze","Yong, Xingwang","Bilgic, Berkin"],"url":"https://arxiv.org/abs/2401.12004","arxiv_id":"2401.12004","published":"2025-12-30"},{"title":"Diffusion MRI with Machine Learning","abstract":"\\hspace{2mm} Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements ma","authors":["Karimi, Davood","Warfield, Simon K."],"url":"https://arxiv.org/abs/2402.00019","arxiv_id":"2402.00019","published":"2025-12-30"},{"title":"DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks","abstract":"Early exiting has demonstrated its effectiveness in accelerating the inference of pre-trained language models like BERT by dynamically adjusting the number of layers executed. However, most existing early exiting methods only consider local information from an individual test sample to determine their exiting indicators, failing to leverage the global information offered by sample population. This leads to suboptimal","authors":["He, Jianing","Zhang, Qi","Ding, Weiping","Miao, Duoqian","Zhao, Jun","Hu, Liang"],"url":"https://arxiv.org/abs/2402.05948","arxiv_id":"2402.05948","published":"2025-12-30"},{"title":"Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography","abstract":"Lung mask creation lacks well-defined criteria and standardized guidelines, leading to a high degree of subjectivity between annotators. In this study, we assess the underestimation of lung regions on chest X-ray segmentation masks created according to the current state-of-the-art method, by comparison with total lung volume evaluated on computed tomography (CT). We show, that lung X-ray masks created by following th","authors":["Bombiński, Przemysław","Szatkowski, Patryk","Sobieski, Bartłomiej","Kwieciński, Tymoteusz","Płotka, Szymon","Adamek, Mariusz"],"url":"https://arxiv.org/abs/2402.11510","arxiv_id":"2402.11510","published":"2025-12-30"},{"title":"Never-Ending Behavior-Cloning Agent for Robotic Manipulation","abstract":"Relying on multi-modal observations, embodied robots (e.g., humanoid robots) could perform multiple robotic manipulation tasks in unstructured real-world environments. However, most language-conditioned behavior-cloning agents in robots still face existing long-standing challenges, i.e., 3D scene representation and human-level task learning, when adapting into a series of new tasks in practical scenarios. We here inv","authors":["Liang, Wenqi","Sun, Gan","He, Yao","Ren, Yu","Dong, Jiahua","Cong, Yang"],"url":"https://arxiv.org/abs/2403.00336","arxiv_id":"2403.00336","published":"2025-12-30"}],"history":{"events":[{"year":2013,"text":"Supporters of Congolese religious leader Paul-Joseph Mukungubila carried out a series of attacks on television studios, the airport and a military base in Kinshasa.","url":"https://en.wikipedia.org/wiki/Paul-Joseph_Mukungubila"},{"year":2009,"text":"Due to a rupture of the Lanzhou–Zhengzhou–Changsha pipeline in Shaanxi, China, caused approximately 150,000 l (40,000 US gal) of diesel oil flowed down the Wei River before finally reaching the Yellow River six days later.","url":"https://en.wikipedia.org/wiki/Lanzhou%E2%80%93Zhengzhou%E2%80%93Changsha_pipeline"},{"year":2009,"text":"Pro-government counter-demonstrators held rallies (pictured) in several Iranian cities in response to recent anti-government protests on the holy day of Ashura.","url":"https://en.wikipedia.org/wiki/2009_Iranian_pro-government_rallies"},{"year":2006,"text":"The Indonesian ferry MV Senopati Nusantara sank in the Java Sea during a storm, killing at least 400 people.","url":"https://en.wikipedia.org/wiki/MV_Senopati_Nusantara"},{"year":2006,"text":"Basque nationalist group ETA detonated a van bomb at Madrid–Barajas Airport in Spain, ending a nine-month ceasefire.","url":"https://en.wikipedia.org/wiki/Basque_nationalism"},{"year":2006,"text":"Saddam Hussein, the former president of Iraq, was executed after being found guilty of crimes against humanity by the Iraqi High Tribunal.","url":"https://en.wikipedia.org/wiki/Saddam_Hussein"},{"year":2005,"text":"Tropical Storm Zeta (pictured) became a tropical depression, making it the record-breaking 28th tropical cyclone of the 2005 Atlantic hurricane season, the most active in recorded history until 2020.","url":"https://en.wikipedia.org/wiki/Tropical_Storm_Zeta"},{"year":2000,"text":"A series of bombings occurred around Metro Manila in the Philippines, killing 22 people and injuring around 100 others.","url":"https://en.wikipedia.org/wiki/Rizal_Day_bombings"},{"year":1958,"text":"The Guatemalan Air Force fired on Mexican fishing boats that had strayed into Guatemalan territory, triggering an eight-month conflict.","url":"https://en.wikipedia.org/wiki/Guatemalan_Air_Force"},{"year":1954,"text":"The Finnish National Bureau of Investigation was established to consolidate criminal investigation and intelligence into a single agency.","url":"https://en.wikipedia.org/wiki/National_Bureau_of_Investigation_(Finland)"},{"year":1940,"text":"The Arroyo Seco Parkway, one of the first freeways built in the U.S., connecting downtown Los Angeles with Pasadena, California, was officially dedicated.","url":"https://en.wikipedia.org/wiki/Arroyo_Seco_Parkway"},{"year":1935,"text":"Second Italo-Ethiopian War: In retaliation for the execution of an Italian prisoner of war, the Italian Air Force destroyed a field hospital of the Swedish Red Cross in Dolo.","url":"https://en.wikipedia.org/wiki/Second_Italo-Ethiopian_War"},{"year":1906,"text":"The All-India Muslim League, a political party in British India that developed into the driving force behind the creation of Pakistan as a Muslim state on the Indian subcontinent, was founded in Dhaka.","url":"https://en.wikipedia.org/wiki/All-India_Muslim_League"},{"year":1903,"text":"In one of the deadliest single-building fires in United States history, the Iroquois Theatre fire in Chicago killed more than 600 people.","url":"https://en.wikipedia.org/wiki/Iroquois_Theatre_fire"},{"year":1902,"text":"The Discovery Expedition under Robert Falcon Scott attained a new Farthest South point in Antarctica.","url":"https://en.wikipedia.org/wiki/Discovery_Expedition"},{"year":1845,"text":"The University of Galway, University College Cork and Queen's University Belfast were established as part of the Queen's University of Ireland.","url":"https://en.wikipedia.org/wiki/University_of_Galway"},{"year":1813,"text":"War of 1812: British forces captured Buffalo, New York, and burned down nearly all its buildings.","url":"https://en.wikipedia.org/wiki/War_of_1812"},{"year":1702,"text":"Queen Anne's War: James Moore, the British colonial governor of Carolina, abandoned a siege against St. Augustine in Spanish Florida and retreated to Charles Town in disgrace.","url":"https://en.wikipedia.org/wiki/Queen_Anne's_War"}],"births":[{"year":1995,"text":"Igor Shesterkin, Russian hockey goaltender","url":"https://en.wikipedia.org/wiki/Igor_Shesterkin"},{"year":1995,"text":"Joshua, American singer","url":"https://en.wikipedia.org/wiki/Joshua_(American_singer)"},{"year":1995,"text":"Ollie Watkins, English footballer","url":"https://en.wikipedia.org/wiki/Ollie_Watkins"},{"year":1995,"text":"V, South Korean singer","url":"https://en.wikipedia.org/wiki/V_(singer)"},{"year":1992,"text":"Ryan Tunnicliffe, English footballer","url":"https://en.wikipedia.org/wiki/Ryan_Tunnicliffe"},{"year":1992,"text":"Carson Wentz, American football player","url":"https://en.wikipedia.org/wiki/Carson_Wentz"},{"year":1991,"text":"Camila Giorgi, Italian tennis player","url":"https://en.wikipedia.org/wiki/Camila_Giorgi"},{"year":1990,"text":"Bruno Henrique, Brazilian footballer","url":"https://en.wikipedia.org/wiki/Bruno_Henrique_(footballer%2C_born_1990)"}],"deaths":[{"year":2025,"text":"Khaleda Zia, Bangladeshi Politician, Former Prime Minister of Bangladesh","url":"https://en.wikipedia.org/wiki/Khaleda_Zia"},{"year":2023,"text":"Bryan Ansell, British role-playing and wargame designer (born 1955)","url":"https://en.wikipedia.org/wiki/Bryan_Ansell"},{"year":2023,"text":"Tom Wilkinson, English actor (born 1948)","url":"https://en.wikipedia.org/wiki/Tom_Wilkinson"},{"year":2023,"text":"Aki Yashiro, Japanese singer (born 1950)","url":"https://en.wikipedia.org/wiki/Aki_Yashiro"},{"year":2022,"text":"Barbara Walters, American journalist, producer, and author (born 1929)","url":"https://en.wikipedia.org/wiki/Barbara_Walters"},{"year":2020,"text":"Dawn Wells, American actress, (born 1938)","url":"https://en.wikipedia.org/wiki/Dawn_Wells"},{"year":2017,"text":"Erica Garner, American civil rights activist (born 1990)","url":"https://en.wikipedia.org/wiki/Erica_Garner"},{"year":2015,"text":"Doug Atkins, American football player (born 1930)","url":"https://en.wikipedia.org/wiki/Doug_Atkins"}]},"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."}