{"date":"2026-01-05","schema_version":1,"captured_at":"2026-08-25T23:22:06.085Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":18},"attention":[{"title":"Nicolás Maduro","views":1503722,"rank":2,"extract":"Nicolás Maduro Moros is a Venezuelan politician and former union leader who served as the 53rd president of Venezuela from 2013 until his capture during the United States intervention in Venezuela in 2026 for alleged drug trafficking to which he pleaded not gu","url":"https://en.wikipedia.org/wiki/Nicol%C3%A1s_Maduro","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/4/46/Nicol%C3%A1s_Maduro_2016_crop.jpg/330px-Nicol%C3%A1s_Maduro_2016_crop.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Venezuela","views":720633,"rank":4,"extract":"Venezuela, officially the Bolivarian Republic of Venezuela, is a country on the northern coast of South America, consisting of a continental landmass and various islands and islets in the Caribbean Sea. 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As the Internet of Things (IoT) evolves towards a more transient and opportunistic execution paradigm, reducing energy storage costs will be key for its economic and ecologic viability. However, decreasing energy storage in harvesting systems introduces reliability issues. Transducers only produce in","authors":["Gomez, Andres","Tretter, Andreas","Hager, Pascal Alexander","Sanmugarajah, Praveenth","Benini, Luca","Thiele, Lothar"],"url":"https://arxiv.org/abs/2108.04059","arxiv_id":"2108.04059","published":"2026-01-05"},{"title":"MCD: Marginal Contrastive Discrimination for conditional density estimation","abstract":"We consider the problem of conditional density estimation, which is a major topic of interest in the fields of statistical and machine learning. Our method, called Marginal Contrastive Discrimination, MCD, reformulates the conditional density function into two factors, the marginal density function of the target variable and a ratio of density functions which can be estimated through binary classification. Like noise","authors":["Meziani, Katia","Ndiaye, Aminata","Riu, Benjamin"],"url":"https://arxiv.org/abs/2206.01592","arxiv_id":"2206.01592","published":"2026-01-05"},{"title":"Density-Based Algorithms for Corruption-Robust Contextual Search and Convex Optimization","abstract":"We study the problem of contextual search, a generalization of binary search in higher dimensions, in the adversarial noise model. Let $d$ be the dimension of the problem, $T$ be the time horizon and $C$ be the total amount of adversarial noise in the system. We focus on the $ε$-ball and the symmetric loss. For the $ε$-ball loss, we give a tight regret bound of $O(C + d \\log(1/ε))$ improving over the $O(d^3 \\log(1/ε)","authors":["Leme, Renato Paes","Podimata, Chara","Schneider, Jon"],"url":"https://arxiv.org/abs/2206.07528","arxiv_id":"2206.07528","published":"2026-01-05"},{"title":"Distributed Sparse Linear Regression under Communication Constraints","abstract":"In multiple domains, statistical tasks are performed in distributed settings, with data split among several end machines that are connected to a fusion center. In various applications, the end machines have limited bandwidth and power, and thus a tight communication budget. In this work we focus on distributed learning of a sparse linear regression model, under severe communication constraints. We propose several two","authors":["Fonseca, Rodney","Nadler, Boaz"],"url":"https://arxiv.org/abs/2301.04022","arxiv_id":"2301.04022","published":"2026-01-05"},{"title":"Efficient Multi-Task Scene Analysis with RGB-D Transformers","abstract":"Scene analysis is essential for enabling autonomous systems, such as mobile robots, to operate in real-world environments. However, obtaining a comprehensive understanding of the scene requires solving multiple tasks, such as panoptic segmentation, instance orientation estimation, and scene classification. Solving these tasks given limited computing and battery capabilities on mobile platforms is challenging. To addr","authors":["Fischedick, Söhnke Benedikt","Seichter, Daniel","Schmidt, Robin","Rabes, Leonard","Gross, Horst-Michael"],"url":"https://arxiv.org/abs/2306.05242","arxiv_id":"2306.05242","published":"2026-01-05"},{"title":"Sparse-Input Neural Network using Group Concave Regularization","abstract":"Simultaneous feature selection and non-linear function estimation is challenging in modeling, especially in high-dimensional settings where the number of variables exceeds the available sample size. In this article, we investigate the problem of feature selection in neural networks. Although the group least absolute shrinkage and selection operator (LASSO) has been utilized to select variables for learning with neura","authors":["Luo, Bin","Halabi, Susan"],"url":"https://arxiv.org/abs/2307.00344","arxiv_id":"2307.00344","published":"2026-01-05"},{"title":"AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection","abstract":"Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, an","authors":["Zhou, Qihang","Pang, Guansong","Tian, Yu","He, Shibo","Chen, Jiming"],"url":"https://arxiv.org/abs/2310.18961","arxiv_id":"2310.18961","published":"2026-01-05"},{"title":"Simple and Effective Input Reformulations for Translation","abstract":"Foundation language models learn from their finetuning input context in different ways. In this paper, we reformulate inputs during finetuning for challenging translation tasks, leveraging model strengths from pretraining in novel ways to improve downstream performance. These reformulations are simple data level modifications, require no additional collection of training data or modification of data at inference time","authors":["Yu, Brian","Lillemark, Hansen","Keutzer, Kurt"],"url":"https://arxiv.org/abs/2311.06696","arxiv_id":"2311.06696","published":"2026-01-05"},{"title":"Probabilistic Reduced-Dimensional Vector Autoregressive Modeling with Oblique Projections","abstract":"In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model to extract low-dimensional dynamics from high-dimensional noisy data. The model utilizes an oblique projection to partition the measurement space into a subspace that accommodates the reduced-dimensional dynamics and a complementary static subspace. An optimal oblique decomposition is derived for the best predictabilit","authors":["Mo, Yanfang","Qin, S. Joe"],"url":"https://arxiv.org/abs/2401.07206","arxiv_id":"2401.07206","published":"2026-01-05"},{"title":"SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks","abstract":"Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient resi","authors":["Erisen, Serdar"],"url":"https://arxiv.org/abs/2401.15741","arxiv_id":"2401.15741","published":"2026-01-05"},{"title":"Scaling Efficient LLMs","abstract":"Recent LLMs have hundreds of billions of parameters consuming vast resources. Furthermore, the so called "AI scaling law" for transformers suggests that the number of parameters must scale linearly with the size of the data. In response, we inquire into efficient LLMs, i.e. those with the fewest parameters that achieve the desired accuracy on a training corpus. Specifically, by comparing theoretical and empir","authors":["Kausik, B. N."],"url":"https://arxiv.org/abs/2402.14746","arxiv_id":"2402.14746","published":"2026-01-05"},{"title":"Real-Time Forecasting of Pathological Gait via IMU Navigation: A Few-Shot and Generative Learning Framework for Wearable Devices","abstract":"Current gait analysis faces challenges in various aspects, including limited and poorly labeled data within existing wearable electronics databases, difficulties in collecting patient data due to privacy concerns, and the inadequacy of the Zero-Velocity Update Technique (ZUPT) in accurately analyzing pathological gait patterns. To address these limitations, we introduce GaitMotion, a novel machine-learning framework ","authors":["Zhang, Wenwen","Zhang, Hao","Jiang, Zenan","Servati, Amir","Servati, Peyman"],"url":"https://arxiv.org/abs/2405.09569","arxiv_id":"2405.09569","published":"2026-01-05"},{"title":"From Transformers to LLMs: A Systematic Survey of Efficiency Considerations in NLP","abstract":"The emergence of Transformer-based Large Language Models (LLMs) has substantially augmented the capabilities of Natural Language Processing (NLP), thereby intensifying the demand for computational resources. Therefore, enhancing efficiency based on factors like computational requirements, energy consumption, carbon footprint and financial cost has become a vital area of research. This motivates us to conduct a system","authors":["Ansar, Wazib","Goswami, Saptarsi","Chakrabarti, Amlan"],"url":"https://arxiv.org/abs/2406.16893","arxiv_id":"2406.16893","published":"2026-01-05"},{"title":"Test-time generative augmentation for medical image segmentation","abstract":"Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. Traditional test-time augmentation (TTA) techniques typically rely on predefined geometric and photometric transformations, limiting their adaptability and effectiveness in complex","authors":["Ma, Xiao","Tao, Yuhui","Zhang, Zetian","Zhang, Yuhan","Wang, Xi","Zhang, Sheng"],"url":"https://arxiv.org/abs/2406.17608","arxiv_id":"2406.17608","published":"2026-01-05"},{"title":"LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid","abstract":"Large language models (LLMs) have shown immense potential across various domains, but their high memory requirements and inference costs remain critical challenges for deployment. Post-training quantization (PTQ) has emerged as a promising technique to reduce memory requirements and decoding latency. However, recent accurate quantization methods often depend on specialized computations or custom data formats to achie","authors":["Zhang, Tianyi","Shrivastava, Anshumali"],"url":"https://arxiv.org/abs/2407.10032","arxiv_id":"2407.10032","published":"2026-01-05"},{"title":"A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism","abstract":"Neural architecture search (NAS) has emerged as a powerful paradigm that enables researchers to automatically explore vast search spaces and discover efficient neural networks. However, NAS suffers from a critical bottleneck, i.e. the evaluation of numerous architectures during the search process demands substantial computing resources and time. In order to improve the efficiency of NAS, a series of methods have been","authors":["Xue, Yu","Jiang, Pengcheng","Zhu, Chenchen","Zhou, MengChu","Wahib, Mohamed","Gabbouj, Moncef"],"url":"https://arxiv.org/abs/2407.15600","arxiv_id":"2407.15600","published":"2026-01-05"},{"title":"CIC: Circular Image Compression","abstract":"Learned image compression (LIC) is currently the cutting-edge method. However, the inherent difference between testing and training images of LIC results in performance degradation to some extent. Especially for out-of-sample, out-of-distribution, or out-of-domain testing images, the performance of LIC degrades significantly. Classical LIC is a serial image compression (SIC) approach that utilizes an open-loop archit","authors":["Li, Honggui","Chen, Sinan","Li, Dingtai","Zhang, Zhengyang","Hossain, Nahid Md Lokman","Xu, Xinfeng"],"url":"https://arxiv.org/abs/2407.15870","arxiv_id":"2407.15870","published":"2026-01-05"},{"title":"UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation","abstract":"Background: Gliomas are among the most common malignant brain tumors and exhibit substantial heterogeneity, complicating accurate detection and segmentation. Although multi-modal MRI is the clinical standard for glioma imaging, variability across modalities and high computational demands hamper effective automated segmentation. Methods: We propose UKAN-EP, a novel 3D extension of the original 2D U-KAN model for multi","authors":["Chen, Yanbing","Tang, Tianze","Kim, Taehyo","Shu, Hai"],"url":"https://arxiv.org/abs/2408.00273","arxiv_id":"2408.00273","published":"2026-01-05"},{"title":"EXAONE 3.0 7.8B Instruction Tuned Language Model","abstract":"We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote open research and innovations. Through extensive evaluations across a wide range of public and in-house benchmarks, EXAONE 3.0 demonstrates highly competitive real-world per","authors":["An, Soyoung","Bae, Kyunghoon","Choi, Eunbi","Choi, Stanley Jungkyu","Choi, Yemuk","Hong, Seokhee"],"url":"https://arxiv.org/abs/2408.03541","arxiv_id":"2408.03541","published":"2026-01-05"},{"title":"Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets","abstract":"In the Newsvendor problem, the goal is to guess the number that will be drawn from some distribution, with asymmetric consequences for guessing too high vs. too low. In the data-driven version, the distribution is unknown, and one must work with samples from the distribution. Data-driven Newsvendor has been studied under many variants: additive vs. multiplicative regret, high probability vs. expectation bounds, and d","authors":["Chen, Zhuoxin","Ma, Will"],"url":"https://arxiv.org/abs/2409.03505","arxiv_id":"2409.03505","published":"2026-01-05"},{"title":"Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model","abstract":"For reasons such as privacy, there are use cases for language models at the edge. This has given rise to small language models targeted for deployment in resource-constrained devices where energy efficiency is critical. Spiking neural networks (SNNs) offer a promising solution due to their energy efficiency, and there are already works on realizing transformer-based models on SNNs. However, key operations like softma","authors":["Tang, Kaiwen","Yan, Zhanglu","Wong, Weng-Fai"],"url":"https://arxiv.org/abs/2409.15298","arxiv_id":"2409.15298","published":"2026-01-05"},{"title":"CloudTrack: Scalable UAV Tracking with Cloud Semantics","abstract":"Nowadays, unmanned aerial vehicles (UAVs) are commonly used in search and rescue scenarios to gather information in the search area. The automatic identification of the person searched for in aerial footage could increase the autonomy of such systems, reduce the search time, and thus increase the missed person's chances of survival. In this paper, we present a novel approach to perform semantically conditioned op","authors":["Blei, Yannik","Krawez, Michael","Nilavadi, Nisarga","Kaiser, Tanja Katharina","Burgard, Wolfram"],"url":"https://arxiv.org/abs/2409.16111","arxiv_id":"2409.16111","published":"2026-01-05"},{"title":"Mitigating optimistic bias in entropic risk estimation and optimization","abstract":"The entropic risk measure is widely used in high-stakes decision-making across economics, management science, finance, and safety-critical control systems because it captures tail risks associated with uncertain losses. However, when data are limited, the empirical entropic risk estimator, formed by replacing the expectation in the risk measure with a sample average, underestimates true risk. We show that this negati","authors":["Sadana, Utsav","Delage, Erick","Georghiou, Angelos"],"url":"https://arxiv.org/abs/2409.19926","arxiv_id":"2409.19926","published":"2026-01-05"},{"title":"Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture","abstract":"Large Language Models (LLMs) utilize extensive knowledge databases and show powerful text generation ability. However, their reliance on high-quality copyrighted datasets raises concerns about copyright infringements in generated texts. Current research often employs prompt engineering or semantic classifiers to identify copyrighted content, but these approaches have two significant limitations: (1) Challenging to id","authors":["Ma, Qichao","Zhu, Rui-Jie","Liu, Peiye","Yan, Renye","Zhang, Fahong","Liang, Ling"],"url":"https://arxiv.org/abs/2410.04454","arxiv_id":"2410.04454","published":"2026-01-05"},{"title":"Sketch to Adapt: Fine-Tunable Sketches for Efficient LLM Adaptation","abstract":"Adapting pre-trained large language models (LLMs) is crucial but challenging due to their enormous size. Parameter-efficient fine-tuning (PEFT) techniques typically employ additive adapters applied to frozen model weights. To further reduce memory usage, model weights are often compressed through quantization. However, existing PEFT methods often yield suboptimal model quality because they rely on restrictive assumpt","authors":["Zhang, Tianyi","Su, Junda","Desai, Aditya","Wu, Oscar","Xu, Zhaozhuo","Shrivastava, Anshumali"],"url":"https://arxiv.org/abs/2410.06364","arxiv_id":"2410.06364","published":"2026-01-05"}],"history":{"events":[{"year":2009,"text":"In Eng Foong Ho v Attorney-General, the Court of Appeal of Singapore held that equality before the law was satisfied by a \"reasonable nexus\" between state action and the object of the law.","url":"https://en.wikipedia.org/wiki/Eng_Foong_Ho_v_Attorney-General"},{"year":2008,"text":"Mikheil Saakashvili was decisively re-elected as President of Georgia in \"the first genuinely competitive presidential election\" in the history of the country.","url":"https://en.wikipedia.org/wiki/Mikheil_Saakashvili"},{"year":2007,"text":"The Taiwan High Speed Rail opened, connecting Taipei and Kaohsiung.","url":"https://en.wikipedia.org/wiki/Taiwan_High_Speed_Rail"},{"year":2005,"text":"The dwarf planet Eris was discovered by a team using images from the Samuel Oschin telescope at Palomar Observatory.","url":"https://en.wikipedia.org/wiki/Dwarf_planet"},{"year":2003,"text":"The Metropolitan Police arrested six people in conjunction with an alleged terrorist plot to release ricin on the London Underground, although no toxin was found.","url":"https://en.wikipedia.org/wiki/Metropolitan_Police"},{"year":2000,"text":"Sri Lankan Tamil politician Kumar Ponnambalam was killed in an assassination suspected to have been sanctioned by President Chandrika Kumaratunga.","url":"https://en.wikipedia.org/wiki/Sri_Lankan_Tamils"},{"year":1991,"text":"Georgian troops attacked Tskhinvali, the South Ossetian capital, beginning the First South Ossetia War.","url":"https://en.wikipedia.org/wiki/Tskhinvali"},{"year":1991,"text":"The embassy of the United States to Somalia was evacuated by helicopter airlift days after violence enveloped Mogadishu during the Somali Civil War.","url":"https://en.wikipedia.org/wiki/Embassy_of_the_United_States%2C_Mogadishu"},{"year":1976,"text":"The Troubles: In response to the killings of six Catholics the previous night, South Armagh Republican Action Force gunmen killed ten Protestants in County Armagh, Northern Ireland.","url":"https://en.wikipedia.org/wiki/The_Troubles"},{"year":1975,"text":"The bulk carrier Lake Illawarra struck a bridge over the River Derwent in Hobart, Australia, causing the deaths of seven of the ship's crewmen and five motorists on the bridge.","url":"https://en.wikipedia.org/wiki/SS_Lake_Illawarra"},{"year":1970,"text":"A magnitude-7.1 earthquake struck Tonghai County in southern China, killing at least 10,000 people and eventually spurring the creation of the nation's largest earthquake monitoring system.","url":"https://en.wikipedia.org/wiki/1970_Tonghai_earthquake"},{"year":1949,"text":"In his State of the Union speech, U.S. president Harry S. Truman (pictured) announced: \"Every segment of our population, and every individual, has a right to expect from his government a fair deal.\"","url":"https://en.wikipedia.org/wiki/State_of_the_Union"},{"year":1941,"text":"Second World War: Australian and British troops defeated Italian forces in Bardia, Libya, the first battle of the war in which an Australian Army formation took part.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1933,"text":"Construction began on the Golden Gate Bridge across the Straits of the Golden Gate, the entrance to San Francisco Bay.","url":"https://en.wikipedia.org/wiki/Golden_Gate_Bridge"},{"year":1925,"text":"Nellie Tayloe Ross was inaugurated as Governor of Wyoming, the first woman to serve as the governor of a U.S. state.","url":"https://en.wikipedia.org/wiki/Nellie_Tayloe_Ross"},{"year":1922,"text":"Antarctic explorer Ernest Shackleton (pictured) died of a heart attack during his final expedition.","url":"https://en.wikipedia.org/wiki/Ernest_Shackleton"},{"year":1919,"text":"The German Workers' Party, the precursor of the Nazi Party, was founded by Anton Drexler.","url":"https://en.wikipedia.org/wiki/German_Workers'_Party"},{"year":1869,"text":"Te Kooti's War: After surviving a five-day siege in the pā at Ngātapa, Māori leader Te Kooti escaped from New Zealand's Armed Constabulary.","url":"https://en.wikipedia.org/wiki/Te_Kooti's_War"}],"births":[{"year":2009,"text":"Walker Scobell, American actor","url":"https://en.wikipedia.org/wiki/Walker_Scobell"},{"year":2004,"text":"Shane Wright, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Shane_Wright"},{"year":2001,"text":"Mykhailo Mudryk, Ukrainian footballer","url":"https://en.wikipedia.org/wiki/Mykhailo_Mudryk"},{"year":2001,"text":"Ellis Simms, English footballer","url":"https://en.wikipedia.org/wiki/Ellis_Simms"},{"year":2000,"text":"Gastón Martirena, Uruguayan footballer","url":"https://en.wikipedia.org/wiki/Gast%C3%B3n_Martirena"},{"year":1999,"text":"Mattias Svanberg, Swedish footballer","url":"https://en.wikipedia.org/wiki/Mattias_Svanberg"},{"year":1999,"text":"Filip Ugrinić, Swiss footballer","url":"https://en.wikipedia.org/wiki/Filip_Ugrini%C4%87"},{"year":1998,"text":"Carles Aleñá, Spanish footballer","url":"https://en.wikipedia.org/wiki/Carles_Ale%C3%B1%C3%A1"}],"deaths":[{"year":2025,"text":"Mike Rinder, Australian-American former Scientologist, critic (born 1955)","url":"https://en.wikipedia.org/wiki/Mike_Rinder"},{"year":2025,"text":"Costas Simitis, Greek economist, lawyer, and politician, Prime Minister of Greece (born 1936)","url":"https://en.wikipedia.org/wiki/Costas_Simitis"},{"year":2024,"text":"Joseph Lelyveld, American journalist and executive editor of The New York Times (born 1937)","url":"https://en.wikipedia.org/wiki/Joseph_Lelyveld"},{"year":2024,"text":"Mário Zagallo, Brazilian footballer and manager (born 1931)","url":"https://en.wikipedia.org/wiki/M%C3%A1rio_Zagallo"},{"year":2022,"text":"Kim Mi-soo, South Korean actress and model (born 1992)","url":"https://en.wikipedia.org/wiki/Kim_Mi-soo"},{"year":2021,"text":"Colin Bell, English footballer (born 1946)","url":"https://en.wikipedia.org/wiki/Colin_Bell_(footballer%2C_born_1946)"},{"year":2021,"text":"John Georgiadis, English violinist and composer (born 1939)","url":"https://en.wikipedia.org/wiki/John_Georgiadis"},{"year":2020,"text":"Tafazzul Haque Habiganji, Bangladeshi Islamic scholar and politician (born 1938)","url":"https://en.wikipedia.org/wiki/Hefazat-e-Islam_Bangladesh"}]},"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."}