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Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric de","authors":["Patro, Gourab K","Biswas, Arpita","Ganguly, Niloy","Gummadi, Krishna P.","Chakraborty, Abhijnan"],"url":"https://arxiv.org/abs/2002.10764","arxiv_id":"2002.10764","published":"2026-02-26"},{"title":"Private Blind Model Averaging - Distributed, Non-interactive, and Convergent","abstract":"Distributed differentially private learning techniques enable a large number of users to jointly learn a model without having to first centrally collect the training data. At the same time, neither the communication between the users nor the resulting model shall leak information about the training data. This kind of learning technique can be deployed to edge devices if it can be scaled up to a large number of users,","authors":["Kirschte, Moritz","Meiser, Sebastian","Ardalan, Saman","Mohammadi, Esfandiar"],"url":"https://arxiv.org/abs/2211.02003","arxiv_id":"2211.02003","published":"2026-02-26"},{"title":"Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks","abstract":"Motivated by applications such as machine repair, project monitoring, and anti-poaching patrol scheduling, we study intervention planning of stochastic processes under resource constraints. This planning problem has previously been modeled as restless multi-armed bandits (RMAB), where each arm is an intervention-dependent Markov Decision Process. However, the existing literature assumes all intervention resources bel","authors":["Biswas, Arpita","Killian, Jackson A.","Diaz, Paula Rodriguez","Ghosh, Susobhan","Tambe, Milind"],"url":"https://arxiv.org/abs/2303.00799","arxiv_id":"2303.00799","published":"2026-02-26"},{"title":"The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucination","abstract":"With the launch of ChatGPT, Large Language Models (LLMs) are shaking up our whole society, rapidly altering the way we think, create and live. For instance, the GPT integration in Bing has altered our approach to online searching. While nascent LLMs have many advantages, new legal and ethical risks are also emerging, stemming in particular from stochastic parrots and hallucination. The EU is the first and foremost ju","authors":["Li, Zihao"],"url":"https://arxiv.org/abs/2304.14347","arxiv_id":"2304.14347","published":"2026-02-26"},{"title":"Emergence of a phonological bias in ChatGPT","abstract":"Current large language models, such as OpenAI's ChatGPT, have captured the public's attention because how remarkable they are in the use of language. Here, I demonstrate that ChatGPT displays phonological biases that are a hallmark of human language processing. More concretely, just like humans, ChatGPT has a consonant bias. That is, the chatbot has a tendency to use consonants over vowels to identify words. ","authors":["Toro, Juan Manuel"],"url":"https://arxiv.org/abs/2305.15929","arxiv_id":"2305.15929","published":"2026-02-26"},{"title":"Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise","abstract":"This paper introduces two key contributions aimed at improving the speed and quality of images generated through inverse diffusion processes. The first contribution involves reparameterizing the diffusion process in terms of the angle on a quarter-circular arc between the image and noise, specifically setting the conventional $\\displaystyle \\sqrt{\\barα}=\\cos(η)$. This reparameterization eliminates two singularities a","authors":["Zhang, Zhenkai","Ehinger, Krista A.","Drummond, Tom"],"url":"https://arxiv.org/abs/2310.17167","arxiv_id":"2310.17167","published":"2026-02-26"},{"title":"Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network","abstract":"Oscillatory combustion in aero engines and modern gas turbines often has significant adverse effects on their operation, and accurately recognizing various oscillation modes is the prerequisite for understanding and controlling combustion instability. However, the high-dimensional spatial-temporal data of a complex combustion system typically poses considerable challenges to the dynamical mode recognition. Based on a","authors":["Xu, Weiming","Yang, Tao","Zhang, Peng"],"url":"https://arxiv.org/abs/2312.02462","arxiv_id":"2312.02462","published":"2026-02-26"},{"title":"Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration","abstract":"Synthetic control methods (SCMs) are a canonical approach used to estimate treatment effects from panel data in the internet economy. We shed light on a frequently overlooked but ubiquitous assumption made in SCMs of "overlap": a treated unit can be written as some combination -- typically, convex or linear -- of the units that remain under control. We show that if units select their own interventions, and th","authors":["Ngo, Daniel","Harris, Keegan","Agarwal, Anish","Syrgkanis, Vasilis","Wu, Zhiwei Steven"],"url":"https://arxiv.org/abs/2312.16307","arxiv_id":"2312.16307","published":"2026-02-26"},{"title":"Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management","abstract":"Life-cycle management of large-scale transportation systems requires determining a sequence of inspection and maintenance decisions to minimize long-term risks and costs while dealing with multiple uncertainties and constraints that lie in high-dimensional spaces. Traditional approaches have been widely applied but often suffer from limitations related to optimality, scalability, and the ability to properly handle un","authors":["Saifullah, M.","Papakonstantinou, K. G.","Bhattacharya, A.","Stoffels, S. M.","Andriotis, C. P."],"url":"https://arxiv.org/abs/2401.12455","arxiv_id":"2401.12455","published":"2026-02-26"},{"title":"Breaking the HISCO Barrier: Automatic Occupational Standardization with OccCANINE","abstract":"This paper introduces OccCANINE, an open-source tool that maps occupational descriptions to HISCO codes. Manual coding is slow and error-prone; OccCANINE replaces weeks of work with results in minutes. We fine-tune CANINE on 15.8 million description-code pairs from 29 sources in 13 languages. The model achieves 96 percent accuracy, precision, and recall. We also show that the approach generalizes to three systems - O","authors":["Dahl, Christian Møller","Johansen, Torben","Vedel, Christian"],"url":"https://arxiv.org/abs/2402.13604","arxiv_id":"2402.13604","published":"2026-02-26"},{"title":"MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models","abstract":"In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and a state-space component captures the temporal relationship. This transforms the nonlinear system into a linear system in a latent space, enabling the application of model predictiv","authors":["Yan, Jiaqi","Chakrabarty, Ankush","Rupenyan, Alisa","Lygeros, John"],"url":"https://arxiv.org/abs/2404.12097","arxiv_id":"2404.12097","published":"2026-02-26"},{"title":"Real-Time Motion Detection Using Dynamic Mode Decomposition","abstract":"Dynamic Mode Decomposition (DMD) is a numerical method that seeks to fit timeseries data to a linear dynamical system. In doing so, DMD decomposes dynamic data into spatially coherent modes that evolve in time according to exponential growth/decay or with a fixed frequency of oscillation. A prolific application of DMD has been to video, where one interprets the high-dimensional pixel space evolving through time as th","authors":["Mignacca, Marco","Brugiapaglia, Simone","Bramburger, Jason J."],"url":"https://arxiv.org/abs/2405.05057","arxiv_id":"2405.05057","published":"2026-02-26"},{"title":"A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning","abstract":"Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent. To foster future advancements, we provide a detailed overview of the UIE task from several perspectives. Firstly, we introduce the physical models, data construction processes, evaluation metrics, and loss functions. ","authors":["Cong, Xiaofeng","Zhao, Yu","Gui, Jie","Hou, Junming","Tao, Dacheng"],"url":"https://arxiv.org/abs/2405.19684","arxiv_id":"2405.19684","published":"2026-02-26"},{"title":"Multi-Head RAG: Solving Multi-Aspect Problems with LLMs","abstract":"Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by retrieving supporting documents into the prompt, but existing methods do not explicitly target queries that require fetching multiple documents with substantially different content. Such multi-aspect queries are challenging because relevant documents can be far apart in embedding space, making joint retrieval difficult. We introduce Multi-H","authors":["Besta, Maciej","Kubicek, Ales","Gerstenberger, Robert","Chrapek, Marcin","Niggli, Roman","Okanovic, Patrik"],"url":"https://arxiv.org/abs/2406.05085","arxiv_id":"2406.05085","published":"2026-02-26"},{"title":"A Problem-Oriented Perspective and Anchor Verification for Code Optimization","abstract":"Large Language Models (LLMs) have shown remarkable capabilities in solving various programming tasks, such as code generation. However, their potential for code optimization, particularly in performance enhancement, remains largely unexplored. This paper investigates the capabilities of LLMs in optimizing code for minimal execution time, addressing a critical gap in current research. The recently proposed code optimi","authors":["Ye, Tong","Ma, Tengfei","Zhang, Xuhong","Yu, Hang","Yin, Jianwei","Wang, Wenhai"],"url":"https://arxiv.org/abs/2406.11935","arxiv_id":"2406.11935","published":"2026-02-26"},{"title":"Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models","abstract":"Despite the outstanding performance in multimodal tasks, Large Vision-Language Models (LVLMs) have been plagued by the issue of hallucination, i.e., generating content that is inconsistent with the corresponding visual inputs. While previous works have proposed various benchmarks to evaluate this issue, the quality of these evaluations remains unverified. We observe that some of these benchmarks may produce inconsist","authors":["Yan, Bei","Zhang, Jie","Yuan, Zheng","Shan, Shiguang","Chen, Xilin"],"url":"https://arxiv.org/abs/2406.17115","arxiv_id":"2406.17115","published":"2026-02-26"},{"title":"When Can Transformers Count to n?","abstract":"Large language models based on the transformer architecture can solve highly complex tasks, yet their fundamental limitations on simple algorithmic problems remain poorly understood. In this work, we focus on basic counting tasks and investigate how the difficulty of these tasks scales with the transformer embedding dimension, the context length, and the vocabulary size. We reveal a sharp theoretical phase transition","authors":["Yehudai, Gilad","Kaplan, Haim","Dar, Guy","Rassin, Royi","Ghandeharioun, Asma","Geva, Mor"],"url":"https://arxiv.org/abs/2407.15160","arxiv_id":"2407.15160","published":"2026-02-26"},{"title":"Shapley Value Computation in Ontology-Mediated Query Answering","abstract":"The Shapley value was originally introduced in cooperative game theory as a wealth distribution mechanism. It has since found use in knowledge representation and databases for the purpose of assigning scores to formulas and database tuples based upon their contribution to obtaining a query result or inconsistency. The application of the Shapley value outside of its original setting relies upon defining a numeric weal","authors":["Bienvenu, Meghyn","Figueira, Diego","Lafourcade, Pierre"],"url":"https://arxiv.org/abs/2407.20058","arxiv_id":"2407.20058","published":"2026-02-26"},{"title":"Temporal Knowledge-Graph Memory in a Partially Observable Environment","abstract":"Agents in partially observable environments require persistent memory to integrate observations over time. While KGs (knowledge graphs) provide a natural representation for such evolving state, existing benchmarks rarely expose agents to environments where both the world dynamics and the agent's memory are explicitly graph-shaped. We introduce the Room Environment v3, a configurable environment whose hidden state","authors":["Kim, Taewoon","François-Lavet, Vincent","Cochez, Michael"],"url":"https://arxiv.org/abs/2408.05861","arxiv_id":"2408.05861","published":"2026-02-26"},{"title":"PACE: Procedural Abstractions for Communicating Efficiently","abstract":"A central but unresolved aspect of problem-solving in AI is the capability to introduce and use abstractions, something humans excel at. Work in cognitive science has demonstrated that humans tend towards higher levels of abstraction when engaged in collaborative task-oriented communication, enabling gradually shorter and more information-efficient utterances. Several computational methods have attempted to replicate","authors":["Thomas, Jonathan D.","Silvi, Andrea","Dubhashi, Devdatt","Johansson, Moa"],"url":"https://arxiv.org/abs/2409.20120","arxiv_id":"2409.20120","published":"2026-02-26"},{"title":"PoseAdapt: Sustainable Human Pose Estimation via Continual Learning Benchmarks and Toolkit","abstract":"Human pose estimators are typically retrained from scratch or naively fine-tuned whenever keypoint sets, sensing modalities, or deployment domains change--an inefficient, compute-intensive practice that rarely matches field constraints. We present PoseAdapt, an open-source framework and benchmark suite for continual pose model adaptation. PoseAdapt defines domain-incremental and class-incremental tracks that simulate","authors":["Khan, Muhammad Saif Ullah","Stricker, Didier"],"url":"https://arxiv.org/abs/2409.20469","arxiv_id":"2409.20469","published":"2026-02-26"},{"title":"Learning Partial Graph Matching via Optimal Partial Transport","abstract":"Partial graph matching extends traditional graph matching by allowing some nodes to remain unmatched, enabling applications in more complex scenarios. However, this flexibility introduces additional complexity, as both the subset of nodes to match and the optimal mapping must be determined. While recent studies have explored deep learning techniques for partial graph matching, a significant limitation remains: the ab","authors":["Ratnayaka, Gathika","Nichols, James","Wang, Qing"],"url":"https://arxiv.org/abs/2410.16718","arxiv_id":"2410.16718","published":"2026-02-26"},{"title":"A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx","abstract":"The stringent regulatory requirements on nitrogen oxides (NOx) emissions from diesel compression ignition engines require accurate and reliable models for real time monitoring and diagnostics. Although traditional methods such as physical sensors and virtual engine control module (ECM) sensors provide essential data, they are only used for estimation. Ubiquitous literature primarily focuses on deterministic models wi","authors":["Zinage, Shrenik","Bilionis, Ilias","Meckl, Peter"],"url":"https://arxiv.org/abs/2410.18424","arxiv_id":"2410.18424","published":"2026-02-26"},{"title":"Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks","abstract":"Missing values are pervasive in large-scale time-series data, posing challenges for reliable analysis and decision-making. Many neural architectures have been designed to model and impute the complex and heterogeneous missingness patterns of such data. Most existing methods are end-to-end, rendering imputation tightly coupled with downstream predictive tasks and leading to limited reusability of the trained model, re","authors":["Raj, Joseph Arul","Qian, Linglong","Ibrahim, Zina"],"url":"https://arxiv.org/abs/2411.03941","arxiv_id":"2411.03941","published":"2026-02-26"},{"title":"LLM2CLIP: Powerful Language Model Unlocks Richer Cross-Modality Representation","abstract":"CLIP is a seminal multimodal model that maps images and text into a shared representation space through contrastive learning on billions of image-caption pairs. Inspired by the rapid progress of large language models (LLMs), we investigate how the superior linguistic understanding and broad world knowledge of LLMs can further strengthen CLIP, particularly in handling long and complex captions. We introduce an efficie","authors":["Huang, Weiquan","Wu, Aoqi","Yang, Yifan","Luo, Xufang","Yang, Yuqing","Naseem, Usman"],"url":"https://arxiv.org/abs/2411.04997","arxiv_id":"2411.04997","published":"2026-02-26"}],"history":{"events":[{"year":2014,"text":"Former editor-in-chief of Hong Kong newspaper Ming Pao Kevin Lau was stabbed, prompting concerns and protests about media freedom.","url":"https://en.wikipedia.org/wiki/Ming_Pao"},{"year":2013,"text":"A hot air balloon crashed near Luxor, Egypt, killing 19 people in the deadliest ballooning disaster in history.","url":"https://en.wikipedia.org/wiki/2013_Luxor_hot_air_balloon_crash"},{"year":2012,"text":"African-American teenager Trayvon Martin was killed by neighborhood-watch coordinator George Zimmerman in Sanford, Florida, prompting a nationwide controversy.","url":"https://en.wikipedia.org/wiki/Trayvon_Martin"},{"year":2008,"text":"In the first significant cultural visit from the United States to North Korea since the Korean War, the New York Philharmonic Orchestra performed in Pyongyang.","url":"https://en.wikipedia.org/wiki/Korean_War"},{"year":1995,"text":"Barings Bank, the oldest merchant bank in London, was declared insolvent after its head derivatives trader in Singapore, Nick Leeson, lost £827 million while making unauthorised trades on futures contracts.","url":"https://en.wikipedia.org/wiki/Barings_Bank"},{"year":1979,"text":"The Superliner railcar entered revenue service with Amtrak.","url":"https://en.wikipedia.org/wiki/Superliner_(railcar)"},{"year":1936,"text":"Imperial Japanese Army troops attempted a coup d'etat, occupying parts of Tokyo, and killing several politicians, including finance minister Takahashi Korekiyo.","url":"https://en.wikipedia.org/wiki/Imperial_Japanese_Army"},{"year":1935,"text":"Adolf Hitler ordered the Luftwaffe reinstated, violating the Treaty of Versailles signed at the end of World War I.","url":"https://en.wikipedia.org/wiki/Adolf_Hitler"},{"year":1935,"text":"With the aid of a radio station in Daventry, England, and two receiving antennae, Scottish engineer and inventor Robert Watson-Watt first demonstrated the use of radar.","url":"https://en.wikipedia.org/wiki/Daventry"},{"year":1917,"text":"The Original Dixieland Jass Band recorded \"Livery Stable Blues\", the first jazz single ever released.","url":"https://en.wikipedia.org/wiki/Original_Dixieland_Jass_Band"},{"year":1914,"text":"RMS Britannic, the third and largest Olympic-class ocean liner of the White Star Line after RMS Olympic and RMS Titanic, was launched at the Harland & Wolff shipyard in Belfast.","url":"https://en.wikipedia.org/wiki/HMHS_Britannic"},{"year":1903,"text":"Storm Ulysses struck Ireland, felling thousands of trees and causing damage to up to a quarter of all houses in Dublin.","url":"https://en.wikipedia.org/wiki/Storm_Ulysses"},{"year":1815,"text":"Napoleon escaped from the Italian island of Elba (depicted), to which he had been exiled after the signing of the Treaty of Fontainebleau.","url":"https://en.wikipedia.org/wiki/Napoleon"},{"year":1606,"text":"Dutch explorer Willem Janszoon made the first recorded European landing in Australia, although he believed that he was on New Guinea.","url":"https://en.wikipedia.org/wiki/Willem_Janszoon"},{"year":-747,"text":"According to Ptolemy, the reign of the Babylonian king Nabonassar (name in Akkadian pictured) began, marking a new era characterized by the systematic maintenance of chronologically precise historical records.","url":"https://en.wikipedia.org/wiki/740s_BC"}],"births":[{"year":2004,"text":"Bibisara Assaubayeva, Kazakhstani chess grandmaster","url":"https://en.wikipedia.org/wiki/Bibisara_Assaubayeva"},{"year":2003,"text":"Jamal Musiala, German footballer","url":"https://en.wikipedia.org/wiki/Jamal_Musiala"},{"year":2002,"text":"Gerard Martín, Spanish footballer","url":"https://en.wikipedia.org/wiki/Gerard_Mart%C3%ADn_(footballer)"},{"year":2002,"text":"César Tárrega, Spanish footballer","url":"https://en.wikipedia.org/wiki/C%C3%A9sar_T%C3%A1rrega"},{"year":2000,"text":"Yeat, American rapper","url":"https://en.wikipedia.org/wiki/Yeat"},{"year":1997,"text":"Jessie Bates, American football player","url":"https://en.wikipedia.org/wiki/Jessie_Bates"},{"year":1994,"text":"Jacob Trouba, American ice hockey player","url":"https://en.wikipedia.org/wiki/Jacob_Trouba"},{"year":1994,"text":"Mahra Al Maktoum, Emirati princess.","url":"https://en.wikipedia.org/wiki/Mahra_bint_Mohammed_Al_Maktoum"}],"deaths":[{"year":2025,"text":"Michelle Trachtenberg, American actress (born 1985)","url":"https://en.wikipedia.org/wiki/Michelle_Trachtenberg"},{"year":2017,"text":"Joseph Wapner, American judge and TV personality (born 1919)","url":"https://en.wikipedia.org/wiki/Joseph_Wapner"},{"year":2016,"text":"Andy Bathgate, Canadian ice hockey player, coach, and manager (born 1932)","url":"https://en.wikipedia.org/wiki/Andy_Bathgate"},{"year":2016,"text":"Don Getty, Canadian football player and politician, 11th Premier of Alberta (born 1933)","url":"https://en.wikipedia.org/wiki/Don_Getty"},{"year":2015,"text":"Sheppard Frere, English historian and archaeologist (born 1916)","url":"https://en.wikipedia.org/wiki/Sheppard_Frere"},{"year":2015,"text":"Theodore Hesburgh, American priest, theologian, educator, and academic (born 1917)","url":"https://en.wikipedia.org/wiki/Theodore_Hesburgh"},{"year":2015,"text":"Earl Lloyd, American basketball player and coach (born 1928)","url":"https://en.wikipedia.org/wiki/Earl_Lloyd"},{"year":2015,"text":"Tom Schweich, American lawyer and politician, 36th State Auditor of Missouri (born 1960)","url":"https://en.wikipedia.org/wiki/Tom_Schweich"}]},"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."}