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Methods for assessing classifier robustness are therefore essential. In this work, we address the challenge of evaluating corruption robustness in a way that allows comparability and interpretability on a given dataset. We propose a test data augmentation method that uses a robustness distance $ε$ der","authors":["Siedel, Georg","Vock, Silvia","Morozov, Andrey","Voß, Stefan"],"url":"https://arxiv.org/abs/2206.13405","arxiv_id":"2206.13405","published":"2026-01-19"},{"title":"A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning","abstract":"Offline reinforcement learning (RL) provides a promising solution to learning an agent fully relying on a data-driven paradigm. However, constrained by the limited quality of the offline dataset, its performance is often sub-optimal. Therefore, it is desired to further finetune the agent via extra online interactions before deployment. Unfortunately, offline-to-online RL can be challenging due to two main challenges:","authors":["Guo, Siyuan","Sun, Yanchao","Hu, Jifeng","Huang, Sili","Chen, Hechang","Piao, Haiyin"],"url":"https://arxiv.org/abs/2306.07541","arxiv_id":"2306.07541","published":"2026-01-19"},{"title":"Feature Propagation on Knowledge Graphs using Cellular Sheaves","abstract":"Many inference tasks on knowledge graphs, including relation prediction, operate on knowledge graph embeddings -- vector representations of the vertices (entities) and edges (relations) that preserve task-relevant structure encoded within the underlying combinatorial object. Such knowledge graph embeddings can be modeled as an approximate global section of a cellular sheaf, an algebraic structure over the graph. Usin","authors":["Cobb, John","Gebhart, Thomas"],"url":"https://arxiv.org/abs/2309.03773","arxiv_id":"2309.03773","published":"2026-01-19"},{"title":"ecoBLE: A Low-Computation Energy Consumption Prediction Framework for Bluetooth Low Energy","abstract":"Bluetooth Low Energy (BLE) is a de-facto technology for Internet of Things (IoT) applications, promising very low energy consumption. However, this low energy consumption accounts only for the radio part, and it overlooks the energy consumption of other hardware and software components. Monitoring and predicting the energy consumption of IoT nodes after deployment can substantially aid in ensuring low energy consumpt","authors":["Schuhmacher, Luisa","Pollin, Sofie","Sallouha, Hazem"],"url":"https://arxiv.org/abs/2309.16686","arxiv_id":"2309.16686","published":"2026-01-19"},{"title":"Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding","abstract":"Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct","authors":["Zhu, Lixing","Zhao, Runcong","Gui, Lin","He, Yulan"],"url":"https://arxiv.org/abs/2310.18783","arxiv_id":"2310.18783","published":"2026-01-19"},{"title":"GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models","abstract":"In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed to address visual grounding in AVs.Our Context-Aware Visual Grounding (CAVG) model is an advanced system that integrates five core encoders-Text, Image, Context, and Cross","authors":["Liao, Haicheng","Shen, Huanming","Li, Zhenning","Wang, Chengyue","Li, Guofa","Bie, Yiming"],"url":"https://arxiv.org/abs/2312.03543","arxiv_id":"2312.03543","published":"2026-01-19"},{"title":"Detecting Toxic Flow","abstract":"This paper develops a framework to predict toxic trades that a broker receives from her clients. Toxic trades are predicted with a novel online learning Bayesian method which we call the projection-based unification of last-layer and subspace estimation (PULSE). PULSE is a fast and statistically-efficient Bayesian procedure for online training of neural networks. We employ a proprietary dataset of foreign exchange tr","authors":["Cartea, Álvaro","Duran-Martin, Gerardo","Sánchez-Betancourt, Leandro"],"url":"https://arxiv.org/abs/2312.05827","arxiv_id":"2312.05827","published":"2026-01-19"},{"title":"Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks","abstract":"We analyze the frequency spectrum of Quantum Neural Networks (QNNs) using Minkowski sums, which yields a compact algebraic description and permits explicit computation. Using this description, we prove several maximality results for broad classes of QNN architectures. Under some mild technical conditions we establish a bijection between classes of models with the same area $A:=R\\cdot L$ that preserves the frequency s","authors":["Holzer, Patrick","Turkalj, Ivica"],"url":"https://arxiv.org/abs/2402.14515","arxiv_id":"2402.14515","published":"2026-01-19"},{"title":"High-Dimensional Tail Index Regression","abstract":"Motivated by the empirical observation of power-law distributions in the credits (e.g., ``likes'') of viral posts in social media, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we propose a regularized estimator, establish its consistency, and derive its convergence rate. Second, we debias the regularized estimator to fac","authors":["Sasaki, Yuya","Tao, Jing","Wang, Yulong"],"url":"https://arxiv.org/abs/2403.01318","arxiv_id":"2403.01318","published":"2026-01-19"},{"title":"Apriori Knowledge in an Era of Computational Opacity: The Role of AI in Mathematical Discovery","abstract":"Can we acquire apriori knowledge of mathematical facts from the outputs of computer programs? People like Burge have argued (correctly in our opinion) that, for example, Appel and Haken acquired apriori knowledge of the Four Color Theorem from their computer program insofar as their program simply automated human forms of mathematical reasoning. However, unlike such programs, we argue that the opacity of modern LLMs ","authors":["Duede, Eamon","Davey, Kevin"],"url":"https://arxiv.org/abs/2403.15437","arxiv_id":"2403.15437","published":"2026-01-19"},{"title":"LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models","abstract":"To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this paper, we address these challenges by proposin","authors":["Peng, Mingxing","Guo, Xusen","Chen, Xianda","Zhu, Meixin","Chen, Kehua"],"url":"https://arxiv.org/abs/2403.18344","arxiv_id":"2403.18344","published":"2026-01-19"},{"title":"Towards Explainable Traffic Flow Prediction with Large Language Models","abstract":"Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results. Achieving both accuracy and explainability in traffic pre","authors":["Guo, Xusen","Zhang, Qiming","Jiang, Junyue","Peng, Mingxing","Zhu, Meixin","Hao"],"url":"https://arxiv.org/abs/2404.02937","arxiv_id":"2404.02937","published":"2026-01-19"},{"title":"Value Improved Actor Critic Algorithms","abstract":"To learn approximately optimal acting policies for decision problems, modern Actor Critic algorithms rely on deep Neural Networks (DNNs) to parameterize the acting policy and greedification operators to iteratively improve it. The reliance on DNNs suggests an improvement that is gradient based, which is per step much less greedy than the improvement possible by greedier operators such as the greedy update used by Q-l","authors":["Oren, Yaniv","Zanger, Moritz A.","van der Vaart, Pascal R.","Celikok, Mustafa Mert","Spaan, Matthijs T. J.","Bohmer, Wendelin"],"url":"https://arxiv.org/abs/2406.01423","arxiv_id":"2406.01423","published":"2026-01-19"},{"title":"Zeroing neural dynamics solving time-variant complex conjugate matrix equation $X(τ)F(τ)-A(τ)\\overline{X}(τ)=C(τ)$","abstract":"Complex conjugate matrix equations (CCME) are important in computation and antilinear systems. Existing research mainly focuses on the time-invariant version, while studies on the time-variant version and its solution using artificial neural networks are still lacking. This paper introduces zeroing neural dynamics (ZND) to solve the earliest time-variant CCME. Firstly, the vectorization and Kronecker product in the c","authors":["He, Jiakuang","Wu, Dongqing"],"url":"https://arxiv.org/abs/2406.12783","arxiv_id":"2406.12783","published":"2026-01-19"},{"title":"SpectralKAN: Weighted Activation Distribution Kolmogorov-Arnold Network for Hyperspectral Image Change Detection","abstract":"Kolmogorov-Arnold networks (KANs) represent data features by learning the activation functions and demonstrate superior accuracy with fewer parameters, FLOPs, GPU memory usage (Memory), shorter training time (TraT), and testing time (TesT) when handling low-dimensional data. However, when applied to high-dimensional data, which contains significant redundant information, the current activation mechanism of KANs leads","authors":["Wang, Yanheng","Yu, Xiaohan","Gao, Yongsheng","Sha, Jianjun","Wang, Jian","Yan, Shiyong"],"url":"https://arxiv.org/abs/2407.00949","arxiv_id":"2407.00949","published":"2026-01-19"},{"title":"Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels","abstract":"Graph anomaly detection (GAD) is widely applied in many areas, such as financial fraud detection and social spammer detection. Anomalous nodes in the graph not only impact their own communities but also create a ripple effect on neighbors throughout the graph structure. Detecting anomalous nodes in complex graphs has been a challenging task. While existing GAD methods assume all labels are correct, real-world scenari","authors":["Wang, Zhu","Dong, Junnan","Zhou, Shuang","Yang, Chang","Zhao, Shengjie","Huang, Xiao"],"url":"https://arxiv.org/abs/2407.05934","arxiv_id":"2407.05934","published":"2026-01-19"},{"title":"HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction","abstract":"Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been developed to measure mutational effects, providing us with extensive datasets across a diverse range of proteins. By training on these data, traditional computational modeling and more recent machine learning approaches have advanced signi","authors":["Visani, Gian Marco","Galvin, William","Jones, Zac","Pun, Michael N.","Daniel, Eric","Borisiak, Kevin"],"url":"https://arxiv.org/abs/2407.06703","arxiv_id":"2407.06703","published":"2026-01-19"},{"title":"FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysis","abstract":"In the field of Image-Text Retrieval (ITR), recent advancements have leveraged large-scale Vision-Language Pretraining (VLP) for Fine-Grained (FG) instance-level retrieval, achieving high accuracy at the cost of increased computational complexity. For Coarse-Grained (CG) category-level retrieval, prominent approaches employ Cross-Modal Hashing (CMH) to prioritise efficiency, albeit at the cost of retrieval performanc","authors":["Williams-Lekuona, Mikel","Cosma, Georgina"],"url":"https://arxiv.org/abs/2407.20114","arxiv_id":"2407.20114","published":"2026-01-19"},{"title":"Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control","abstract":"Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency compared to model-free RL by utilizing a virtual environment model. However, it is challenging to obtain sufficiently accurate representations of the environmental dynamics due to uncertainties in complex systems and environments. An inaccurate environment model may degrade the sample efficiency and performance of model-based R","authors":["Sheng, Zihao","Huang, Zilin","Chen, Sikai"],"url":"https://arxiv.org/abs/2408.17380","arxiv_id":"2408.17380","published":"2026-01-19"},{"title":"Automating Traffic Model Enhancement with AI Research Agent","abstract":"Developing efficient traffic models is crucial for optimizing modern transportation systems. However, current modeling approaches remain labor-intensive and prone to human errors due to their dependence on manual workflows. These processes typically involve extensive literature reviews, formula tuning, and iterative testing, which often lead to inefficiencies. To address this, we propose TR-Agent, an AI-powered frame","authors":["Guo, Xusen","Yang, Xinxi","Peng, Mingxing","Lu, Hongliang","Zhu, Meixin","Yang, Hai"],"url":"https://arxiv.org/abs/2409.16876","arxiv_id":"2409.16876","published":"2026-01-19"},{"title":"Better Language Models Exhibit Higher Visual Alignment","abstract":"How well do text-only large language models (LLMs) align with the visual world? We present a systematic evaluation of this question by incorporating frozen representations of various language models into a discriminative vision-language framework and measuring zero-shot generalization to novel concepts. We find that decoder-based models exhibit stronger visual alignment than encoders, even when controlling for model ","authors":["Ruthardt, Jona","Burghouts, Gertjan J.","Belongie, Serge","Asano, Yuki M."],"url":"https://arxiv.org/abs/2410.07173","arxiv_id":"2410.07173","published":"2026-01-19"},{"title":"Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior SDRTV-to-HDRTV Conversion","abstract":"The rise of HDR-WCG display devices has highlighted the need to convert SDRTV to HDRTV, as most video sources are still in SDR. Existing methods primarily focus on designing neural networks to learn a single-style mapping from SDRTV to HDRTV. However, the limited information in SDRTV and the diversity of styles in real-world conversions render this process an ill-posed problem, thereby constraining the performance an","authors":["He, Gang","Xu, Kepeng","Xu, Li","Wang, Siqi","Yu, Wenxin","Wu, Xianyun"],"url":"https://arxiv.org/abs/2411.10775","arxiv_id":"2411.10775","published":"2026-01-19"},{"title":"V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception","abstract":"Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged; however, these datasets primarily focus on came","authors":["Yang, Lei","Zhang, Xinyu","Li, Jun","Wang, Chen","Ma, Jiaqi","Song, Zhiying"],"url":"https://arxiv.org/abs/2411.10962","arxiv_id":"2411.10962","published":"2026-01-19"},{"title":"FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction","abstract":"Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions, they frequently overlook detailed car-following behaviors","authors":["You, Junwei","Gan, Rui","Tang, Weizhe","Huang, Zilin","Liu, Jiaxi","Jiang, Zhuoyu"],"url":"https://arxiv.org/abs/2411.16747","arxiv_id":"2411.16747","published":"2026-01-19"},{"title":"FOF-X: Towards Real-time Detailed Human Reconstruction from a Single Image","abstract":"We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality results is a persistent challenge, mainly due to the high computational demands of existing 3D representations. To address this, we propose Fourier Occupancy Field (FOF), an efficient 3D representation by learning the Fourier series. The core of FOF is to factorize a 3D occupa","authors":["Feng, Qiao","Yang, Yuanwang","Liu, Yebin","Lai, Yu-Kun","Yang, Jingyu","Li, Kun"],"url":"https://arxiv.org/abs/2412.05961","arxiv_id":"2412.05961","published":"2026-01-19"}],"history":{"events":[{"year":2012,"text":"The Hong Kong–based file-sharing website Megaupload was shut down by the FBI.","url":"https://en.wikipedia.org/wiki/File_sharing"},{"year":2007,"text":"Turkish-Armenian journalist and human-rights activist Hrant Dink was assassinated by a Turkish nationalist in Istanbul.","url":"https://en.wikipedia.org/wiki/Hrant_Dink"},{"year":2007,"text":"A four-man team, using only skis and kites, completed a 1,093-mile (1,759 km) trek to reach the Southern Pole of Inaccessibility, the first people to get there since 1967, and the first to do so on foot.","url":"https://en.wikipedia.org/wiki/Snowkiting"},{"year":2006,"text":"In the deadliest aviation accident in Slovak history, an Antonov An-24 operated by the Slovak Air Force crashed in northern Hungary, killing 42 of the 43 people on board.","url":"https://en.wikipedia.org/wiki/Antonov_An-24"},{"year":1996,"text":"A tank barge and a tug grounded on a beach in Rhode Island, causing a spill of an estimated 828,000 U.S. gallons (3.13 million litres) of home heating oil.","url":"https://en.wikipedia.org/wiki/Barge"},{"year":1977,"text":"Iva Toguri (pictured), convicted of treason for broadcasting Japanese propaganda, was granted a full pardon by U.S. president Gerald Ford.","url":"https://en.wikipedia.org/wiki/Iva_Toguri_D'Aquino"},{"year":1975,"text":"An earthquake registering 6.8 Ms struck northern Himachal Pradesh in India, causing extensive damage to the region.","url":"https://en.wikipedia.org/wiki/1975_Kinnaur_earthquake"},{"year":1972,"text":"The French newspaper L'Aurore revealed that the former Nazi SS officer Klaus Barbie, the \"Butcher of Lyon\", had been found to be living in Peru.","url":"https://en.wikipedia.org/wiki/L'Aurore_(newspaper_founded_1944)"},{"year":1920,"text":"The American Civil Liberties Union was founded by the directors of the National Civil Liberties Bureau.","url":"https://en.wikipedia.org/wiki/American_Civil_Liberties_Union"},{"year":1915,"text":"World War I: The first major attack of the German bombing campaign against Britain took place when Zeppelins bombed several towns in Norfolk.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1909,"text":"A deed was recorded for David Hanbury to sell Island No. 2 in northern California to his brother John for $10 ($358.33 in .mw-parser-output .tooltip-dotted{border-bottom:1px dotted;cursor:help}2025).","url":"https://en.wikipedia.org/wiki/Deed"},{"year":1795,"text":"The Batavian Republic was established the day after William V fled the Dutch Republic as a result of the Batavian Revolution in Amsterdam.","url":"https://en.wikipedia.org/wiki/Batavian_Republic"},{"year":1511,"text":"War of the League of Cambrai: Troops led by Pope Julius II captured Mirandola (depicted) after a brief siege.","url":"https://en.wikipedia.org/wiki/War_of_the_League_of_Cambrai"},{"year":1419,"text":"Hundred Years' War: The siege of Rouen ended with English troops capturing the city from Norman French forces.","url":"https://en.wikipedia.org/wiki/Hundred_Years'_War"}],"births":[{"year":2003,"text":"Felix Afena-Gyan, Ghanaian footballer","url":"https://en.wikipedia.org/wiki/Felix_Afena-Gyan"},{"year":1999,"text":"Jonathan Taylor, American football player","url":"https://en.wikipedia.org/wiki/Jonathan_Taylor"},{"year":1999,"text":"Donyell Malen, Dutch footballer","url":"https://en.wikipedia.org/wiki/Donyell_Malen"},{"year":1998,"text":"Emre Guler, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Emre_Guler"},{"year":1996,"text":"Jakub Jankto, Czech footballer","url":"https://en.wikipedia.org/wiki/Jakub_Jankto"},{"year":1994,"text":"Matthias Ginter, German footballer","url":"https://en.wikipedia.org/wiki/Matthias_Ginter"},{"year":1994,"text":"Alfie Mawson, English footballer","url":"https://en.wikipedia.org/wiki/Alfie_Mawson"},{"year":1994,"text":"Marvelous Nakamba, Zimbabwean footballer","url":"https://en.wikipedia.org/wiki/Marvelous_Nakamba"}],"deaths":[{"year":2026,"text":"Valentino, Italian fashion designer, founder of Valentino (born 1932)","url":"https://en.wikipedia.org/wiki/Valentino_(fashion_designer)"},{"year":2025,"text":"Jeff Torborg, American baseball player and manager (born 1941)","url":"https://en.wikipedia.org/wiki/Jeff_Torborg"},{"year":2017,"text":"Miguel Ferrer, American actor (born 1955)","url":"https://en.wikipedia.org/wiki/Miguel_Ferrer"},{"year":2016,"text":"Richard Levins, American ecologist and geneticist (born 1930)","url":"https://en.wikipedia.org/wiki/Richard_Levins"},{"year":2016,"text":"Ettore Scola, Italian director and screenwriter (born 1931)","url":"https://en.wikipedia.org/wiki/Ettore_Scola"},{"year":2016,"text":"Sheila Sim (Lady Attenborough), English actress (born 1922)","url":"https://en.wikipedia.org/wiki/Sheila_Sim"},{"year":2015,"text":"Justin Capră, Romanian engineer and academic (born 1933)","url":"https://en.wikipedia.org/wiki/Justin_Capr%C4%83"},{"year":2015,"text":"Michel Guimond, Canadian lawyer and politician (born 1953)","url":"https://en.wikipedia.org/wiki/Michel_Guimond"}]},"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."}