971601 results (page 41 of 38865)
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MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding
MER2026 marks the fourth edition of the MER series of challenges. The MER series provides valuable data resources to the research community and offers tasks centered on recent research trends, establishing itself as one of the largest challenges in the field. Throughout its history, the focus of MER has shifted from discriminative emotion recognition to generative emotion understanding. Specifical…
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CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation
Sequential Recommendation (SR) aims to predict the next interaction of a user based on their behavior sequence, where complementary relations often provide essential signals for predicting the next item. However, mainstream models relying on sparse co-purchase statistics often mistake spurious correlations (e.g., due to popularity bias) for true complementary relations. Identifying true complement…
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Oort Cloud Ecology - IV. Exchanging Asteroids
Aims. Investigate the influence of cluster environments on asteroids, with special attention towards captured material. Methods. Using numerical methods, a sub-virial fractally distributed star-forming region and a virialised Plummer distributed star-forming region are simulated. Both models are initialised with a virial radius of 0.5pc and 150 stars. Stellar populations and their corresponding pl…
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VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing
Large vision-language models (LVLMs) frequently suffer from Object Hallucination (OH), wherein they generate descriptions containing objects that are not actually present in the input image. This phenomenon is particularly problematic in real-world applications such as medical imaging and autonomous driving, where accuracy is critical. Recent studies suggest that the hallucination problem may stem…
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GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
Understanding road scenes in a geometrically consistent, scene-centric representation is crucial for planning and mapping. We present GOLD-BEV, a framework that learns dense bird's-eye-view (BEV) semantic environment maps-including dynamic agents-from ego-centric sensors, using time-synchronized aerial imagery as supervision only during training. BEV-aligned aerial crops provide an intuitive targe…
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Understanding Password Preferences, Memorability, and Security through a Human-Centered Lens
Passwords remain the primary authentication method, yet user-created passwords are often the weakest due to the security-usability trade-off. Although AI-based password generators are emerging, little is known about their effectiveness and user perceptions. This eye-tracking study examined how behavior during password creation, selection, and memorization relates to objective and subjective passwo…
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HP-Edit: A Human-Preference Post-Training Framework for Image Editing
Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (RL) methods such as Diffusion-DPO and Flow-GRPO have further improved generation quality, efficiently applying Reinforcement Learning from Human Feedback (RLHF) to diffusion-based editing remains largely unexplored, due …
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Lost in Translation: Do LVLM Judges Generalize Across Languages?
Automatic evaluators such as reward models play a central role in the alignment and evaluation of large vision-language models (LVLMs). Despite their growing importance, these evaluators are almost exclusively assessed on English-centric benchmarks, leaving open the question of how well these evaluators generalize across languages. To answer this question, we introduce MM-JudgeBench, the first lar…
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M$^{2}$GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit
Traditional policy learning methods in cooperative pursuit face fundamental challenges in biomimetic underwater robots, where long-horizon decision making, partial observability, and inter-robot coordination require both expressiveness and stability. To address these issues, a novel framework called Mamba-based multi-agent group relative policy optimization (M$^{2}$GRPO) is proposed, which integra…
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VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets
Accurate cardiac anatomy modeling requires the model to be able to handle intricate interrelations among structures. In this paper, we propose VecHeart, a unified framework for holistic reconstruction and generation of four-chamber cardiac structures. To overcome the limitations of current feed-forward implicit methods, specifically their restriction to single-object modeling and their neglect of …
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Neural posterior estimation of the neutrino direction in IceCube using transformer-encoded normalizing flows on the sphere
IceCube is a cubic-kilometer-scale neutrino detector located at the geographic South Pole. A precise directional reconstruction of IceCube neutrinos is vital for associations with astronomical objects. In this context, we discuss neural posterior estimation of the neutrino direction via a transformer encoder that maps to a normalizing flow on the 2-sphere. It achieves a new state-of-the-art angula…
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IZ Tel and UW Vir: Southern oscillating eclipsing Algol systems with active mass transfer
This study is an in-depth examination of IZ Tel and UW Vir which are semi-detached oscillating Eclipsing Algol binary systems (oEA stars). The radial velocities of both components of each system were derived using spectra observed with the Australian National University's 2.3 m telescope. The spectral types of the IZ Tel primary and secondary components were determined as F2V and K2IV; and those o…
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Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding
Knowledge Graph Embeddings (KGEs) support a wide range of downstream tasks over Knowledge Graphs (KGs). In practice, KGs evolve as new entities and facts are added, motivating Continual Knowledge Graph Embedding (CKGE) methods that update embeddings over time. Current CKGE approaches address catastrophic forgetting (i.e., the performance degradation on previously learned tasks) primarily by limiti…
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CASCADE: Detecting Inconsistencies between Code and Documentation with Automatic Test Generation
Maintaining consistency between code and documentation is a crucial yet frequently overlooked aspect of software development. Even minor mismatches can confuse API users, introduce new bugs, and increase overall maintenance effort. This creates demand for automated solutions that can assist developers in identifying code-documentation inconsistencies. However, since automatic reports still require…
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Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically consists of two phases: (i) distributing the global model from a server to clients, and (ii) collecting updated local models from clients to the server for aggregation. This paper focuses on a type of FL where communication between a client and the se…
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GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models
Large language models (LLMs) are expensive to serve because model parameters, attention computation, and KV caches impose substantial memory and latency costs. We present GRASPrune, a structured pruning framework applied after pretraining that jointly prunes FFN channels and KV head groups under a single global budget. Instead of learning importance scores without constraints and applying the budg…
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VIVA Stimuli: A Web-Based Platform for Eye Tracking Stimuli
Reproducibility in eye-tracking research is increasingly important as researchers conduct diverse experiments and seek to validate or replicate findings. However, exact replication remains challenging due to differences in laboratory practices and experimental setups. Inconsistent stimulus presentation can yield divergent metrics from identical oculomotor behavior, yet the stimulus layer remains l…
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Does Self-Consistency Improve the Recall of Encyclopedic Knowledge?
While self-consistency is known to improve performance on symbolic reasoning, its effect on the recall of encyclopedic knowledge is unclear due to a lack of targeted evaluation grounds. To address this, we establish such a knowledge recall split for the popular MMLU benchmark by applying a data-driven heuristic from prior work. We validate this split by showing that the performance patterns on the…
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Can Continual Pre-training Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain?
This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via continual pre-training and merging. We address the scarcity of specialized non-English data by constructing a high-quality German medical corpus (FineMed-de) from FineWeb2. This corpus is used to continually pre-train and merge three well-known LLMs…
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Model-independent consistency tests of DESI DR2 BAO and SN Ia
Cosmic distances can be measured using two complementary probes: Type Ia supernovae (SN Ia), serving as standard candles, and baryon acoustic oscillations (BAO), serving as standard rulers. The luminosity distance derived from supernovae and the angular diameter distance obtained from BAO must be mutually consistent if these data are to be combined for cosmological inference. Hence, the existence …
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HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition
Satellite image composition plays a critical role in remote sensing applications such as data augmentation, disaste simulation, and urban planning. We propose HarmoniDiff-RS, a training-free diffusion-based framework for harmonizing composite satellite images under diverse domain conditions. Our method aligns the source and target domains through a Latent Mean Shift operation that transfers radiom…
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On the Practical Performance of Noise Modulation for Ultra-Low-Power IoT: Limitations, Capacity, and Energy Trade-offs
Ultra-low-power (ULP) Internet of Things (IoT) applications demand communication architectures with minimal energy consumption. Noise Modulation (NoiseMod) addresses this by encoding data through the statistical variance of a noise-like signal, eliminating the need for a coherent carrier. To bridge the gap between theoretical potential and practical deployment, this paper benchmarks NoiseMod again…
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Towards Formalising Stakeholder Context using SysML v2
This paper presents a framework to bridge the gap between subjective stakeholder context and formal system architecture. This is achieved using Soft Systems Methodology (SSM) and Systems Modelling Language version 2 (SysML v2). The methodology utilises the precision of Kernel Modelling Language (KerML) and the alignment of SysML v2 with ISO 42010 to define a reference architecture for the mapping …
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Blockage-Aware and Shadowing Aware RIS Assisted Joint Communication and Positioning for Urban Non Terrestrial Networks
Reconfigurable intelligent surfaces (RISs) have recently attracted interest for non-terrestrial networks (NTNs), especially for improving satellite communication performance. However, RIS-assisted urban NTN designs that jointly support reliable communication and user positioning under blockage, while maintaining low online complexity, remain limited. This paper proposes a blockage-aware and shadow…
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Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval
Composed Image Retrieval (CIR) has attracted significant attention due to its flexible multimodal query method, yet its development is severely constrained by the Noisy Triplet Correspondence (NTC) problem. Most existing robust learning methods rely on the "small loss hypothesis", but the unique semantic ambiguity in NTC, such as "partial matching", invalidates this assumption, leading to unreliab…