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Fine-grained Fragment Retrieval in Multi-modal Long-form Dialogues
arXiv:2606.04591v1 Announce Type: new Abstract: With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. Users often need to retrieve coherent dialogue fragments related to specific topics, rather than isolated utterances. We propose Fine-grained Fragment Retrieval (FFR), which locates semantically relevant multi-utterance, multi-image fragments in multi-modal long-form dialogues.
When Entropy Is Not Enough: Multi-Modal Classification of Encrypted and Compressed Data Fragments
arXiv:2605.31337v1 Announce Type: new Abstract: Reliable identification of encrypted data fragments is essential in cybersecurity, with applications to ransomware detection, digital forensics, and large-scale data analysis. Distinguishing encrypted from compressed fragments is particularly challenging, as short fragments lack structural data and exhibit low statistical redundancy. Traditional statistical methods based on byte-level distributions show limited effectiveness on this task.
The semi-explicit nonsmooth Newmark time integrator for robust unilateral contact in dynamic fragmentation simulations
Announce Type: new Abstract: Numerical simulations of solids undergoing dynamic fragmentation, a problem characterized by dynamic fracture and dense contacts, require accurately capturing the transition from a solid continuum to interacting fragments. We use the finite-element method with the extrinsic cohesive zone model for fracture. For contact, conventional penalty-based methods often exhibit numerical instabilities in dynamic collision-rich settings.
Event-driven dynamic trajectories reconstruction and measurement of mechanical parameters for fragments
arXiv:2606.09208v1 Announce Type: new Abstract: During warhead detonation, high-density, high-speed, and mutually occluded fragments are generated. Their mechanical parameters (position, velocity, kinetic energy) directly determine the lethality of the warhead fragment field.
A Reproducible UAV-Assisted VANET Dataset Generator for Fragmentation Risk Analysis in Intelligent Transportation Systems
arXiv:2606.01488v1 Announce Type: new Abstract: Vehicular Ad Hoc Networks (VANETs) are a key component of Intelligent Transportation Systems, enabling cooperative communication among vehicles and between vehicles and roadside infrastructure. However, their highly dynamic topology makes them vulnerable to network fragmentation, particularly in highway scenarios, low-density traffic conditions, localized accident zones, and communication-stressed environments. Although Unmanned Aerial Vehicles...
Short videos may hinder learning by fragmenting attention and memory, study finds
June 4, 2026 feature Short videos may hinder learning by fragmenting attention and memory, study finds Ingrid Fadelli Author Stephanie Baum Scientific Editor Robert Egan Associate Editor Recent technological advances and the introduction of new digital media platforms have dramatically changed how people learn and source information about topics that interest them. Some recent studies have found that while browsing online or scrolling down social media platforms, users tend to spend under...
Auditable Climate Risk Intelligence from Fragmented ESG Data: Deterministic Orchestration and Imbalance-Aware Learning for Scope 1-3 Validation
Announce Type: new Abstract: ESG and climate risk data remain fragmented across heterogeneous Scope 1, Scope 2, and Scope 3 reporting environments, while conventional validation pipelines lack provenance aware auditability, hidden drift detection, and reproducibility oriented governance. This paper proposes a deterministic climate risk intelligence framework integrating single source of truth orchestration, temporal anomaly detection, imbalance aware ensemble learning, and explainability...
AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading
arXiv:2511.18454v3 Announce Type: replace Abstract: Embryo fragmentation is a morphological indicator critical for evaluating developmental potential in In Vitro Fertilization (IVF). However, manual grading is subjective and inefficient, while existing deep learning solutions often lack clinical explainability or suffer from accumulated errors in segmentation area estimation. To address these issues, this study proposes AttnRegDeepLab (Attention-Guided Regression DeepLab), a framework...
MimeLens: Position-Agnostic Content-Type Detection for Binary Fragments
arXiv:2606.04171v1 Announce Type: new Abstract: File-type classification underlies many workflows like malware triage, forensic carving, packet inspection, and storage indexing. Learned systems such as Google's Magika assume whole-file access at a known offset, so they break on the inputs many of these tasks actually produce, like a single packet payload, a header-less carved fragment, a random disk block, or a chunked upload. We introduce MimeLens, a family of small BERT-style encoders...
AttnRegDeepLab: A Two-Stage Decoupled Framework for Interpretable Embryo Fragmentation Grading
arXiv:2511.18454v4 Announce Type: replace Abstract: Assessing embryo fragmentation is crucial for predicting IVF success, yet manual grading is prone to subjectivity, and existing AI models struggle with clinical interpretability and segmentation errors. We propose AttnRegDeepLab, a Multi-Task Learning (MTL) framework designed to solve these challenges. The model enhances a DeepLabV3+ decoder with Attention Gates to filter out cytoplasmic noise and retain sharp contour details.