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Waktu: Semua Jam Hari Minggu Bulan Tahun  ·  Bahasa: Indonesia Semua

  1. YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review

    http://arxiv.org/abs/2501.13400v4 · arxiv

    Note: This is a preliminary version of the manuscript. The final, peer-reviewed, and substantially revised version has been published in Jurnal RESTI. Readers are encouraged to access and cite the published version: DOI: https://doi.org/10.29207/resti.v10i2.6598 In the field of deep learning-based

  2. Context in object detection: a systematic literature review

    http://arxiv.org/abs/2503.23249v1 · arxiv

    Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated obj

  3. A Brief Review of Hypernetworks in Deep Learning

    http://arxiv.org/abs/2306.06955v3 · arxiv

    Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and mod

  4. Fluctuations and Higgs mechanism in Under-Doped Cuprates: a Review

    http://arxiv.org/abs/1906.10146v1 · arxiv

    The physics of the pseudo-gap phase of high temperature cuprate superconductors has been an enduring mystery in the past thirty years. The ubiquitous presence of the pseudo-gap phase in under-doped cuprates suggests that its understanding holds a key in unraveling the origin of high temperature supe

  5. Status of the $S_8$ Tension: A 2026 Review of Probe Discrepancies

    http://arxiv.org/abs/2602.12238v2 · arxiv

    The parameter $S_8 \equiv σ_8 (Ω_m/0.3)^{0.5}$ quantifies the amplitude of matter density fluctuations. A persistent discrepancy exists between early-universe CMB observations and late-universe probes. This review assesses the ``$S_8$ tension'' against a new 2026 baseline: a unified ``Combined CMB''

  6. YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

    http://arxiv.org/abs/2406.19407v8 · arxiv

    This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and pr

  7. Reformulation Techniques for Automated Planning: A Systematic Review

    http://arxiv.org/abs/2301.10079v2 · arxiv

    Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal re

  8. How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review

    http://arxiv.org/abs/2503.13467v2 · arxiv

    Background: Metacognition has gained significant attention for its potential to enhance autonomy and adaptability of artificial agents but remains a fragmented field: diverse theories, terminologies, and design choices have led to disjointed developments and limited comparability across systems. Exi

  9. UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering

    http://arxiv.org/abs/2608.27467v1 · arxiv

    We describe the UIC-AIHealth4All system for ArchEHR-QA 2026, a shared task on grounded question answering from electronic health records. We participated in Subtasks 2 (evidence identification), 3 (answer generation), and 4 (answer-evidence alignment). For Subtasks 2 and 3, we propose an answer-firs

  10. AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality

    http://arxiv.org/abs/2608.03581v1 · arxiv

    AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying

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