2839 papers
This paper introduces SonoReasoner, an ultrasound vision-language model that enhances diagnostic performance and consistency by explicitly formulating interpretation as hierarchical clinical reasoning, supported by the SonoFlow-1M corpus, the 7-million-scale SonoCorpus reasoning dataset, and the clinician-curated SonoVQA benchmark.
This paper proposes SLS-DNANet, a novel deep learning framework integrating a Multi-Scale Spatial Attention module and a Scale and Location-Sensitive loss function to significantly enhance the detection accuracy and reduce false alarms for small infrared targets with low pixel occupancy and scale variations.
This paper proposes GSE-SFD, a grouped selective federated distillation framework that integrates out-of-distribution filtering, threshold secret sharing, and anomaly detection to simultaneously achieve high accuracy, robustness against client disconnections and poisoning attacks, and enhanced privacy protection in resource-constrained edge environments.
RubriQ is a scalable, HPC-driven framework that leverages rubric-guided Group Relative Policy Optimization (GRPO) to automate the synthesis of quantum circuits that simultaneously satisfy algorithmic correctness, surface-code cost minimization, and near-term hardware constraints.
This paper proposes a method for estimating the complete 3D shape and pose of transparent objects from a single RGB-D image by leveraging YOLO11 for initial detection and iteratively optimizing a category-specific statistical shape model to overcome the limitations of conventional depth sensors.
CAPE-Net is a conformal, adaptive, and drift-aware early-exit classifier that enables ISPs to perform accurate, certified, and maintainable encrypted traffic classification on packet-sequence metadata by integrating distribution-free error guarantees, abstention on unknown applications, and efficient per-class drift repair within a single compact model.
This paper proposes and evaluates a blockchain-enabled Policy-Based Access Control (PBAC) framework implemented on Hyperledger Besu using Solidity smart contracts to achieve secure, GDPR-compliant, and patient-centric medical data sharing with dynamic consent and transparent auditing, demonstrating a balanced trade-off between performance efficiency and patient autonomy across various consensus mechanisms.
This systematic literature review, adhering to PRISMA 2020 guidelines, analyzes 32 studies on using large language models for mathematical discovery to identify seven technical paradigms, highlight the superior efficacy of evolutionary search combined with formal verification, and provide a taxonomy to guide future research in the field.
This paper proposes an IIoT cyberattack detection method that combines class imbalance processing techniques, such as Borderline-SMOTE, with a CNN–BiLSTM–Attention model to significantly enhance the recognition of minority attack classes and overall detection performance on the TON-IoT dataset.
This paper introduces TrustSLM, a trust-aware framework that aggregates outputs from multiple large language models using reliability metrics to detect hallucinations, identify the accuracy paradox of overconfident errors, and regulate responses through a controlled abstention policy for safer AI deployment.