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Related Articles from SNS
Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions
Announce Type: replace Abstract: Data-driven machine learning (ML) models, such as FuXi, exhibit notable limitations in forecasting typhoon intensity and structure. This study presents a comprehensive evaluation of FuXi-SHTM, a hybrid ML-physics model, using all 2024 western North Pacific typhoon cases. The FuXi-SHTM hybrid demonstrates clear improvements in both track and intensity forecasts compared to the standalone SHTM, FuXi, and ECMWF HRES models.
Towards Post-Quantum Secure Pharmacovigilance with ML-KEM and ML-DSA
arXiv:2606.09412v1 Announce Type: new Abstract: Pharmacovigilance systems handle sensitive healthcare and drug-safety data, including adverse event reports and clinical observations. As quantum computing advances, classical public-key cryptographic systems such as RSA and elliptic-curve cryptography may become vulnerable, creating long-term risks for healthcare data that must remain confidential for many years. This paper presents an educational prototype of a post-quantum secure...
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
arXiv:2411.06469v2 Announce Type: replace Abstract: Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question is: Can LLMs beat traditional ML models in clinical prediction?
Position: Adversarial ML for LLMs Is Not Making Any Progress
Announce Type: replace Abstract: In the past decade, considerable research effort has been devoted to securing machine learning (ML) models that operate in adversarial settings. Yet, progress has been slow even for simple "toy" problems (e.g., robustness to small adversarial perturbations) and is often hindered by non-rigorous evaluations. Today, adversarial ML research has shifted towards studying larger, general-purpose language models.
Reducing the GPU Memory Bottleneck with Lossless Compression for ML -- Extended
arXiv:2605.30728v1 Announce Type: new Abstract: Machine learning (ML) training and inference often process data sets far exceeding GPU memory capacity, forcing them to rely on PCIe for on-demand tensor transfers, causing critical transfer bottlenecks. Lossy compression has been proposed to relieve bottlenecks but introduces workload-dependent accuracy loss, making it complex or even prohibitive to use in existing ML deployments.
Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads
new Abstract: The rapid growth of large-scale machine learning (ML) has made distributed training across multiple GPUs a fundamental component of modern ML systems. As model sizes and computational throughput continue to increase, communication overhead has become a dominant bottleneck in multi-GPU training, particularly when computation and communication are executed sequentially. This work explores concurrent execution of computation and collective communication using two portable runtime...
Comparing ML-Specific and General Python Code Smells Across Project Characteristics
arXiv:2606.01882v1 Announce Type: new Abstract: Machine learning systems consist of general-purpose code as well as machine-learning-specific code. While ML-specific code smells have been identified, their connection to project characteristics and their interaction with overall code quality are not well understood.
Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems
arXiv:2603.24963v3 Announce Type: replace Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization events. To support a wide variety of product surfaces and advertiser goals, these platforms frequently maintain an extensive ecosystem of machine learning (ML) models. However, operating at this scale creates significant development and efficiency challenges.
Short paper: Models in the dark -- Rectification and erasure under GDPR in ML supply chains
arXiv:2606.05946v1 Announce Type: new Abstract: The rights to rectification and erasure, as established under the General Data Protection Regulation (GDPR), are central to protecting individuals' privacy. However, their effective enforcement in machine learning (ML) systems remains challenging. Existing work has largely addressed these rights from either a legal or a technical perspective in isolation and disregards the fact that models are produced in complex supply chains involving...
FDM: A Framework for Decision-making to build ML-based Malware detection systems
arXiv:2606.06894v1 Announce Type: new Abstract: Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts.