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Related Articles from SNS
Kentucky DL dies at 20; no foul play suspected
LEXINGTON, Ky. -- Kentucky defensive lineman Nic Smith has died at age 20, the school announced Monday. School spokesman Jay Blanton said the University of Kentucky Police Department received a call Monday at 10:03 a.m. reporting that a student had been found dead in a residence hall. The university announced Smith's death in a statement later in the day.
A PMP-inspired Evaluation Framework for Assessing Deep-Learning Earth System Models
arXiv:2604.06567v3 Announce Type: replace Abstract: In recent years, Deep-Learning Earth System Models (DL-ESMs) have emerged as promising, computationally efficient complements to traditional Earth system models. Here, we present an evaluation framework for testing DL-ESMs from an Earth system model-development perspective using standardized diagnostics from the PCMDI Metrics Package (PMP). This framework allows DL-ESMs, including Ai2's ACE2 and Google's NeuralGCM, to be assessed with...
Driving licence may remain valid till holder turns 50
New Delhi: Govt is looking at extending the validity period of driving license (DL) from the current 20 years till the time DL holder turns 50, and making the process of transfer of vehicle ownership and renewal of permits completely online, moves which are aimed at eliminating hassles faced by people. TOI has learnt that road transport ministry is working on both proposals and may roll out these for "ease of living" in near future. Officials said state govts' revenues won't be impacted as...
Cross-Domain Federated Semantic Communication with Global Representation Alignment and Domain-Aware Aggregation
Announce Type: replace Abstract: Semantic communication can significantly improve bandwidth utilization in wireless systems by exploiting the meaning behind raw data. However, the advancements achieved through semantic communication are closely dependent on the development of deep learning (DL) models for joint source-channel coding (JSCC) encoder/decoder techniques, which require a large amount of data for training. To address this data-intensive nature of DL models, federated learning (FL)...
Are Deep Learning Based Hybrid PDE Solvers Reliable? Why Training Paradigms and Update Strategies Matter
arXiv:2602.06842v2 Announce Type: replace Abstract: Deep learning-based hybrid iterative methods (DL-HIMs) integrate classical numerical solvers with neural operators, utilizing their complementary spectral biases to accelerate convergence. Despite this promise, many DL-HIMs stagnate at false fixed points where neural updates vanish while the physical residual remains large, raising questions about reliability in scientific computing. In this paper, we provide evidence that performance is...
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
arXiv:2412.06147v2 Announce Type: replace Abstract: For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to improve clinical results significantly. However, they also present unique challenges relating to data integration and ethical issues.
Tensor Algebraic Property Skeletons: Amplifying Property-Based Testing for AI Compilers
Announce Type: new Abstract: Deep learning (DL) compilers such as TVM and ONNX-MLIR lower tensor computation graphs into optimized executables for target backends. Testing these AI compilers has made substantial progress in generating well-formed inputs in the context of fuzzing; however, such generation alone does not catch semantic drifts from algebraic invariants that graph transformations and optimizations are expected to preserve. While tensor algebra has been studied for decades, it...
ViTAMIn-O: Democratizing computer vision-based machine learning for stem cell research
Deep Learning (DL) holds exciting potential in automating the prediction of organoid differentiation results. Nevertheless, current models lack adaptability, openness, and robustness in performance. Additionally, broad employments of predictive models in wet-lab settings necessitate machine learning expertise, often not readily available in biologically oriented laboratories.
A Comparative Study of Deep Learning Models for Geological Carbon Sequestration
Announce Type: new Abstract: Numerical reservoir simulations are extremely computationally expensive, as they require the repeated solution of large nonlinear algebraic systems derived from the discretized governing equations. With growing demand for real-time optimization, uncertainty quantification, and history matching in digital twin applications, reducing computational cost has become essential. Deep learning (DL)--based surrogate models have emerged as an effective approach for...
Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection
arXiv:2606.08033v1 Announce Type: new Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages. Advances in deep learning (DL), particularly convolutional neural networks (CNNs), have enabled scalable solutions for automated crack detection. However, CNN performance strongly depends on the availability of large and diverse datasets, which is particularly challenging for complex surfaces such as masonry.