KubePACS formulates spot instance selection as a multi-objective ILP problem solved with GSS, integrated with Karpenter, and reports 55% average higher performance per dollar than prior tools.
Gopal Krishna Patro and Kishore Kumar Sahu
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
As we know that the normalization is a pre-processing stage of any type problem statement. Especially normalization takes important role in the field of soft computing, cloud computing etc. for manipulation of data like scale down or scale up the range of data before it becomes used for further stage. There are so many normalization techniques are there namely Min-Max normalization, Z-score normalization and Decimal scaling normalization. So by referring these normalization techniques we are going to propose one new normalization technique namely, Integer Scaling Normalization. And we are going to show our proposed normalization technique using various data sets.
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Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
RecPIE jointly optimizes recommendation predictions and LLM-generated natural-language explanations via alternating training and reinforcement learning, yielding 3-4% accuracy gains and higher human preference on Google Maps POI data.
OpenGLT benchmark finds no single GNN architecture dominates graph-level tasks, with subgraph-based models strongest in expressiveness, graph learning and SSL models in robustness, node and pooling models in efficiency, and graph topology partially guiding architecture choice.
A PINN constrained by the two-component multiplicity model learns the hard-scattering fraction from Zr+Zr events and predicts N_ch more accurately than a data-driven NN on unseen Ru+Ru and Au+Au collisions.
citing papers explorer
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KubePACS: Kubernetes Cluster Using Performant, Highly Available, and Cost Efficient Spot Instances
KubePACS formulates spot instance selection as a multi-objective ILP problem solved with GSS, integrated with Karpenter, and reports 55% average higher performance per dollar than prior tools.
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Long-Term Dynamical Evolution and Ejection of Near-Earth Asteroids
Machine learning classifiers on initial orbital elements and convolutional neural networks on recurrence plots from short integrations classify long-term ejection of near-Earth asteroids with accuracy comparable to full numerical simulations.
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Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations
RecPIE jointly optimizes recommendation predictions and LLM-generated natural-language explanations via alternating training and reinforcement learning, yielding 3-4% accuracy gains and higher human preference on Google Maps POI data.
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OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks
OpenGLT benchmark finds no single GNN architecture dominates graph-level tasks, with subgraph-based models strongest in expressiveness, graph learning and SSL models in robustness, node and pooling models in efficiency, and graph topology partially guiding architecture choice.
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Physics-informed neural network (PINN) modeling of charged particle multiplicity using the two-component framework in heavy-ion collisions: A comparison with data-driven neural networks
A PINN constrained by the two-component multiplicity model learns the hard-scattering fraction from Zr+Zr events and predicts N_ch more accurately than a data-driven NN on unseen Ru+Ru and Au+Au collisions.