2024
Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade
Jiang Z, Carlson B, Deiana A, Eastlack J, Hauck S, Hsu S, Narayan R, Parajuli S, Yin D, Zuo B. Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade. Journal Of Instrumentation 2024, 19: p05031. DOI: 10.1088/1748-0221/19/05/p05031.Peer-Reviewed Original ResearchProcessing tasksMachine learningComplexity of algorithm designIndividuals process tasksSignal processing tasksVolume of dataReal-time processingMachine learning algorithmsMachine learning evaluationLearning algorithmsOverall latencyFiltering decisionsFiltering taskATLAS experimentAlgorithm designEvent processorProcessing platformHigh energy physics applicationsFPGALarge Hadron ColliderAlgorithmResource utilizationMachineTaskHadron ColliderResponse to: MiniMed 780G System Outperforms Other Automated Insulin Systems Due to Algorithm Design, Not Bias—Response to Inaccurate Allegations
Forlenza G, Sherr J. Response to: MiniMed 780G System Outperforms Other Automated Insulin Systems Due to Algorithm Design, Not Bias—Response to Inaccurate Allegations. Diabetes Technology & Therapeutics 2024, 26: 785-786. PMID: 38512386, DOI: 10.1089/dia.2024.0125.Peer-Reviewed Original ResearchAlgorithm design
2021
Online Resource Allocation Under Partially Predictable Demand
Hwang D, Jaillet P, Manshadi V. Online Resource Allocation Under Partially Predictable Demand. Operations Research 2021, 69: 895-915. DOI: 10.1287/opre.2020.2017.Peer-Reviewed Original ResearchOnline resource allocationOnline algorithmAnalysis of online algorithmsAdversarial modelAdversarial componentResource allocationArrival modelSequence of arrivalsReal-time resource allocationMultiple stopping rulesCapacity scalingStochastic demand modelDesign online algorithmsModel of demandImprove allocation decisionsDemand modelStochastic componentAlgorithm designOnline decisionsAllocation decisionsPredicted demandCustomersOnline allocationUnpredictable componentsAlgorithm
2012
Privacy-preserving heterogeneous health data sharing
Mohammed N, Jiang X, Chen R, Fung B, Ohno-Machado L. Privacy-preserving heterogeneous health data sharing. Journal Of The American Medical Informatics Association 2012, 20: 462-469. PMID: 23242630, PMCID: PMC3628047, DOI: 10.1136/amiajnl-2012-001027.Peer-Reviewed Original ResearchConceptsSet-valued dataDifferential privacyNoise additionPrivacy-preserving mannerAdversary's background knowledgeStrong privacy guaranteesBackground knowledgeHealth data sharingPrivacy modelPrivacy guaranteesSensitive dataData sharingHealthcare dataPrivate mannerAlgorithm designPrivacyRaw dataSynthetic dataAlgorithmHealth dataProbabilistic wayDiscriminative analysisExperimental resultsUseful informationClassification analysis
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