Search results for "CE implementation"

showing 4 items of 4 documents

Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project

2021

ABSTRACTDiffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project - a large collaborative open-source project which …

Computer scienceopen-source softwaremicrostructureNeurosciences. Biological psychiatry. NeuropsychiatryGrey matter030218 nuclear medicine & medical imagingWhite matterdiffusion MRI03 medical and health sciencesBehavioral Neuroscience0302 clinical medicinebiophysicsmedicineTechnology and CodeReference implementationDiffusion (business)DKIBiological Psychiatrycomputer.programming_languageGround truthmedicine.diagnostic_testMagnetic resonance imagingHuman NeuroscienceBiological tissueInvariant (physics)Python (programming language)Characterization (materials science)pythonDiffusion imagingPsychiatry and Mental healthmedicine.anatomical_structureNeuropsychology and Physiological PsychologyNeurologyDTIKurtosisAlgorithmcomputer030217 neurology & neurosurgeryRC321-571MRITractographyDiffusion MRIFrontiers in Human Neuroscience
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Progressive Stochastic Binarization of Deep Networks

2019

A plethora of recent research has focused on improving the memory footprint and inference speed of deep networks by reducing the complexity of (i) numerical representations (for example, by deterministic or stochastic quantization) and (ii) arithmetic operations (for example, by binarization of weights). We propose a stochastic binarization scheme for deep networks that allows for efficient inference on hardware by restricting itself to additions of small integers and fixed shifts. Unlike previous approaches, the underlying randomized approximation is progressive, thus permitting an adaptive control of the accuracy of each operation at run-time. In a low-precision setting, we match the accu…

FOS: Computer and information sciencesScheme (programming language)Computer Science - Machine LearningComputer scienceStochastic processScalar (physics)Sampling (statistics)Machine Learning (stat.ML)Machine Learning (cs.LG)Statistics - Machine LearningApproximation errorBounded functionReference implementationRepresentation (mathematics)computerAlgorithmcomputer.programming_language2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS)
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The Acts project: track reconstruction software for HL-LHC and beyond

2019

The reconstruction of trajectories of the charged particles in the tracking detectors of high energy physics experiments is one of the most difficult and complex tasks of event reconstruction at particle colliders. As pattern recognition algorithms exhibit combinatorial scaling to high track multiplicities, they become the largest contributor to the CPU consumption within event reconstruction, particularly at current and future hadron colliders such as the LHC, HL-LHC and FCC-hh. Current algorithms provide an extremely high standard of physics and computing performance and have been tested on billions of simulated and recorded data events. However, most algorithms were first written 20 year…

Multi-core processor010308 nuclear & particles physicsEvent (computing)track data analysisPhysicsQC1-999Complex event processing01 natural sciencesprogrammingComputing and ComputersComputer engineeringMultithreading0103 physical sciencesmultiprocessorCERN LHC Coll: upgradeProgramming paradigmThread safety[INFO]Computer Science [cs]data managementReference implementation010306 general physicsnumerical calculationsperformanceactivity reportEvent reconstruction
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Organisational Drivers and Challenges in Circular Economy Implementation : An Issue Life Cycle Approach

2022

Business is a significant cause of various global sustainability challenges addressed by the Circular Economy (CE), making companies instrumental in the transition from a linear economic model to a circular one. While drivers and challenges in corporate CE implementation have been researched on an organisational level, a more detailed understanding is needed regarding their contextual nature and interplay with the evolution of CE implementation in a company. Utilising issue life cycle theory, this study contributes to the literature on CE implementation in business by displaying differences in corporate CE implementation drivers and challenges, relating to the time since starting, and the …

haasteet (ongelmat)Organizational Behavior and Human Resource Managementkestävä kehityscircular economychallengesdriverselinkaariyrityksetliiketoimintayritysvastuukiertotalouskäyttöönottoissue life cycleGeneral Environmental ScienceCE implementation
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