From tiny-AI to lite-AI in edge computing

Abstract: This keynote navigates the transformation of AI models used in edge computing, transitioning from Tiny AI to Lite AI. The discussion commences with the prevalence of Tiny ML in edge computing. Despite its suitability for edge devices, Tiny ML necessitates that developers construct models from scratch, leading to limited capabilities in data extraction. As we progress towards more complex tasks such as federated learning, online learning, and high-resolution image analysis, these constraints have started posing significant challenges. This development has paved the way for a new generation of AI, Lite AI. Lite AI is the process of harnessing large, well-trained models like DenseNet and ResNet, deconstructing them to create more efficient, optimized versions suitable for edge devices. This approach enhances their functionality while maintaining computational efficiency. Various techniques, such as mixed learning, are employed to ensure maximum efficiency. The transition to Lite AI represents a paradigm shift, allowing us to meet the growing demands of edge computing without sacrificing the benefits of complex models. This keynote offers an in-depth understanding of the evolution of AI models for edge computing, showing how we've moved from the era of Tiny AI to the promising future of Lite AI

Preliminary Result: AI-Generated Neutrophil Image using Deep Convolution GAN for Data Augmentation.

GANs (Generative Adversarial Networks) have grown impressively, producing significant photorealistic visuals that imitate the content of datasets they were trained to replicate. A GAN is essentially two neural networks that feed into one other. One generates increasingly accurate data, while the other increases its capacity to classify such data over time. One recurring subject in medical imaging is whether GANs can be as effective at producing usable medical data as they are at producing realistic images. The deep learning model is data-hungry in nature, it requires a lot of example images to train well. However, due to the lack of medical images, data augmentation comes in handy to generate extra medical images using GAN. This paper aims to generate microscopic peripheral blood cell images, specifically neutrophils, as a form of data augmentation to optimize haematological diagnosis. To accomplish this, we developed a Deep Convolution GAN (DCGAN) and trained with 3329 neutrophil images. For the preliminary result, we will present our work on the impact of different learning rates and optimizers of DCGAN on the generated images and training losses. The quality of the generated images is far from perfect from the dataset we want to imitate, also the convergence of the model is slow and not stable. Yet, there were reasonably generated images during the training where the model has a rough idea about the neutrophil structure

INPAINTING OF DENTAL �PANORAMIC TOMOGRAPHY �VIA DEEP LEARNING METHOD

The tradition of image inpainting has existed for a long time; it is used to correct old and corrupted images. In recent times, progress in deep learning allows artificial neural networks to perform inpainting on clinical images to reduce image artifacts. In this paper, we demonstrated how various neural network models could perform inpainting on a dental panoramic tomography that was taken by using cone-beam computed tomography (CBCT). Experiments were done to compare the output of three different artificial neural network models: shallow convolutional autoencoder, deep convolutional autoencoder, and U-Net architecture. The dataset was taken from an open online dataset provided by Noor Medical Imaging Center. Qualitative assessment of the output shows that the U-net model reproduces the best output images with minimal blurriness. This result is also supported by the quantitative measurement, which shows that the U-net model has the smallest mean squared root error and the highest structural similarity index measure. The experiment results give an early indication that it is feasible to use U-Net to fix and reduce any image artifact that occurs in dental panoramic tomography

D-0-Meson R-AA in PbPb collisions at root s(NN)=5.02 TeV and elliptic flow in pPb collisions at root s(NN)=8.16 TeV with CMS

The study of charm production in heavy-ion collisions is considered an excellent probe for the properties of the hot and dense medium created in heavy-ion collisions. Measurements of D0-meson nuclear modification factor can provide strong constraints into the mechanisms of in-medium energy loss and charm flow in the medium. The measurement of D0-meson elliptic flow in pPb collisions helps us understand the strength of charm quarks coupling to significantly reduced systems which demonstrate hydrodynamic properties. In this paper, the measurements of the D0-meson nuclear modification factor in PbPb collisions at 5.02 TeV together with the new measurement of D0-meson elliptic flow in high multiplicity pPb collisions at 5.02 TeV using the two-particle correlation method will be presented

Search for the associated production of the Higgs boson and a vector boson in proton-proton collisions at √s = 13 TeV via Higgs boson decays to τ leptons

A search for the standard model Higgs boson produced in association with a W or a Z boson and decaying to a pair of τ leptons is performed. A data sample of proton-proton collisions collected at √ s = 13 TeV by the CMS experiment at the CERN LHC is used, corresponding to an integrated luminosity of 35.9 fb−1 . The signal strength is measured relative to the expectation for the standard model Higgs boson, yielding µ = 2.5 +1.4 −1.3 . These results are combined with earlier CMS measurements targeting Higgs boson decays to a pair of τ leptons, performed with the same data set in the gluon fusion and vector boson fusion production modes. The combined signal strength is µ = 1.24+0.29 −0.27 (1.00+0.24 −0.23 expected), and the observed significance is 5.5 standard deviations (4.8 expected) for a Higgs boson mass of 125 GeV

Search for Zγ resonances using leptonic and hadronic final states in proton-proton collisions at s√=13 TeV

A search is presented for resonances decaying to a Z boson and a photon. The analysis is based on data from proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb−1, and collected with the CMS detector at the LHC in 2016. Two decay modes of the Z boson are investigated. In the leptonic channels, the Z boson candidates are reconstructed using electron or muon pairs. In the hadronic channels, they are identified using a large-radius jet, containing either light-quark or b quark decay products of the Z boson, via jet substructure and advanced b quark tagging techniques. The results from these channels are combined and interpreted in terms of upper limits on the product of the production cross section and the branching fraction to Zγ for narrow and broad spin-0 resonances with masses between 0.35 and 4.0 TeV, providing thereby the most stringent limits on such resonances

Search for supersymmetry in final states with photons and missing transverse momentum in proton-proton collisions at 13 TeV

Results are reported of a search for supersymmetry in final states with photons and missing transverse momentum in proton-proton collisions at the LHC. The data sample corresponds to an integrated luminosity of 35.9 fb−1 collected at a center-of-mass energy of 13 TeV using the CMS detector. The results are interpreted in the context of models of gauge-mediated supersymmetry breaking. Production cross section limits are set on gluino and squark pair production in this framework. Gluino masses below 1.86 TeV and squark masses below 1.59 TeV are excluded at 95% confidence level

Measurement of the Z/gamma* -> tau tau cross section in pp collisions at root s=13 TeV and validation of tau lepton analysis techniques

A measurement is presented of the Z/γ ∗ → τ τ cross section in pp collisions at √s = 13 TeV, using data recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 2.3 fb−1. The product of the inclusive cross section and branching fraction is measured to be σ (pp → Z/γ ∗+X) B(Z/γ ∗ → ττ) = 1848 ± 12 (stat) ± 67 (syst + lumi) pb, in agreement with the standard model expectation, computed at next-to-next-to-leading order accuracy in perturbative quantum chromodynamics. The measurement is used to validate new analysis techniques relevant for future measurements of τ lepton production. The measurement also provides the reconstruction efficiency and energy scale for τ decays to hadrons + ντ final states, determined with respective relative uncertainties of 2.2 and 0.9

Measurement of inclusive very forward jet cross sections in proton-lead collisions at √sNN = 5.02 TeV

Measurements of differential cross sections for inclusive very forward jet production in proton-lead collisions as a function of jet energy are presented. The data were collected with the CMS experiment at the LHC in the laboratory pseudorapidity range −6.6 < η < −5.2. Asymmetric beam energies of 4 TeV for protons and 1.58 TeV per nucleon for Pb nuclei were used, corresponding to a center-of-mass energy per nucleon pair of sNN = 5.02 TeV. Collisions with either the proton (p+Pb) or the ion (Pb+p) traveling towards the negative η hemisphere are studied. The jet cross sections are unfolded to stable-particle level cross sections with p T ≳ 3 GeV, and compared to predictions from various Monte Carlo event generators. In addition, the cross section ratio of p+Pb and Pb+p data is presented. The results are discussed in terms of the saturation of gluon densities at low fractional parton momenta. None of the models under consideration describes all the data over the full jet-energy range and for all beam configurations. Discrepancies between the differential cross sections in data and model predictions of more than two orders of magnitude are observed.[Figure not available: see fulltext.]. © 2019, The Author(s)

Measurement of the energy density as a function of pseudorapidity in proton-proton collisions at root s=13 TeV

A measurement of the energy density in proton– proton collisions at a centre-of-mass energy of √s = 13 TeV is presented. The data have been recorded with the CMS experiment at the LHC during low luminosity operations in 2015. The energy density is studied as a function of pseudorapidity in the ranges − 6.6 <η< − 5.2 and 3.15 < |η| < 5.20. The results are compared with the predictions of several models. All the models considered suggest a different shape of the pseudorapidity dependence compared to that observed in the data. A comparison with LHC proton–proton collision data at √s = 0.9 and 7 TeV confirms the compatibility of the data with the hypothesis of limiting fragmentation