FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.

The diphoton $$q_T$$ spectrum at N$$^3$$LL$$^\prime $$ + NNLO

We present a $$q_T$$ q T -resummed calculation of diphoton production at order N$$^3$$ 3 LL$$^\prime $$ ′ + NNLO. To reach the primed level of accuracy we have implemented the recently published three-loop $${\mathcal {O}}(\alpha _s^3)$$ O ( α s 3 ) virtual corrections in the $$q\bar{q}$$ q q ¯ channel and the three-loop transverse momentum dependent beam functions and combined them with the existing infrastructure of , a code performing resummation at order N$${}^3$$ 3 LL. While the primed predictions are parametrically not more accurate, one typically observes from lower orders and other processes that they are the dominant effect of the next order. We include in both the $$q\bar{q}$$ q q ¯ and loop-induced gg channel the hard contributions consistently together at order $$\alpha _s^3$$ α s 3 and find that the resummed $$q\bar{q}$$ q q ¯ channel without matching stabilizes indeed. Due to large matching corrections and large contributions and uncertainties from the gg channel, the overall improvements are small though. We furthermore study the effect of hybrid-cone photon isolation and hard-scale choice on our fully matched results to describe the ATLAS $$\text {8}$$ 8 TeV data and find that the hybrid-cone isolation worsens agreement at small $$q_T$$ q T compared to smooth-cone isolation.

Scale and isolation sensitivity of diphoton distributions at the LHC

Precision measurements of diphoton distributions at the LHC display some tension with theory predictions, obtained at next-to-next-to-leading order (NNLO) in QCD. We revisit the theoretical uncertainties arising from the approximation of the experimental photon isolation by smooth-cone isolation, and from the choice of functional form for the renormalisation and factorisation scales. We find that the resulting variations are substantial overall, and enhanced in certain regions. We discuss the infrared sensitivity at the cone boundaries in cone-based isolation in related distributions. Finally, we compare predictions made with alternative choices of dynamical scale and isolation prescriptions to experimental data from ATLAS at 8 TeV, observing improved agreement. This contrasts with previous results, highlighting that scale choice and isolation prescription are potential sources of theoretical uncertainty that were previously underestimated.