Search results for "Computer Science::Neural and Evolutionary Computation"
showing 10 items of 61 documents
"Table 5" of "Measurement of exclusive $\gamma\gamma\rightarrow \ell^+\ell^-$ production in proton-proton collisions at $\sqrt{s} = 7$ TeV with the A…
2015
Acoplanarity (ACO) distributions unfolded for detector resolution, and lepton pair trigger, reconstruction and identification efficiencies for e+ e- channel (empty bins are not reported).
"Table 36" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
p/pi ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
BoltzmaNN: Predicting effective pair potentials and equations of state using neural networks
2019
Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure. The NNs are trained with data from molecular dynamics simulations of monoatomic gases and liquids, sampled in the NVT ensemble at various densities. We find that the NNs provide much more accurate results compared to the analytic low-density limit estimate of the second virial coefficient and the Carnahan-Starling equation of state for hard sphere liquids. Furthermore, we design and train NNs for computing (effective) pair potentials from radial pair distribution functions, g(r), a task that is often performed for inverse design and coarse-graini…
Synchronised gravitational atoms from mergers of bosonic stars
2020
If ultralight bosonic fields exist in Nature as dark matter, superradiance spins down rotating black holes (BHs), dynamically endowing them with equilibrium bosonic clouds, here dubbed synchronised gravitational atoms (SGAs). The self-gravity of these same fields, on the other hand, can lump them into (scalar or vector) horizonless solitons known as bosonic stars (BSs). We show that the dynamics of BSs yields a new channel forming SGAs. We study BS binaries that merge to form spinning BHs. After horizon formation, the BH spins up by accreting the bosonic field, but a remnant lingers around the horizon. If just enough angular momentum is present, the BH spin up stalls precisely as the remnan…
"Table 6" of "Measurement of exclusive $\gamma\gamma\rightarrow \ell^+\ell^-$ production in proton-proton collisions at $\sqrt{s} = 7$ TeV with the A…
2015
Acoplanarity (ACO) distributions unfolded for detector resolution, and lepton pair trigger, reconstruction and identification efficiencies for mu+ mu- channel (empty bins are not reported).
Dynamic Economic Load Dispatch using Levenberg Marquardt Algorithm
2018
Abstract Economic Load Dispatch (ELD) is a very important feature of power system network. This work proposes the novel approach which considers the constraint of ramp rate limit (RRL) to solve the ELD problem. It build up the time varying dynamic economic load dispatch in which load dispatching is calculated for each specified time interval, first it is tested with conventional lambda iteration technique and then the outcomes are used to train artificial neural network (ANN) it is based on Levenberg Marquardt algorithm (LMA).As compared with any other ANN method, the Levenberg Marquardt algorithm based dynamic economic load dispatch is more swift and precise. The propose algorithm is teste…
"Table 37" of "Centrality dependence of Pi, K, p production in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV"
2018
K/pi ratio in Pb-Pb collisions at sqrt(sNN) = 2.76 TeV.
Unknown order process emulation
2002
Approaches the emulation problem using feedforward neural networks of single input single output (SISO) processes, applying a backpropagation method with a higher convergence rate. In this kind of application, difficult problems appear when the system's order is a priori unknown. A search through the SISO processes space is proposed, aiming to find a favorable neural emulator over the training examples set.
Deep Motion Model for Pedestrian Tracking in 360 Degrees Videos
2019
This paper proposes a deep convolutional neural network (CNN) for pedestrian tracking in 360◦ videos based on the target’s motion. The tracking algorithm takes advantage of a virtual Pan-Tilt-Zoom (vPTZ) camera simulated by means of the 360◦ video. The CNN takes in input a motion image, i.e. the difference of two images taken by using the vPTZ camera at different times by the same pan, tilt and zoom parameters. The CNN predicts the vPTZ camera parameter adjustments required to keep the target at the center of the vPTZ camera view. Experiments on a publicly available dataset performed in cross-validation demonstrate that the learned motion model generalizes, and that the proposed tracking algo…
FPGA implementation of Spiking Neural Networks
2012
Abstract Spiking Neural Networks (SNN) have optimal characteristics for hardware implementation. They can communicate among neurons using spikes, which in terms of logic resources, means a single bit, reducing the logic occupation in a device. Additionally, SNN are similar in performance compared to other neural Artificial Neural Network (ANN) architectures such as Multilayer Perceptron, and others. SNN are very similar to those found in the biological neural system, having weights and delays as adjustable parameters. This work describes the chosen models for the implemented SNN: Spike Response Model (SRM) and temporal coding is used. FPGA implementation using VHDL language is also describe…