Training neural networks with structured noise improves classification and generalization

The beneficial role of noise in learning is nowadays a consolidated concept in the field of artificial neural networks, suggesting that even biological systems might take advantage of similar mechanisms to maximize their performance. The training-with-noise algorithm proposed by Gardner and collaborators is an emblematic example of a noise injection procedure in recurrent networks, which are usually employed to model real neural systems. We show how adding structure into noisy training data can substantially improve the algorithm performance, allowing to approach perfect classification and maximal basins of attraction. We also prove that the so-called Hebbian unlearning rule coincides with the training-with-noise algorithm when noise is maximal and data are fixed points of the network dynamics. A sampling scheme for optimal noisy data is eventually proposed and implemented to outperform both the training-with-noise and the Hebbian unlearning procedures.Comment: 21 pages, 17 figures, main text and appendice

Noisy quantum walks of two indistinguishable interacting particles

We investigate the dynamics of continuous-time two-particle quantum walks on a one-dimensional noisy lattice. Depending on the initial condition, we show how the interplay between particle indistinguishability and interaction determines distinct propagation regimes. A realistic model for the environment is considered by introducing non-Gaussian noise as time-dependent fluctuations of the tunneling amplitudes between adjacent sites. We observe that the combined effect of particle interaction and fast noise (weak coupling with the environment) provides a faster propagation compared to the noiseless case. This effect can be understood in terms of the band structure of the Hubbard model, and a detailed analysis as a function of both noise and system parameters is presented.Comment: 9 pages, 8 figure

GPU-accelerated algorithms for many-particle continuous-time quantum walks

Many-particle continuous-time quantum walks (CTQWs) represent a resource for several tasks in quantum technology, including quantum search algorithms and universal quantum computation. In order to design and implement CTQWs in a realistic scenario, one needs effective simulation tools for Hamiltonians that take into account static noise and fluctuations in the lattice, i.e.\ua0Hamiltonians containing stochastic terms. To this aim, we suggest a parallel algorithm based on the Taylor series expansion of the evolution operator, and compare its performances with those of algorithms based on the exact diagonalization of the Hamiltonian or a 4th order Runge\u2013Kutta integration. We prove that both Taylor-series expansion and Runge\u2013Kutta algorithms are reliable and have a low computational cost, the Taylor-series expansion showing the additional advantage of a memory allocation not depending on the precision of calculation. Both algorithms are also highly parallelizable within the SIMT paradigm, and are thus suitable for GPGPU computing. In turn, we have benchmarked 4 NVIDIA GPUs and 3 quad-core Intel CPUs for a 2-particle system over lattices of increasing dimension, showing that the speedup provided by GPU computing, with respect to the OPENMP parallelization, lies in the range between 8x and (more than) 20x, depending on the frequency of post-processing. GPU-accelerated codes thus allow one to overcome concerns about the execution time, and make it possible simulations with many interacting particles on large lattices, with the only limit of the memory available on the device. Program summary Program Title: cuQuWa Licensing provisions: GNU General Public License, version 3 Program Files doi: http://dx.doi.org/10.17632/vjpnjgycdj.1 Programming language: CUDA C Nature of problem: Evolution of many-particle continuous-time quantum-walks on a multidimensional grid in a noisy environment. The submitted code is specialized for the simulation of 2-particle quantum-walks with periodic boundary conditions. Solution method: Taylor-series expansion of the evolution operator. The density-matrix is calculated by averaging multiple independent realizations of the system. External routines: cuBLAS, cuRAND Unusual features: Simulations are run exclusively on the graphic processing unit within the CUDA environment. An undocumented misbehavior in the random-number generation routine (cuRAND package) can corrupt the simulation of large systems, though no problems are reported for small and medium-size systems. Compiling the code with the -arch=sm_30 flag for compute capability 3.5 and above fixes this issue

GPU-accelerated algorithms for many-particle continuous-time quantum walks

Many-particle continuous-time quantum walks (CTQWs) represent a resource for several tasks in quantum technology, including quantum search algorithms and universal quantum computation. In order to design and implement CTQWs in a realistic scenario, one needs effective simulation tools for Hamiltonians that take into account static noise and fluctuations in the lattice, i.e. Hamiltonians containing stochastic terms. To this aim, we suggest a parallel algorithm based on the Taylor series expansion of the evolution operator, and compare its performances with those of algorithms based on the exact diagonalization of the Hamiltonian or a 4th order Runge–Kutta integration. We prove that both Taylor-series expansion and Runge–Kutta algorithms are reliable and have a low computational cost, the Taylor-series expansion showing the additional advantage of a memory allocation not depending on the precision of calculation. Both algorithms are also highly parallelizable within the SIMT paradigm, and are thus suitable for GPGPU computing. In turn, we have benchmarked 4 NVIDIA GPUs and 3 quad-core Intel CPUs for a 2-particle system over lattices of increasing dimension, showing that the speedup provided by GPU computing, with respect to the OPENMP parallelization, lies in the range between 8x and (more than) 20x, depending on the frequency of post-processing. GPU-accelerated codes thus allow one to overcome concerns about the execution time, and make it possible simulations with many interacting particles on large lattices, with the only limit of the memory available on the device. Program summary Program Title: cuQuWa Licensing provisions: GNU General Public License, version 3 Program Files doi: http://dx.doi.org/10.17632/vjpnjgycdj.1 Programming language: CUDA C Nature of problem: Evolution of many-particle continuous-time quantum-walks on a multidimensional grid in a noisy environment. The submitted code is specialized for the simulation of 2-particle quantum-walks with periodic boundary conditions. Solution method: Taylor-series expansion of the evolution operator. The density-matrix is calculated by averaging multiple independent realizations of the system. External routines: cuBLAS, cuRAND Unusual features: Simulations are run exclusively on the graphic processing unit within the CUDA environment. An undocumented misbehavior in the random-number generation routine (cuRAND package) can corrupt the simulation of large systems, though no problems are reported for small and medium-size systems. Compiling the code with the -arch=sm_30 flag for compute capability 3.5 and above fixes this issue

Quantum walks of two interacting particles on percolation graphs

We address the dynamics of two indistinguishable interacting particles moving on a dynamical percolation graph, i.e., a graph where the edges are independent random telegraph processes whose values jump between 0 and 1, thus mimicking percolation. The interplay between the particle interaction strength, initial state and the percolation rate determine different dynamical regimes for the walkers. We show that, whenever the walkers are initially localised within the interaction range, fast noise enhances the particle spread compared to the noiseless case

Finite Element Thermal Analysis of Metal Parts Additively Manufactured via Selective Laser Melting

In this chapter, a three-dimensional finite element model is developed to simulate the thermal behavior of the molten pool in selective laser melting (SLM) process. Laser-based additive manufacturing (AM) is a near net shape manufacturing process able to produce 3D objects. They are layer-wise built through selective melting of a metal powder bed. The necessary energy is provided by a laser source. The interaction between laser and material occurs within a few microseconds, hence the transient thermal behavior must be taken into account. A calibration procedure is carried out to fit the numerical solution with the experimental data. Once the calibration has corrected the thermal parameters, a dynamic mesh refinement is applied to reduce the computational cost. The scanning strategy adopted by the laser is simulated by a path simulator built using MatLab®, while numerical analysis is carried out using ANSYS®, a commercial finite element software. To improve the performance of the simulation, the two codes interact each other to solve the analysis. Temperature distribution and geometrical feature of the molten pool under different process conditions are investigated. Results from the FE analysis provide guidance for setting up the optimization of process parameters and develop a base for further residual stress analysis

Management of the Kidney Transplant Patient with Chronic Hepatitis C Infection

Chronic Hepatitis C (HCV) infection is an important cause of morbidity and mortality in patients with end-stage renal disease. Renal transplantation confers a survival advantage in HCV-infected patients. Renal transplant candidates with serologic evidence of HCV infection should undergo a liver biopsy to assess for fibrosis and cirrhosis. Patients with Metavir fibrosis score ≤3 and compensated cirrhosis should be evaluated for interferon-based therapy. Achievement of sustained virological response (SVR) may reduce the risks for both posttransplantation hepatic and extrahepatic complications such as de novo or recurrent glomerulonephritis associated with HCV. Patients who cannot achieve SVR and have no live kidney donor may be considered for HCV-positive kidneys. Interferon should be avoided after kidney transplant except for treatment of life-threatening liver injury, such as fibrosing cholestatic hepatitis. Early detection, prevention, and treatment of complications due to chronic HCV infection may improve the outcomes of kidney transplant recipients with chronic HCV infection

Opposite effects of suicidality and lithium on gray matter volumes in bipolar depression

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Management of the Kidney Transplant Patient with Chronic Hepatitis C Infection

Chronic Hepatitis C (HCV) infection is an important cause of morbidity and mortality in patients with end-stage renal disease. Renal transplantation confers a survival advantage in HCV-infected patients. Renal transplant candidates with serologic evidence of HCV infection should undergo a liver biopsy to assess for fibrosis and cirrhosis. Patients with Metavir fibrosis score ≤3 and compensated cirrhosis should be evaluated for interferon-based therapy. Achievement of sustained virological response (SVR) may reduce the risks for both posttransplantation hepatic and extrahepatic complications such as de novo or recurrent glomerulonephritis associated with HCV. Patients who cannot achieve SVR and have no live kidney donor may be considered for HCV-positive kidneys. Interferon should be avoided after kidney transplant except for treatment of life-threatening liver injury, such as fibrosing cholestatic hepatitis. Early detection, prevention, and treatment of complications due to chronic HCV infection may improve the outcomes of kidney transplant recipients with chronic HCV infection