A Hierarchy of Transport Approximations for High Energy Heavy (HZE) Ions

The transport of high energy heavy (HZE) ions through bulk materials is studied neglecting energy dependence of the nuclear cross sections. A three term perturbation expansion appears to be adequate for most practical applications for which penetration depths are less than 30 g per sq cm of material. The differential energy flux is found for monoenergetic beams and for realistic ion beam spectral distributions. An approximate formalism is given to estimate higher-order terms

A Generative deep learning approach for shape recognition of arbitrary objects from phaseless acoustic scattering data

We propose and demonstrate a generative deep learning approach for the shape recognition of an arbitrary object from its acoustic scattering properties. The strategy exploits deep neural networks to learn the mapping between the latent space of a two-dimensional acoustic object and the far-field scattering amplitudes. A neural network is designed as an Adversarial autoencoder and trained via unsupervised learning to determine the latent space of the acoustic object. Important structural features of the object are embedded in lower-dimensional latent space which supports the modeling of a shape generator and accelerates the learning in the inverse design process.The proposed inverse design uses the variational inference approach with encoder and decoder-like architecture where the decoder is composed of two pretrained neural networks, the generator and the forward model. The data-driven framework finds an accurate solution to the ill-posed inverse scattering problem, where non-unique solution space is overcome by the multifrequency phaseless far-field patterns. This inverse method is a powerful design tool that does not require complex analytical calculation and opens up new avenues for practical realization, automatic recognition of arbitrary shaped submarines or large fish, and other underwater applications.Comment: 43 pages, 19 figure

Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems

Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmission with respect to the wave propagation direction. Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process. In particular, we construct a deep learning model that relates the transmission and asymmetric reflection in non-conservative settings and propose sub-manifold learning to recognize non-Hermitian features from transmission spectra. The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response. These findings offer a route for intelligent inverse design and contribute to the understanding of physical mechanism in general non-Hermitian systems.Postprint (published version

Improved convergence of scattering calculations in the oscillator representation

The Schr\"odinger equation for two and tree-body problems is solved for scattering states in a hybrid representation where solutions are expanded in the eigenstates of the harmonic oscillator in the interaction region and on a finite difference grid in the near-- and far--field. The two representations are coupled through a high--order asymptotic formula that takes into account the function values and the third derivative in the classical turning points. For various examples the convergence is analyzed for various physics problems that use an expansion in a large number of oscillator states. The results show significant improvement over the JM-ECS method [Bidasyuk et al, Phys. Rev. C 82, 064603 (2010)]

Imaging Biomarkers for Precision Medicine in Locally Advanced Breast Cancer

Guidelines from the American National Comprehensive Cancer Network (NCCN)recommend neoadjuvant chemotherapy (NAC) to patients with locally advanced breast cancer (LABC) to downstage tumors before surgery. However, only a small fraction (15-17%) of LABC patients achieve complete pathologic response (pCR), i.e. no residual tumor in the breast, after treatment. Measuring tumor response during 53 neoadjuvant chemotherapy can potentially help physicians adapt treatment thus, potentially improving the pCR rate. Recently, imaging biomarkers that are used to measure the tumor’s functional and biological features have been studied as pre-treatment markers for pCR or as an indicator for intra-treatment tumor response. Also, imaging biomarkers have been the focus of intense research to characterize tumor heterogeneity as well as to advance our understanding of the principle mechanisms behind chemoresistance. Advances in investigational radiology are moving rapidly to high-resolution imaging, capturing metabolic data, performing tissue characterization and statistical modelling of imaging biomarkers, with an endpoint of personalized medicine in breast cancer treatment. In this commentary, we present studies within the framework of imaging biomarkers used to measure breast tumor response to chemotherapy. Current studies are showing that significant progress has been made in the accuracy of measuring tumor response either before or during chemotherapy, yet the challenges at the forefront of these works include translational gaps such as needing large-scale clinical trials for validation, and standardization of imaging methods. However, the ongoing research is showing that imaging biomarkers may play an important role in personalized treatments for LABC

A Multi-Area Architecture for Real-Time Feedback-Based Optimization of Distribution Grids

A challenge in transmission-distribution coordination is how to quickly and reliably coordinate Distributed Energy Resources (DERs) across large multi-stakeholder Distribution Networks (DNs) to support the Transmission Network (TN), while ensuring operational constraints continue to be met within the DN. Here we propose a hierarchical feedback-based control architecture for coordination of DERs in DNs, enabling the DN to quickly respond to power set-point requests from the Transmission System Operator (TSO) while maintaining local DN constraints. Our scheme allows for multiple independently-managed areas within the DN to optimize their local resources while coordinating to support the TN, and while maintaining data privacy; the only required inter-area communication is between physically adjacent areas within the DN control hierarchy. We conduct a rigorous stability analysis, establishing intuitive conditions for closed-loop stability, and provide detailed tuning recommendations. The proposal is validated via case studies on multiple feeders, including IEEE-123 and IEEE-8500, using a custom MATLAB-based application which integrates with OpenDSS. The simulation results show that the proposed structure is highly scalable and can quickly coordinate DERs in response to TSO commands, while responding to local disturbances within the DN and maintaining DN operational limits.Comment: 13 pages, 11 figures, for the supplement document (pdf), see https://1drv.ms/b/s!AmH_VfOdVVKazYUpDEVplHjtvj-7ZA?e=oyfXD

GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers

Inverse scattering problems are inherently challenging, given the fact they are ill-posed and nonlinear. This paper presents a powerful deep learning-based approach that relies on generative adversarial networks to accurately and efficiently reconstruct randomly-shaped two-dimensional dielectric objects from amplitudes of multi-frequency scattered electric fields. An adversarial autoencoder (AAE) is trained to learn to generate the scatterer's geometry from a lower-dimensional latent representation constrained to adhere to the Gaussian distribution. A cohesive inverse neural network (INN) framework is set up comprising a sequence of appropriately designed dense layers, the already-trained generator as well as a separately trained forward neural network. The images reconstructed at the output of the inverse network are validated through comparison with outputs from the forward neural network, addressing the non-uniqueness challenge inherent to electromagnetic (EM) imaging problems. The trained INN demonstrates an enhanced robustness, evidenced by a mean binary cross-entropy (BCE) loss of $0.13$ and a structure similarity index (SSI) of $0.90$. The study not only demonstrates a significant reduction in computational load, but also marks a substantial improvement over traditional objective-function-based methods. It contributes both to the fields of machine learning and EM imaging by offering a real-time quantitative imaging approach. The results obtained with the simulated data, for both training and testing, yield promising results and may open new avenues for radio-frequency inverse imaging

A ferrofluid based neural network: design of an analogue associative memory

We analyse an associative memory based on a ferrofluid, consisting of a system of magnetic nano-particles suspended in a carrier fluid of variable viscosity subject to patterns of magnetic fields from an array of input and output magnetic pads. The association relies on forming patterns in the ferrofluid during a trainingdphase, in which the magnetic dipoles are free to move and rotate to minimize the total energy of the system. Once equilibrated in energy for a given input-output magnetic field pattern-pair the particles are fully or partially immobilized by cooling the carrier liquid. Thus produced particle distributions control the memory states, which are read out magnetically using spin-valve sensors incorporated in the output pads. The actual memory consists of spin distributions that is dynamic in nature, realized only in response to the input patterns that the system has been trained for. Two training algorithms for storing multiple patterns are investigated. Using Monte Carlo simulations of the physical system we demonstrate that the device is capable of storing and recalling two sets of images, each with an accuracy approaching 100%.Comment: submitted to Neural Network