Many-Body Electronic Structure of Americium metal

We report computer based simulations of energetics, spectroscopy and electron-phonon interaction of americium using a novel spectral density functional method. This approach gives rise to a new concept of a many-body electronic structure and reveals the unexpected mixed valence regime of Am 5f6 electrons which under pressure acquire the 5f7 valence state. This explains unique properties of Am and addresses the fundamental issue of how the localization delocalization edge is approached from the localized side in a closed shell system.Comment: 4 pages, 3 figure

Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets

Another approach to track reconstruction: cluster analysis

A novel combination of data analysis techniques is proposed for the reconstruction of all tracks of primary charged particles, as well as of daughters of displaced vertices (decays, photon conversions, nuclear interactions), created in high energy collisions. Instead of performing a classical trajectory building or an image transformation, an efficient use of both local and global information is undertaken while keeping competing choices open. The measured hits of adjacent tracking layers are clustered first with the help of a mutual nearest neighbor search in the angular distance. The resulted chains of connected hits are used as initial clusters and as input for cluster analysis algorithms, such as the robust $k$-medians clustering. This latter proceeds by alternating between the hit-to-track assignment and the track-fit update steps, until convergence. The calculation of the hit-to-track distance and that of the track-fit $\chi^2$ is performed through the global covariance of the measured hits. The clustering is complemented with elements from a more sophisticated Metropolis-Hastings MCMC algorithm, with the possibility of adding new track hypotheses or removing unnecessary ones. Simplified but realistic models of today's silicon trackers, including the relevant physics processes, are employed to test and study the performance (efficiency, purity) of the proposed method as a function of the particle multiplicity in the collision event.Comment: Proceedings of "Connecting the Dots and Workshop on Intelligent Trackers (CTD/WIT 2019)"; 7 pages, 6 figure

A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models

Memory formation in matter

Memory formation in matter is a theme of broad intellectual relevance; it sits at the interdisciplinary crossroads of physics, biology, chemistry, and computer science. Memory connotes the ability to encode, access, and erase signatures of past history in the state of a system. Once the system has completely relaxed to thermal equilibrium, it is no longer able to recall aspects of its evolution. Memory of initial conditions or previous training protocols will be lost. Thus many forms of memory are intrinsically tied to far-from-equilibrium behavior and to transient response to a perturbation. This general behavior arises in diverse contexts in condensed matter physics and materials: phase change memory, shape memory, echoes, memory effects in glasses, return-point memory in disordered magnets, as well as related contexts in computer science. Yet, as opposed to the situation in biology, there is currently no common categorization and description of the memory behavior that appears to be prevalent throughout condensed-matter systems. Here we focus on material memories. We will describe the basic phenomenology of a few of the known behaviors that can be understood as constituting a memory. We hope that this will be a guide towards developing the unifying conceptual underpinnings for a broad understanding of memory effects that appear in materials

A Magnetorheological Damper with Embedded Piezoelectric Force Sensor: Experiment and Modeling

This chapter describes configuration, fabrication, calibration and performance tests of the devised self-sensing MR damper firstly. Then, a black-box identification approach for modeling the forward and inverse dynamics of the self-sensing MR damper is presented, which is developed with the synthesis of NARX model and neural network within a Bayesian inference framework to have the ability of enhancing generalization.Department of Civil and Environmental Engineerin

Enhancing touch interactions with passive finger acoustics

In this report, we introduce a unique method of interacting with mobile devices through passive finger acoustics. Phone users typically use one of two fingers when interacting with a touchscreen device, leaving the other fingers idle. The motivation is that users can make use of their idle fingers to enhance the experience of an application. By wearing minimally obtrusive rings on the thumb and index finger, users can make distinguishable clicking sounds to quickly perform actions, without having to interact directly with the screen. Our system leverages the microphone embedded in the mobile device to capture sound and recognize sound in real-time without requiring Internet connection. The different sounds introduce new ways for users to interact with their devices, without cluttering the screen. We evaluated our system on an Android drawing application, which allows the user to switch tools based on clicks. We optimized our system based on accuracy, classification speed, and ease of use, in order to create a comfortable user experienceInformatio