SLOCC determinant invariants of order 2^{n/2} for even n qubits

In this paper, we study SLOCC determinant invariants of order 2^{n/2} for any even n qubits which satisfy the SLOCC determinant equations. The determinant invariants can be constructed by a simple method and the set of all these determinant invariants is complete with respect to permutations of qubits. SLOCC entanglement classification can be achieved via the vanishing or not of the determinant invariants. We exemplify the method for several even number of qubits, with an emphasis on six qubits.Comment: J. Phys. A: Math. Theor. 45 (2012) 07530

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach accuracies on par with density functional theory reference. Locality is exploited within non-linear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to sub-mesoscale lengths

Random on-board pixel sampling (ROPS) X-ray Camera

Recent advances in compressed sensing theory and algorithms offer new possibilities for high-speed X-ray camera design. In many CMOS cameras, each pixel has an independent on-board circuit that includes an amplifier, noise rejection, signal shaper, an analog-to-digital converter (ADC), and optional in-pixel storage. When X-ray images are sparse, i.e., when one of the following cases is true: (a.) The number of pixels with true X-ray hits is much smaller than the total number of pixels; (b.) The X-ray information is redundant; or (c.) Some prior knowledge about the X-ray images exists, sparse sampling may be allowed. Here we first illustrate the feasibility of random on-board pixel sampling (ROPS) using an existing set of X-ray images, followed by a discussion about signal to noise as a function of pixel size. Next, we describe a possible circuit architecture to achieve random pixel access and in-pixel storage. The combination of a multilayer architecture, sparse on-chip sampling, and computational image techniques, is expected to facilitate the development and applications of high-speed X-ray camera technology.Comment: 9 pages, 6 figures, Presented in 19th iWoRI

Have you forgotten? A method to assess if machine learning models have forgotten data

In the era of deep learning, aggregation of data from several sources is a common approach to ensuring data diversity. Let us consider a scenario where several providers contribute data to a consortium for the joint development of a classification model (hereafter the target model), but, now one of the providers decides to leave. This provider requests that their data (hereafter the query dataset) be removed from the databases but also that the model `forgets' their data. In this paper, for the first time, we want to address the challenging question of whether data have been forgotten by a model. We assume knowledge of the query dataset and the distribution of a model's output. We establish statistical methods that compare the target's outputs with outputs of models trained with different datasets. We evaluate our approach on several benchmark datasets (MNIST, CIFAR-10 and SVHN) and on a cardiac pathology diagnosis task using data from the Automated Cardiac Diagnosis Challenge (ACDC). We hope to encourage studies on what information a model retains and inspire extensions in more complex settings.Comment: Accepted by MICCAI 202

Free field realization of the exceptional current superalgebra \hat{D(2,1;\a)}_k

The free-field representations of the D(2,1;\a) current superalgebra and the corresponding energy-momentum tensor are constructed. The related screening currents of the first kind are also presented.Comment: Latex file, 10 page

Magnetic field symmetry of pump currents of adiabatically driven mesoscopic structures

We examine the scattering properties of a slowly and periodically driven mesoscopic sample using the Floquet function approach. One might expect that at sufficiently low driving frequencies it is only the frozen scattering matrix which is important. The frozen scattering matrix reflects the properties of the sample at a given instant of time. Indeed many aspects of adiabatic scattering can be described in terms of the frozen scattering matrix. However, we demonstrate that the Floquet scattering matrix, to first order in the driving frequency, is determined by an additional matrix which reflects the fact that the scatterer is time-dependent. This low frequency irreducible part of the Floquet matrix has symmetry properties with respect to time and/or a magnetic field direction reversal opposite to that of the frozen scattering matrix. We investigate the quantum rectification properties of a pump which additionally is subject to an external dc voltage. We split the dc current flowing through the pump into several parts with well defined properties with respect to a magnetic field and/or an applied voltage inversion.Comment: 13 pages, 4 figure

Incommensurate spin correlations in highly oxidized cobaltates La2−x_{2-x}Srx_{x}CoO4_{4}

We observe quasi-static incommensurate magnetic peaks in neutron scattering experiments on layered cobalt oxides La2-xSrxCoO4 with high Co oxidation states that have been reported to be paramagnetic. This enables us to measure the magnetic excitations in this highly hole-doped incommensurate regime and compare our results with those found in the low-doped incommensurate regime that exhibit hourglass magnetic spectra. The hourglass shape of magnetic excitations completely disappears given a high Sr doping. Moreover, broad low-energy excitations are found, which are not centered at the incommensurate magnetic peak positions but around the quarter-integer values that are typically exhibited by excitations in the checkerboard charge ordered phase. Our findings suggest that the strong inter-site exchange interactions in the undoped islands are critical for the emergence of hourglass spectra in the incommensurate magnetic phases of La2-xSrxCoO4.Comment: http://www.nature.com/articles/srep25117

Information entropy in fragmenting systems

The possibility of facing critical phenomena in nuclear fragmentation is a topic of great interest. Different observables have been proposed to identify such a behavior, in particular, some related to the use of information entropy as a possible signal of critical behavior. In this work we critically examine some of the most widespread used ones comparing its performance in bond percolation and in the analysis of fragmenting Lennard Jones Drops.Comment: 3 pages, 3 figure

A Two-Tiered Correlation of Dark Matter with Missing Transverse Energy: Reconstructing the Lightest Supersymmetric Particle Mass at the LHC

We suggest that non-trivial correlations between the dark matter particle mass and collider based probes of missing transverse energy H_T^miss may facilitate a two tiered approach to the initial discovery of supersymmetry and the subsequent reconstruction of the LSP mass at the LHC. These correlations are demonstrated via extensive Monte Carlo simulation of seventeen benchmark models, each sampled at five distinct LHC center-of-mass beam energies, spanning the parameter space of No-Scale F-SU(5).This construction is defined in turn by the union of the Flipped SU(5) Grand Unified Theory, two pairs of hypothetical TeV scale vector-like supersymmetric multiplets with origins in F-theory, and the dynamically established boundary conditions of No-Scale Supergravity. In addition, we consider a control sample comprised of a standard minimal Supergravity benchmark point. Led by a striking similarity between the H_T^miss distribution and the familiar power spectrum of a black body radiator at various temperatures, we implement a broad empirical fit of our simulation against a Poisson distribution ansatz. We advance the resulting fit as a theoretical blueprint for deducing the mass of the LSP, utilizing only the missing transverse energy in a statistical sampling of >= 9 jet events. Cumulative uncertainties central to the method subsist at a satisfactory 12-15% level. The fact that supersymmetric particle spectrum of No-Scale F-SU(5) has thrived the withering onslaught of early LHC data that is steadily decimating the Constrained Minimal Supersymmetric Standard Model and minimal Supergravity parameter spaces is a prime motivation for augmenting more conventional LSP search methodologies with the presently proposed alternative.Comment: JHEP version, 17 pages, 9 Figures, 2 Table

A Review of Object Detection Models based on Convolutional Neural Network

Convolutional Neural Network (CNN) has become the state-of-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.Comment: 17 pages, 11 figures, 1 tabl