Definitive Evidence for Order-by-Quantum-Disorder in Er2Ti2O7

Here we establish the systematic existence of a U(1) degeneracy of all symmetry-allowed Hamiltonians quadratic in the spins on the pyrochlore lattice, at the mean-field level. By extracting the Hamiltonian of Er2Ti2O7 from inelastic neutron scattering measurements, we then show that the U(1)-degenerate states of Er2Ti2O7 are its classical ground states, and unambiguously show that quantum fluctuations break the degeneracy in a way which is confirmed by experiment. This is the first definitive observation of order-by-disorder in any material. We provide further verifiable consequences of this phenomenon, and several additional comparisons between theory and experiment.Comment: 4.5 pages, 3 figures, 7.5 pages of Supplemental Material, 8 supplemental figure

Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques

The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this paper, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. These techniques require a small number of experimental data points and does not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network-based prediction task for the Ohmic resistance value in Gallium Nitride devices. A 70% reduction in mean absolute error when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.Comment: Entirely rewrote and reorganized. Updated model

On the determination of redundancies in sociometric chains

The use of a matrix to represent a relationship between the members of a group is well known in sociometry. If this matrix is raised to a certain power, the elements appearing give the total number of connecting paths between each pair of members. In general, some of these paths will be redundant. Methods of finding the number of such redundant paths have been developed for three- and four-step chains by Luce and Perry (3) and Katz (2), respectively. We have derived formulas for the number of redundant paths of five and six steps; and in addition, an algorithm for determining the number of redundant paths of any given length.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45690/1/11336_2005_Article_BF02288782.pd

PACAP neurons in the ventral premammillary nucleus regulate reproductive function in the female mouse.

Pituitary adenylate cyclase activating polypeptide (PACAP, Adcyap1) is a neuromodulator implicated in anxiety, metabolism and reproductive behavior. PACAP global knockout mice have decreased fertility and PACAP modulates LH release. However, its source and role at the hypothalamic level remain unknown. We demonstrate that PACAP-expressing neurons of the ventral premamillary nucleus of the hypothalamus (PMVPACAP) project to, and make direct contact with, kisspeptin neurons in the arcuate and AVPV/PeN nuclei and a subset of these neurons respond to PACAP exposure. Targeted deletion of PACAP from the PMV through stereotaxic virally mediated cre- injection or genetic cross to LepR-i-cre mice with Adcyap1fl/fl mice led to delayed puberty onset and impaired reproductive function in female, but not male, mice. We propose a new role for PACAP-expressing neurons in the PMV in the relay of nutritional state information to regulate GnRH release by modulating the activity of kisspeptin neurons, thereby regulating reproduction in female mice

Ensemble interactions in strained semiconductor quantum dots

Large variations in InxGa1-xAs quantum dot concentrations were obtained with simultaneous growths on vicinal GaAs [001] substrates with different surface step densities. It was found that decreasing dot-dot separation blueshifts all levels, narrows intersublevel transition energies, shortens luminescence decay times for excited states, and increases inhomogeneous photoluminescence broadening. These changes in optical properties are attributed to a progressive strain deformation of the confining potentials and to the increasing effects of positional disorder in denser dot ensembles

Impact of electrostatic crosstalk on spin qubits in dense CMOS quantum dot arrays

Quantum processors based on integrated nanoscale silicon spin qubits are a promising platform for highly scalable quantum computation. Current CMOS spin qubit processors consist of dense gate arrays to define the quantum dots, making them susceptible to crosstalk from capacitive coupling between a dot and its neighbouring gates. Small but sizeable spin-orbit interactions can transfer this electrostatic crosstalk to the spin g-factors, creating a dependence of the Larmor frequency on the electric field created by gate electrodes positioned even tens of nanometers apart. By studying the Stark shift from tens of spin qubits measured in nine different CMOS devices, we developed a theoretical frawework that explains how electric fields couple to the spin of the electrons in increasingly complex arrays, including those electric fluctuations that limit qubit dephasing times $T_2^*$. The results will aid in the design of robust strategies to scale CMOS quantum technology.Comment: 9 pages, 4 figure