Topological non-Hermitian origin of surface Maxwell waves

Maxwell electromagnetism, describing the wave properties of light, was formulated 150 years ago. More than 60 years ago it was shown that interfaces between optical media (including dielectrics, metals, negative-index materials) can support surface electromagnetic waves, which now play crucial roles in plasmonics, metamaterials, and nano-photonics. Here we show that surface Maxwell waves at interfaces between homogeneous, isotropic media described by real permittivities and permeabilities have a purely topological origin explained by the bulk-boundary correspondence. Importantly, the topological classification is determined by the helicity operator, which is generically non-Hermitian even in lossless optical media. The corresponding topological invariant, which determines the number of surface modes, is a Z4 number (or a pair of Z2 numbers) describing the winding of the complex helicity spectrum across the interface. Moreover, there is an additional pair of non-topological Z2 indices, which describe zones of the TE and TM polarizations at the phase diagram of surface modes. Our theory provides a new twist and insights for several areas of wave physics: Maxwell electromagnetism, topological quantum states, non-Hermitian wave physics, and metamaterials.Comment: 12 pages, 3 figure

Computational Controversy

Climate change, vaccination, abortion, Trump: Many topics are surrounded by fierce controversies. The nature of such heated debates and their elements have been studied extensively in the social science literature. More recently, various computational approaches to controversy analysis have appeared, using new data sources such as Wikipedia, which help us now better understand these phenomena. However, compared to what social sciences have discovered about such debates, the existing computational approaches mostly focus on just a few of the many important aspects around the concept of controversies. In order to link the two strands, we provide and evaluate here a controversy model that is both, rooted in the findings of the social science literature and at the same time strongly linked to computational methods. We show how this model can lead to computational controversy analytics that have full coverage over all the crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social Informatics (SocInfo) 201

Initial-state dependence in time-dependent density functional theory

Time-dependent density functionals in principle depend on the initial state of the system, but this is ignored in functional approximations presently in use. For one electron it is shown there is no initial-state dependence: for any density, only one initial state produces a well-behaved potential. For two non-interacting electrons with the same spin in one-dimension, an initial potential that makes an alternative initial wavefunction evolve with the same density and current as a ground state is calculated. This potential is well-behaved and can be made arbitrarily different from the original potential

Metal Surface Energy: Persistent Cancellation of Short-Range Correlation Effects beyond the Random-Phase Approximation

The role that non-local short-range correlation plays at metal surfaces is investigated by analyzing the correlation surface energy into contributions from dynamical density fluctuations of various two-dimensional wave vectors. Although short-range correlation is known to yield considerable correction to the ground-state energy of both uniform and non-uniform systems, short-range correlation effects on intermediate and short-wavelength contributions to the surface formation energy are found to compensate one another. As a result, our calculated surface energies, which are based on a non-local exchange-correlation kernel that provides accurate total energies of a uniform electron gas, are found to be very close to those obtained in the random-phase approximation and support the conclusion that the error introduced by the local-density approximation is small.Comment: 5 pages, 1 figure, to appear in Phys. Rev.

Correlation energy of a two-dimensional electron gas from static and dynamic exchange-correlation kernels

We calculate the correlation energy of a two-dimensional homogeneous electron gas using several available approximations for the exchange-correlation kernel $f_{\rm xc}(q,\omega)$ entering the linear dielectric response of the system. As in the previous work of Lein {\it et al.} [Phys. Rev. B {\bf 67}, 13431 (2000)] on the three-dimensional electron gas, we give attention to the relative roles of the wave number and frequency dependence of the kernel and analyze the correlation energy in terms of contributions from the $(q, i\omega)$ plane. We find that consistency of the kernel with the electron-pair distribution function is important and in this case the nonlocality of the kernel in time is of minor importance, as far as the correlation energy is concerned. We also show that, and explain why, the popular Adiabatic Local Density Approximation performs much better in the two-dimensional case than in the three-dimensional one.Comment: 9 Pages, 4 Figure

Change in Markers of Bone Metabolism with Chemotherapy for Advanced Prostate Cancer: Interleukin-6 Response Is a Potential Early Indicator of Response to Therapy

Men with androgen-independent prostate cancer (AIPC) frequently have bone metastasis. The effects of chemotherapy on markers of bone metabolism have not been well characterized. We conducted a prospective study of patients with AIPC randomized in the first cycle to receive either docetaxel/estramustine or zoledronic acid, a bisphosphonate, to inhibit osteoclastic activity. Here we report the effects of therapy on markers of bone metabolism in these patients following the first cycle of therapy. Serum levels of several indices of bone remodeling were evaluated using commercial enzyme-linked immunosorbent assays. Changes in markers of bone metabolism were compared in patients receiving initial chemotherapy versus bisphosphonate. There was no significant difference in median change in any of the measured bone markers in patients given zoledronic acid when compared to chemotherapy. When comparing responders to nonresponders, overall interleukin-6 (IL-6) decreased by 35% in prostate-specific antigen responders; whereas, IL-6 levels increased by 76% in nonresponders (p = 0.03). Elevated IL-6 levels and reductions in IL-6 levels early in treatment may reflect ultimate clinical response to docetaxel-based regimens.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78145/1/jir.2008.0024.pd

Why are Prices Sticky? Evidence from Business Survey Data

This paper offers new insights on the price setting behaviour of German retail firms using a novel dataset that consists of a large panel of monthly business surveys from 1991-2006. The firm-level data allows matching changes in firms' prices to several other firm-characteristics. Moreover, information on price expectations allow analyzing the determinants of price updating. Using univariate and bivariate ordered probit specifications, empirical menu cost models are estimated relating the probability of price adjustment and price updating, respectively, to both time- and state- dependent variables. First, results suggest an important role for state-dependence; changes in the macroeconomic and institutional environment as well as firm-specific factors are significantly related to the timing of price adjustment. These findings imply that price setting models should endogenize the timing of price adjustment in order to generate realistic predictions concerning the transmission of monetary policy. Second, an analysis of price expectations yields similar results providing evidence in favour of state-dependent sticky plan models. Third, intermediate input cost changes are among the most important determinants of price adjustment suggesting that pricing models should explicitly incorporate price setting at different production stages. However, the results show that adjustment to input cost changes takes time indicating "additional stickiness" at the last stage of processing

Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice.

To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntington's disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo

Unique reporter-based sensor platforms to monitor signalling in cells

Introduction: In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level. <p/>Methods: Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis. <p/>Findings: We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation. <p/>Conclusions: We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters

A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing

Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.Neuro Imaging Researc