Polariton Pattern Formation and Photon Statistics of the Associated Emission

We report on the formation of a diverse family of transverse spatial polygon patterns in a microcavity polariton fluid under coherent driving by a blue-detuned pump. Patterns emerge spontaneously as a result of energy-degenerate polariton-polariton scattering from the pump state to interfering high order vortex and antivortex modes, breaking azimuthal symmetry. The interplay between a multimode parametric instability and intrinsic optical bistability leads to a sharp spike in the value of second order coherence g (2)(0) of the emitted light, which we attribute to the strongly superlinear kinetics of the underlying scattering processes driving the formation of patterns. We show numerically by means of a linear stability analysis how the growth of parametric instabilities in our system can lead to spontaneous symmetry breaking, predicting the formation and competition of different pattern states in good agreement with experimental observations

Latent class cluster analysis of symptom ratings identifies distinct subgroups within the clinical high risk for psychosis syndrome

© 2017 The clinical-high-risk for psychosis (CHR-P) syndrome is heterogeneous in terms of clinical presentation and outcomes. Identifying more homogenous subtypes of the syndrome may help clarify its etiology and improve the prediction of psychotic illness. This study applied latent class cluster analysis (LCCA) to symptom ratings from the North American Prodrome Longitudinal Studies 1 and 2 (NAPLS 1 and 2). These analyses produced evidence for three to five subgroups within the CHR-P syndrome. Differences in negative and disorganized symptoms distinguished among the subgroups. Subgroup membership was found to predict conversion to psychosis. The authors contrast the methods employed within this study with previous attempts to identify more homogenous subgroups of CHR-P individuals and discuss how these results could be tested in future samples of CHR-P individuals

The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning

Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EPS) against the Structured Interview for Psychosis-risk Syndromes (SIPS). The Prodromal Questionnaire–Brief Version (PQ-B) and 148 additional items were administered to 229 individuals being screened with the SIPS at 7 North American Prodrome Longitudinal Study sites and at Columbia University. Fifty individuals were found to have SIPS scores of 0, 1, or 2, making them clinically low risk (CLR) controls; 144 were classified as clinically high risk (CHR) (SIPS 3–5) and 35 were found to have first episode psychosis (FEP) (SIPS 6). Spectral clustering analysis, performed on 124 of the items, yielded two cohesive item groups, the first mostly related to psychosis and mania, the second mostly related to depression, anxiety, and social and general work/school functioning. Items within each group were sorted according to their usefulness in distinguishing between CLR and CHR individuals using the Minimum Redundancy Maximum Relevance procedure. A receiver operating characteristic area under the curve (AUC) analysis indicated that maximal differentiation of CLR and CHR participants was achieved with a 26-item solution (AUC = 0.899 ± 0.001). The EPS-26 outperformed the PQ-B (AUC = 0.834 ± 0.001). For screening purposes, the self-report EPS-26 appeared to differentiate individuals who are either CLR or CHR approximately as well as the clinician-administered SIPS. The EPS-26 may prove useful as a self-report screener and may lead to a decrease in the duration of untreated psychosis. A validation of the EPS-26 against actual conversion is underway

Extended Gari-Krumpelmann model fits to nucleon electromagnetic form factors

Nucleon electromagnetic form factor data (including recent data) is fitted with models that respect the confinement and asymptotic freedom properties of QCD. Gari-Krumpelmann (GK) type models, which include the major vector meson pole contributions and at high momentum transfer conform to the predictions of perturbative QCD, are combined with Hohler-Pietarinen (HP) models, which also include the width of the rho meson and the addition of higher mass vector meson exchanges, but do not evolve into the explicit form of PQCD at high momentum transfer. Different parameterizations of the GK model's hadronic form factors, the effect of including the width of the rho meson and the addition of the next (in mass) isospin 1 vector meson are considered. The quality of fit and the consistency of the parameters select three of the combined HP/GK type models. Projections are made to the higher momentum transfers which are relevant to electron-deuteron experiments. The projections vary little for the preferred models, removing much of the ambiguity in electron-nucleus scattering predictions.Comment: 18pp, 7 figures, using RevTeX with BoxedEPS macros; 1 new figure, minor textual changes; email correspondence to [email protected]

Octet-Baryon Form Factors in the Diquark Model

We present an alternative parameterization of the quark-diquark model of baryons which particularly takes care of the most recent proton electric form-factor data from the E136 experiment at SLAC. In addition to electromagnetic form factors of the nucleon, for which good agreement with data is achieved, we discuss the weak axial vector form factor of the nucleon as well as electromagnetic form factors of $\Lambda$ and $\Sigma$ hyperons. Technical advance in calculating the pertinent analytic expressions within perturbative quantum chromodynamics is gained by formulating the wave function of the quark-diquark system in a covariant way. Finally, we also comment on the influence of Sudakov corrections within the scope of the diquark model.Comment: 16 pages, WU-B 93-07, latex, uuencoded postscript files of 7 figures appended at the end of the latex fil

Effect of recent R_p and R_n measurements on extended Gari-Krumpelmann model fits to nucleon electromagnetic form factors

The Gari-Krumpelmann (GK) models of nucleon electromagnetic form factors, in which the rho, omega, and phi vector meson pole contributions evolve at high momentum transfer to conform to the predictions of perturbative QCD (pQCD), was recently extended to include the width of the rho meson by substituting the result of dispersion relations for the pole and the addition of rho' (1450) isovector vector meson pole. This extended model was shown to produce a good overall fit to all the available nucleon electromagnetic form factor (emff) data. Since then new polarization data shows that the electric to magnetic ratios R_p and R_n obtained are not consistent with the older G_{Ep} and G_{En} data in their range of momentum transfer. The model is further extended to include the omega' (1419) isoscalar vector meson pole. It is found that while this GKex cannot simultaneously fit the new R_p and the old G_{En} data, it can fit the new R_p and R_n well simultaneously. An excellent fit to all the remaining data is obtained when the inconsistent G_{Ep} and G_{En} is omitted. The model predictions are shown up to momentum transfer squared, Q^2, of 8 GeV^2/c^2.Comment: 14 pages, 8 figures, using RevTeX4; email correspondence to [email protected] ; minor typos corrected, figures added, conclusions extende

Peripheral NF-κB dysregulation in people with schizophrenia drives inflammation: putative anti-inflammatory functions of NF-κB kinases

Elevations in plasma levels of pro-inflammatory cytokines and C-reactive protein (CRP) in patient blood have been associated with impairments in cognitive abilities and more severe psychiatric symptoms in people with schizophrenia. The transcription factor nuclear factor kappa B (NF-κB) regulates the gene expression of pro-inflammatory factors whose protein products trigger CRP release. NF-κB activation pathway mRNAs are increased in the brain in schizophrenia and are strongly related to neuroinflammation. Thus, it is likely that this central immune regulator is also dysregulated in the blood and associated with cytokine and CRP levels. We measured levels of six pro-inflammatory cytokine mRNAs and 18 mRNAs encoding NF-κB pathway members in peripheral blood leukocytes from 87 people with schizophrenia and 83 healthy control subjects. We then assessed the relationships between the alterations in NF-κB pathway genes, pro-inflammatory cytokine and CRP levels, psychiatric symptoms and cognition in people with schizophrenia. IL-1β and IFN-γ mRNAs were increased in patients compared to controls (both p < 0.001), while IL-6, IL-8, IL-18, and TNF-α mRNAs did not differ. Recursive two-step cluster analysis revealed that high levels of IL-1β mRNA and high levels of plasma CRP defined 'high inflammation' individuals in our cohort, and a higher proportion of people with schizophrenia were identified as displaying 'high inflammation' compared to controls using this method (p = 0.03). Overall, leukocyte expression of the NF-κB-activating receptors, TLR4 and TNFR2, and the NF-κB subunit, RelB, was increased in people with schizophrenia compared to healthy control subjects (all p < 0.01), while NF-κB-inducing kinase mRNAs IKKβ and NIK were downregulated in patients (all p < 0.05). We found that elevations in TLR4 and RelB appear more related to inflammatory status than to a diagnosis of schizophrenia, but changes in TNFR2 occur in both the high and low inflammation patients (but were exaggerated in high inflammation patients). Further, decreased leukocyte expression of IKKβ and NIK mRNAs was unique to high inflammation patients, which may represent schizophrenia-specific dysregulation of NF-κB that gives rise to peripheral inflammation in a subset of patients.Caitlin E. Murphy, Adam K. Walker, Maryanne O, Donnell, Cherrie Galletly, Andrew R. Lloyd, Dennis Liu, Cynthia Shannon Weickert, and Thomas W. Weicker

Networks of blood proteins in the neuroimmunology of schizophrenia.

Levels of certain circulating cytokines and related immune system molecules are consistently altered in schizophrenia and related disorders. In addition to absolute analyte levels, we sought analytes in correlation networks that could be prognostic. We analyzed baseline blood plasma samples with a Luminex platform from 72 subjects meeting criteria for a psychosis clinical high-risk syndrome; 32 subjects converted to a diagnosis of psychotic disorder within two years while 40 other subjects did not. Another comparison group included 35 unaffected subjects. Assays of 141 analytes passed early quality control. We then used an unweighted co-expression network analysis to identify highly correlated modules in each group. Overall, there was a striking loss of network complexity going from unaffected subjects to nonconverters and thence to converters (applying standard, graph-theoretic metrics). Graph differences were largely driven by proteins regulating tissue remodeling (e.g. blood-brain barrier). In more detail, certain sets of antithetical proteins were highly correlated in unaffected subjects (e.g. SERPINE1 vs MMP9), as expected in homeostasis. However, for particular protein pairs this trend was reversed in converters (e.g. SERPINE1 vs TIMP1, being synthetical inhibitors of remodeling of extracellular matrix and vasculature). Thus, some correlation signals strongly predict impending conversion to a psychotic disorder and directly suggest pharmaceutical targets

Observation of Zitterbewegung in photonic microcavities

We present and experimentally study the effects of the photonic spin–orbit coupling on the real space propagation of polariton wavepackets in planar semiconductor microcavities and polaritonic analogues of graphene. In particular, we demonstrate the appearance of an analogue Zitterbewegung effect, a term which translates as ‘trembling motion’ in English, which was originally proposed for relativistic Dirac electrons and consisted of the oscillations of the centre of mass of a wavepacket in the direction perpendicular to its propagation. For a planar microcavity, we observe regular Zitterbewegung oscillations whose amplitude and period depend on the wavevector of the polaritons. We then extend these results to a honeycomb lattice of coupled microcavity resonators. Compared to the planar cavity, such lattices are inherently more tuneable and versatile, allowing simulation of the Hamiltonians of a wide range of important physical systems. We observe an oscillation pattern related to the presence of the spin-split Dirac cones in the dispersion. In both cases, the experimentally observed oscillations are in good agreement with theoretical modelling and independently measured bandstructure parameters, providing strong evidence for the observation of Zitterbewegung

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio