Improving single-photon sources via linear optics and photodetection

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How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices

Supporting systematic reviews using LDA-based document representations

BACKGROUND: Identifying relevant studies for inclusion in a systematic review (i.e. screening) is a complex, laborious and expensive task. Recently, a number of studies has shown that the use of machine learning and text mining methods to automatically identify relevant studies has the potential to drastically decrease the workload involved in the screening phase. The vast majority of these machine learning methods exploit the same underlying principle, i.e. a study is modelled as a bag-of-words (BOW). METHODS: We explore the use of topic modelling methods to derive a more informative representation of studies. We apply Latent Dirichlet allocation (LDA), an unsupervised topic modelling approach, to automatically identify topics in a collection of studies. We then represent each study as a distribution of LDA topics. Additionally, we enrich topics derived using LDA with multi-word terms identified by using an automatic term recognition (ATR) tool. For evaluation purposes, we carry out automatic identification of relevant studies using support vector machine (SVM)-based classifiers that employ both our novel topic-based representation and the BOW representation. RESULTS: Our results show that the SVM classifier is able to identify a greater number of relevant studies when using the LDA representation than the BOW representation. These observations hold for two systematic reviews of the clinical domain and three reviews of the social science domain. CONCLUSIONS: A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening. The proposed term-enriched topics are more informative and less ambiguous to systematic reviewers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13643-015-0117-0) contains supplementary material, which is available to authorized users

Nitrogen fixation in a landrace of maize is supported by a mucilage-associated diazotrophic microbiota

© 2018 Van Deynze et al. http://creativecommons.org/licenses/by/4.0/. Plants are associated with a complex microbiota that contributes to nutrient acquisition, plant growth, and plant defense. Nitrogen-fixing microbial associations are efficient and well characterized in legumes but are limited in cereals, including maize. We studied an indigenous landrace of maize grown in nitrogen-depleted soils in the Sierra Mixe region of Oaxaca, Mexico. This landrace is characterized by the extensive development of aerial roots that secrete a carbohydrate-rich mucilage. Analysis of the mucilage microbiota indicated that it was enriched in taxa for which many known species are diazotrophic, was enriched for homologs of genes encoding nitrogenase subunits, and harbored active nitrogenase activity as assessed by acetylene reduction and 15 N 2 incorporation assays. Field experiments in Sierra Mixe using 15 N natural abundance or 15 N-enrichment assessments over 5 years indicated that atmospheric nitrogen fixation contributed 29%–82% of the nitrogen nutrition of Sierra Mixe maize

All-sky search for short gravitational-wave bursts in the second Advanced LIGO and Advanced Virgo run

We present the results of a search for short-duration gravitational-wave transients in the data from the second observing run of Advanced LIGO and Advanced Virgo. We search for gravitational-wave transients with a duration of milliseconds to approximately one second in the 32-4096 Hz frequency band with minimal assumptions about the signal properties, thus targeting a wide variety of sources. We also perform a matched-filter search for gravitational-wave transients from cosmic string cusps for which the waveform is well modeled. The unmodeled search detected gravitational waves from several binary black hole mergers which have been identified by previous analyses. No other significant events have been found by either the unmodeled search or the cosmic string search. We thus present the search sensitivities for a variety of signal waveforms and report upper limits on the source rate density as a function of the characteristic frequency of the signal. These upper limits are a factor of 3 lower than the first observing run, with a 50% detection probability for gravitational-wave emissions with energies of ∼10-9 Mc2 at 153 Hz. For the search dedicated to cosmic string cusps we consider several loop distribution models, and present updated constraints from the same search done in the first observing run

Search for gravitational waves from Scorpius X-1 in the second Advanced LIGO observing run with an improved hidden Markov model

We present results from a semicoherent search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1, using a hidden Markov model (HMM) to track spin wandering. This search improves on previous HMM-based searches of LIGO data by using an improved frequency domain matched filter, the $\mathcal{J}$-statistic, and by analysing data from Advanced LIGO's second observing run. In the frequency range searched, from $60$ to $650\,\mathrm{Hz}$, we find no evidence of gravitational radiation. At $194.6\,\mathrm{Hz}$, the most sensitive search frequency, we report an upper limit on gravitational wave strain (at 95\% confidence) of $h_0^{95\%} = 3.47 \times 10^{-25}$ when marginalising over source inclination angle. This is the most sensitive search for Scorpius X-1, to date, that is specifically designed to be robust in the presence of spin wandering

Search for Eccentric Binary Black Hole Mergers with Advanced LIGO and Advanced Virgo during Their First and Second Observing Runs

When formed through dynamical interactions, stellar-mass binary black holes (BBHs) may retain eccentric orbits (e > 0.1 at 10 Hz) detectable by ground-based gravitational-wave detectors. Eccentricity can therefore be used to differentiate dynamically formed binaries from isolated BBH mergers. Current template-based gravitational-wave searches do not use waveform models associated with eccentric orbits, rendering the search less efficient for eccentric binary systems. Here we present the results of a search for BBH mergers that inspiral in eccentric orbits using data from the first and second observing runs (O1 and O2) of Advanced LIGO and Advanced Virgo. We carried out the search with the coherent WaveBurst algorithm, which uses minimal assumptions on the signal morphology and does not rely on binary waveform templates. We show that it is sensitive to binary mergers with a detection range that is weakly dependent on eccentricity for all bound systems. Our search did not identify any new binary merger candidates. We interpret these results in light of eccentric binary formation models. We rule out formation channels with rates ⪆100 Gpc-3 yr-1 for e > 0.1, assuming a black hole mass spectrum with a power-law index ≲2

Search for intermediate mass black hole binaries in the first and second observing runs of the Advanced LIGO and Virgo network

Gravitational-wave astronomy has been firmly established with the detection of gravitational waves from the merger of ten stellar-mass binary black holes and a neutron star binary. This paper reports on the all-sky search for gravitational waves from intermediate mass black hole binaries in the first and second observing runs of the Advanced LIGO and Virgo network. The search uses three independent algorithms: two based on matched filtering of the data with waveform templates of gravitational-wave signals from compact binaries, and a third, model-independent algorithm that employs no signal model for the incoming signal. No intermediate mass black hole binary event is detected in this search. Consequently, we place upper limits on the merger rate density for a family of intermediate mass black hole binaries. In particular, we choose sources with total masses M=m1+m2ϵ[120,800] M and mass ratios q=m2/m1ϵ[0.1,1.0]. For the first time, this calculation is done using numerical relativity waveforms (which include higher modes) as models of the real emitted signal. We place a most stringent upper limit of 0.20 Gpc-3 yr-1 (in comoving units at the 90% confidence level) for equal-mass binaries with individual masses m1,2=100 M and dimensionless spins χ1,2=0.8 aligned with the orbital angular momentum of the binary. This improves by a factor of ∼5 that reported after Advanced LIGO's first observing run