Unsupervised crosslingual adaptation of tokenisers for spoken language recognition

Phone tokenisers are used in spoken language recognition (SLR) to obtain elementary phonetic information. We present a study on the use of deep neural network tokenisers. Unsupervised crosslingual adaptation was performed to adapt the baseline tokeniser trained on English conversational telephone speech data to different languages. Two training and adaptation approaches, namely cross-entropy adaptation and state-level minimum Bayes risk adaptation, were tested in a bottleneck i-vector and a phonotactic SLR system. The SLR systems using the tokenisers adapted to different languages were combined using score fusion, giving 7-18% reduction in minimum detection cost function (minDCF) compared with the baseline configurations without adapted tokenisers. Analysis of results showed that the ensemble tokenisers gave diverse representation of phonemes, thus bringing complementary effects when SLR systems with different tokenisers were combined. SLR performance was also shown to be related to the quality of the adapted tokenisers

Unsupervised crosslingual adaptation of tokenisers for spoken language recognition

Phone tokenisers are used in spoken language recognition (SLR) to obtain elementary phonetic information. We present a study on the use of deep neural network tokenisers. Unsupervised crosslingual adaptation was performed to adapt the baseline tokeniser trained on English conversational telephone speech data to different languages. Two training and adaptation approaches, namely cross-entropy adaptation and state-level minimum Bayes risk adaptation, were tested in a bottleneck i-vector and a phonotactic SLR system. The SLR systems using the tokenisers adapted to different languages were combined using score fusion, giving 7-18% reduction in minimum detection cost function (minDCF) compared with the baseline configurations without adapted tokenisers. Analysis of results showed that the ensemble tokenisers gave diverse representation of phonemes, thus bringing complementary effects when SLR systems with different tokenisers were combined. SLR performance was also shown to be related to the quality of the adapted tokenisers

Carbonate-Templated Self-Assembly of an Alkylthiolate-Bridged Cadmium Macrocycle

In the presence of Cd(ClO4)2 and a base, a new mixed N,S-donor alkylthiolate ligand supported both carbonate formation from atmospheric CO2 and the self-assembly of a novel bicapped puckered (CdS)6 molecular wheel. The remarkable stability of the complex was demonstrated by slow intermolecular ligand exchange on the 2J(HH) and J(111/113Cd1H) time scales at elevated temperature. Both CO2 and the base were required to convert amorphous “CdLClO4” precipitated in the absence of air to the carbonate complex. The complex shares structural features with the ζ-carbonic anhydrase class associating cadmium(II) with the biogeochemical cycling of carbon and is the first structurally characterized carbonate complex of any metal involving an alkylthiolate ligand

The role of the cerebellum in adaptation: ALE meta‐analyses on sensory feedback error

It is widely accepted that unexpected sensory consequences of self‐action engage the cerebellum. However, we currently lack consensus on where in the cerebellum, we find fine‐grained differentiation to unexpected sensory feedback. This may result from methodological diversity in task‐based human neuroimaging studies that experimentally alter the quality of self‐generated sensory feedback. We gathered existing studies that manipulated sensory feedback using a variety of methodological approaches and performed activation likelihood estimation (ALE) meta‐analyses. Only half of these studies reported cerebellar activation with considerable variation in spatial location. Consequently, ALE analyses did not reveal significantly increased likelihood of activation in the cerebellum despite the broad scientific consensus of the cerebellum's involvement. In light of the high degree of methodological variability in published studies, we tested for statistical dependence between methodological factors that varied across the published studies. Experiments that elicited an adaptive response to continuously altered sensory feedback more frequently reported activation in the cerebellum than those experiments that did not induce adaptation. These findings may explain the surprisingly low rate of significant cerebellar activation across brain imaging studies investigating unexpected sensory feedback. Furthermore, limitations of functional magnetic resonance imaging to probe the cerebellum could play a role as climbing fiber activity associated with feedback error processing may not be captured by it. We provide methodological recommendations that may guide future studies

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level.Comment: 24 pages, 5 figure

Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages.Comment: 27 pages, 3 figure

Open data from the third observing run of LIGO, Virgo, KAGRA, and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages