A census of ρ\rho Oph candidate members from Gaia DR2

The Ophiuchus cloud complex is one of the best laboratories to study the earlier stages of the stellar and protoplanetary disc evolution. The wealth of accurate astrometric measurements contained in the Gaia Data Release 2 can be used to update the census of Ophiuchus member candidates. We seek to find potential new members of Ophiuchus and identify those surrounded by a circumstellar disc. We constructed a control sample composed of 188 bona fide Ophiuchus members. Using this sample as a reference we applied three different density-based machine learning clustering algorithms (DBSCAN, OPTICS, and HDBSCAN) to a sample drawn from the Gaia catalogue centred on the Ophiuchus cloud. The clustering analysis was applied in the five astrometric dimensions defined by the three-dimensional Cartesian space and the proper motions in right ascension and declination. The three clustering algorithms systematically identify a similar set of candidate members in a main cluster with astrometric properties consistent with those of the control sample. The increased flexibility of the OPTICS and HDBSCAN algorithms enable these methods to identify a secondary cluster. We constructed a common sample containing 391 member candidates including 166 new objects, which have not yet been discussed in the literature. By combining the Gaia data with 2MASS and WISE photometry, we built the spectral energy distributions from 0.5 to 22\microm for a subset of 48 objects and found a total of 41 discs, including 11 Class II and 1 Class III new discs. Density-based clustering algorithms are a promising tool to identify candidate members of star forming regions in large astrometric databases. If confirmed, the candidate members discussed in this work would represent an increment of roughly 40% of the current census of Ophiuchus.Comment: A&A, Accepted. Abridged abstrac

The Distance to the Large Magellanic Cloud from the Eclipsing Binary HV2274

The distance to the Large Magellanic Cloud (LMC) is crucial for the calibration of the Cosmic Distance Scale. We derive a distance to the LMC based on an analysis of ground-based photometry and HST-based spectroscopy and spectrophotometry of the LMC eclipsing binary system HV2274. Analysis of the optical light curve and HST/GHRS radial velocity curve provides the masses and radii of the binary components. Analysis of the HST/FOS UV/optical spectrophotometry provides the temperatures of the component stars and the interstellar extinction of the system. When combined, these data yield a distance to the binary system. After correcting for the location of HV2274 with respect to the center of the LMC, we find d(LMC) = 45.7 +/- 1.6 kpc or DM(LMC) = 18.30 +/- 0.07 mag. This result, which is immune to the metallicity-induced zero point uncertainties that have plagued other techniques, lends strong support to the ``short'' LMC distance scale as derived from a number of independent methods.Comment: 6 pages, including 2 pages of figures. Newly available optical (B and V) photometry has revealed -- and allowed the elimination of -- a systematic error in the previously reported determination of E(B-V) for HV2274. The new result is E(B-V) = 0.12 mag (as compared to the value of 0.083 reported in the original submission) and produces a DECREASE in the distance modulus of HV2274 by 0.12 mag. ApJ Letters, in pres

Unsupervised adaptation of deep speech activity detection models to unseen domains

Speech Activity Detection (SAD) aims to accurately classify audio fragments containing human speech. Current state-of-the-art systems for the SAD task are mainly based on deep learning solutions. These applications usually show a significant drop in performance when test data are different from training data due to the domain shift observed. Furthermore, machine learning algorithms require large amounts of labelled data, which may be hard to obtain in real applications. Considering both ideas, in this paper we evaluate three unsupervised domain adaptation techniques applied to the SAD task. A baseline system is trained on a combination of data from different domains and then adapted to a new unseen domain, namely, data from Apollo space missions coming from the Fearless Steps Challenge. Experimental results demonstrate that domain adaptation techniques seeking to minimise the statistical distribution shift provide the most promising results. In particular, Deep CORAL method reports a 13% relative improvement in the original evaluation metric when compared to the unadapted baseline model. Further experiments show that the cascaded application of Deep CORAL and pseudo-labelling techniques can improve even more the results, yielding a significant 24% relative improvement in the evaluation metric when compared to the baseline system

Biogeography And Diversification Of Rhegmatorhina (Aves: Thamnophilidae): Implications For The Evolution Of Amazonian Landscapes During The Quaternary

Aim: To test the importance of alternative diversification drivers and biogeographical processes for the evolution of Amazonian upland forest birds through a densely sampled analysis of diversification of the endemic Amazonian genus Rhegmatorhina at multiple taxonomic and temporal scales. Location: Amazonia. Taxon: Antbirds (Thamnophilidae). Methods: We sequenced four mtDNA and nuclear gene regions of 120 individuals from 50 localities representing all recognized species and subspecies of the genus. We performed molecular phylogenetic analyses using both gene tree and species tree methods, molecular dating analysis and estimated population demographic history and gene flow. Results: Dense sampling throughout the distribution of Rhegmatorhina revealed that the main Amazonian rivers delimit the geographic distribution of taxa as inferred from mtDNA lineages. Molecular phylogenetic analyses resulted in a strongly supported phylogenetic hypothesis for the genus, with two main clades currently separated by the Madeira River. Molecular dating analysis indicated diversification during the Quaternary. Reconstruction of recent demographic history of populations revealed a trend for population expansion in eastern Amazonia and stability in the west. Estimates of gene flow corroborate the possibility that migration after divergence had some influence on the current patterns of diversity. Main Conclusions: Based on broad-scale sampling, a clarification of taxonomic boundaries, and strongly supported phylogenetic relationships, we confirm that, first, mitochondrial lineages within this upland forest Amazonian bird genus agree with spatial patterns known for decades based on phenotypes, and second, that most lineages are geographically delimited by the large Amazonian rivers. The association between past demographic changes related to palaeoclimatic cycles and the historically varying strength and size of rivers as barriers to dispersal may be the path to the answer to the long-standing question of identifying the main drivers of Amazonian diversification

An Intervention-AUV learns how to perform an underwater valve turning

Intervention autonomous underwater vehicles (I-AUVs) are a promising platform to perform intervention task in underwater environments, replacing current methods like remotely operate underwater vehicles (ROVs) and manned sub-mersibles that are more expensive. This article proposes a complete system including all the necessary elements to perform a valve turning task using an I-AUV. The knowledge of an operator to perform the task is transmitted to an I-AUV by a learning by demonstration (LbD) algorithm. The algorithm learns the trajectory of the vehicle and the end-effector to accomplish the valve turning. The method has shown its feasibility in a controlled environment repeating the learned task with different valves and configurations

Progressive loss functions for speech enhancement with deep neural networks

The progressive paradigm is a promising strategy to optimize network performance for speech enhancement purposes. Recent works have shown different strategies to improve the accuracy of speech enhancement solutions based on this mechanism. This paper studies the progressive speech enhancement using convolutional and residual neural network architectures and explores two criteria for loss function optimization: weighted and uniform progressive. This work carries out the evaluation on simulated and real speech samples with reverberation and added noise using REVERB and VoiceHome datasets. Experimental results show a variety of achievements among the loss function optimization criteria and the network architectures. Results show that the progressive design strengthens the model and increases the robustness to distortions due to reverberation and noise

Spectroscopic characterisation of CARMENES target candidates from FEROS, CAFE and HRS high-resolution spectra

CARMENES (Calar Alto high-Resolution search for M dwarfs with Exoearths with Near-infrared and optical Echelle Spectrographs) started a new planet survey on M-dwarfs in January this year. The new high-resolution spectrographs are operating in the visible and near-infrared at Calar Alto Observatory. They will perform high-accuracy radial-velocity measurements (goal 1 m s-1) of about 300 M-dwarfs with the aim to detect low-mass planets within habitable zones. We characterised the candidate sample for CARMENES and provide fundamental parameters for these stars in order to constrain planetary properties and understand star-planet systems. Using state-of-the-art model atmospheres (PHOENIX-ACES) and chi2-minimization with a downhill-simplex method we determine effective temperature, surface gravity and metallicity [Fe/H] for high-resolution spectra of around 480 stars of spectral types M0.0-6.5V taken with FEROS, CAFE and HRS. We find good agreement between the models and our observed high-resolution spectra. We show the performance of the algorithm, as well as results, parameter and spectral type distributions for the CARMENES candidate sample, which is used to define the CARMENES target sample. We also present first preliminary results obtained from CARMENES spectra

Human dendritic cells adenovirally-engineered to express three defined tumor antigens promote broad adaptive and innate immunity

Dendritic cell (DC) immunotherapy has shown a promising ability to promote anti-tumor immunity in vitro and in vivo. Many trials have tested single epitopes and single antigens to activate single T cell specificities, and often CD8+ T cells only. We previously found that determinant spreading and breadth of antitumor immunity correlates with improved clinical response. Therefore, to promote activation and expansion of polyclonal, multiple antigen-specific CD8+ T cells, as well as provide cognate help from antigen-specific CD4+ T cells, we have created an adenovirus encoding three full length melanoma tumor antigens (tyrosinase, MART-1 and MAGE-A6, “AdVTMM”). We previously showed that adenovirus (AdV)-mediated antigen engineering of human DC is superior to peptide pulsing for T cell activation, and has positive biological effects on the DC, allowing for efficient activation of not only antigen-specific CD8+ and CD4+ T cells, but also NK cells. Here we describe the cloning and testing of “AdVTMM2,” an E1/E3-deleted AdV encoding the three melanoma antigens. This novel three-antigen virus expresses mRNA and protein for all antigens, and AdVTMM-transduced DC activate both CD8+ and CD4+ T cells which recognize melanoma tumor cells more efficiently than single antigen AdV. Addition of physiological levels of interferon-α (IFNα) further amplifies melanoma antigen-specific T cell activation. NK cells are also activated, and show cytotoxic activity. Vaccination with multi-antigen engineered DC may provide for superior adaptive and innate immunity and ultimately, improved antitumor responses