Landscape population genetics and the role of organic farming

This project aims at understanding the effect of different farming systems on the genetic diversity of common agricultural species. It is well known that organic farming generally improves the biodiversity and abundance of species in the agricultural landscape (Hole et al., 2005). A reduction in species number and abundance has been shown as a result of the intensification of farming suggesting a relationship between farming intensity and species abundance (e.g. Stoate et al., 2001). Anyway, none of the studies that investigated the effects of pesticides presence and farming intensity has investigated the effect on the genetic diversity and isolation of the populations. It has been shown that, despite the theoretical expectations, also very abundant species like Abax parallelepipedus can be divided in isolated and genetically distinct populations within very few years in response to human activity (e.g. construction of streets: Keller et al., 2004). Therefore, we chose two common agricultural species (field vole, Microtus agrestis, and a non-pest ground beetle, Bembidion lampros) belonging to different taxa and with different dispersal abilities, to investigate the effect of pesticide use and intensiveness of farming on their genetic structuring and diversity

Multi-task Learning for Speaker Verification and Voice Trigger Detection

Automatic speech transcription and speaker recognition are usually treated as separate tasks even though they are interdependent. In this study, we investigate training a single network to perform both tasks jointly. We train the network in a supervised multi-task learning setup, where the speech transcription branch of the network is trained to minimise a phonetic connectionist temporal classification (CTC) loss while the speaker recognition branch of the network is trained to label the input sequence with the correct label for the speaker. We present a large-scale empirical study where the model is trained using several thousand hours of labelled training data for each task. We evaluate the speech transcription branch of the network on a voice trigger detection task while the speaker recognition branch is evaluated on a speaker verification task. Results demonstrate that the network is able to encode both phonetic \emph{and} speaker information in its learnt representations while yielding accuracies at least as good as the baseline models for each task, with the same number of parameters as the independent models

Driver frustration detection from audio and video in the wild

We present a method for detecting driver frustration from both video and audio streams captured during the driver's interaction with an in-vehicle voice-based navigation system. The video is of the driver's face when the machine is speaking, and the audio is of the driver's voice when he or she is speaking. We analyze a dataset of 20 drivers that contains 596 audio epochs (audio clips, with duration from 1 sec to 15 sec) and 615 video epochs (video clips, with duration from 1 sec to 45 sec). The dataset is balanced across 2 age groups, 2 vehicle systems, and both genders. The model was subject-independently trained and tested using 4-fold cross-validation. We achieve an accuracy of 77.4% for detecting frustration from a single audio epoch and 81.2% for detecting frustration from a single video epoch. We then treat the video and audio epochs as a sequence of interactions and use decision fusion to characterize the trade-off between decision time and classification accuracy, which improved the prediction accuracy to 88.5% after 9 epochs

The SAGA Survey: I. Satellite Galaxy Populations Around Eight Milky Way Analogs

We present the survey strategy and early results of the "Satellites Around Galactic Analogs" (SAGA) Survey. The SAGA Survey's goal is to measure the distribution of satellite galaxies around 100 systems analogous to the Milky Way down to the luminosity of the Leo I dwarf galaxy ($M_r < -12.3$). We define a Milky Way analog based on $K$-band luminosity and local environment. Here, we present satellite luminosity functions for 8 Milky Way analog galaxies between 20 to 40 Mpc. These systems have nearly complete spectroscopic coverage of candidate satellites within the projected host virial radius down to $r_o < 20.75$ using low redshift $gri$ color criteria. We have discovered a total of 25 new satellite galaxies: 14 new satellite galaxies meet our formal criteria around our complete host systems, plus 11 additional satellites in either incompletely surveyed hosts or below our formal magnitude limit. Combined with 13 previously known satellites, there are a total of 27 satellites around 8 complete Milky Way analog hosts. We find a wide distribution in the number of satellites per host, from 1 to 9, in the luminosity range for which there are five Milky Way satellites. Standard abundance matching extrapolated from higher luminosities predicts less scatter between hosts and a steeper luminosity function slope than observed. We find that the majority of satellites (26 of 27) are star-forming. These early results indicate that the Milky Way has a different satellite population than typical in our sample, potentially changing the physical interpretation of measurements based only on the Milky Way's satellite galaxies.Comment: 22 pages, 19 figures, 2 tables. Updated to published version. Survey website: http://sagasurvey.org

An investigation of the 'female camouflage effect' in autism using a computerized ADOS-2 and a test of sex/gender differences.

BACKGROUND: Autism spectrum conditions (autism) are diagnosed more frequently in boys than in girls. Females with autism may have been under-identified due to not only a male-biased understanding of autism but also females' camouflaging. The study describes a new technique that allows automated coding of non-verbal mode of communication (gestures) and offers the possibility of objective, evaluation of gestures, independent of human judgment. The EyesWeb software platform and the Kinect sensor during two demonstration activities of ADOS-2 (Autism Diagnostic Observation Schedule, Second Edition) were used. METHODS: The study group consisted of 33 high-functioning Polish girls and boys with formal diagnosis of autism or Asperger syndrome aged 5-10, with fluent speech, IQ average and above and their parents (girls with autism, n = 16; boys with autism, n = 17). All children were assessed during two demonstration activities of Module 3 of ADOS-2, administered in Polish, and coded using Polish codes. Children were also assessed with Polish versions of the Eyes and Faces Tests. Parents provided information on the author-reviewed Polish research translation of SCQ (Social Communication Questionnaire, Current and Lifetime) and Polish version of AQ Child (Autism Spectrum Quotient, Child). RESULTS: Girls with autism tended to use gestures more vividly as compared to boys with autism during two demonstration activities of ADOS-2. Girls with autism made significantly more mistakes than boys with autism on the Faces Test. All children with autism had high scores in AQ Child, which confirmed the presence of autistic traits in this group. The current communication skills of boys with autism reported by parents in SCQ were significantly better than those of girls with autism. However, both girls with autism and boys with autism improved in the social and communication abilities over the lifetime. The number of stereotypic behaviours in boys significantly decreased over life whereas it remained at a comparable level in girls with autism. CONCLUSIONS: High-functioning females with autism might present better on non-verbal (gestures) mode of communication than boys with autism. It may camouflage other diagnostic features. It poses risk of under-diagnosis or not receiving the appropriate diagnosis for this population. Further research is required to examine this phenomenon so appropriate gender revisions to the diagnostic assessments might be implemented.SBC was supported by the Autism Research Trust and the Medical Research Council UK during the period of this work, and the team were supported by the EU ASC-Inclusion.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13229-016-0073-0

The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism

The INTERSPEECH 2013 Computational Paralinguistics Challenge provides for the first time a unified test-bed for Social Signals such as laughter in speech. It further introduces conflict in group discussions as new tasks and picks up on autism and its manifestations in speech. Finally, emotion is revisited as task, albeit with a broader ranger of overall twelve emotional states. In this paper, we describe these four Sub-Challenges, Challenge conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants. \em Bj\"orn Schuller$^1$, Stefan Steidl$^2$, Anton Batliner$^1$, Alessandro Vinciarelli$^{3,4}$, Klaus Scherer$^5$}\\ {\em Fabien Ringeval$^6$, Mohamed Chetouani$^7$, Felix Weninger$^1$, Florian Eyben$^1$, Erik Marchi$^1$, }\\ {\em Hugues Salamin$^3$, Anna Polychroniou$^3$, Fabio Valente$^4$, Samuel Kim$^4$