Transcribing and annotating speech corpora for speech recognition: A three-step crowdsourcing approach with quality control

Large speech corpora with word-level transcriptions annotated for noises and disfluent speech are necessary for training automatic speech recognisers. Crowdsourcing is a lower-cost, faster-turnaround, highly scalable alternative for expert transcription and annotation. In this paper, we showcase our three-step crowdsourcing approach motivated by the importance of accurate transcriptions and annotations.info:eu-repo/semantics/publishedVersio

Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups

We strive to find contexts (i.e., subgroups of entities) under which exceptional (dis-)agreement occurs among a group of individuals , in any type of data featuring individuals (e.g., parliamentarians , customers) performing observable actions (e.g., votes, ratings) on entities (e.g., legislative procedures, movies). To this end, we introduce the problem of discovering statistically significant exceptional contextual intra-group agreement patterns. To handle the sparsity inherent to voting and rating data, we use Krippendorff's Alpha measure for assessing the agreement among individuals. We devise a branch-and-bound algorithm , named DEvIANT, to discover such patterns. DEvIANT exploits both closure operators and tight optimistic estimates. We derive analytic approximations for the confidence intervals (CIs) associated with patterns for a computationally efficient significance assessment. We prove that these approximate CIs are nested along specialization of patterns. This allows to incorporate pruning properties in DEvIANT to quickly discard non-significant patterns. Empirical study on several datasets demonstrates the efficiency and the usefulness of DEvIANT. Technical Report Associated with the ECML/PKDD 2019 Paper entitled: "DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups"

Ensemble deep clustering analysis for time window determination of event-related potentials

Data availability: Data will be made available on request.Copyright © 2023 The Authors. Objective: Cluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. Methods: We designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time window. Results: After applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20 dB to −5 dB white Gaussian noise) was added to the prepared data. Conclusion: Compared to the state-of-the-art clustering methods, a superior clustering performance was yielded from both ERP data. Furthermore, more stable and precise time windows were elicited as the noise increased. Significance: Our study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect after preprocessing

High resolution mapping of a novel late blight resistance gene Rpi-avll, from the wild Bolivian species Solanum avilesii

Both Mexico and South America are rich in Solanum species that might be valuable sources of resistance (R) genes to late blight (Phytophthora infestans). Here, we focus on an R gene present in the diploid Bolivian species S. avilesii. The genotype carrying the R gene was resistant to eight out of 10 Phytophthora isolates of various provenances. The identification of a resistant phenotype and the generation of a segregating population allowed the mapping of a single dominant R gene, Rpi-avl1, which is located in an R gene cluster on chromosome 11. This R gene cluster is considered as an R gene “hot spot”, containing R genes to at least five different pathogens. High resolution mapping of the Rpi-avl1 gene revealed a marker co-segregating in 3890 F1 individuals, which may be used for marker assisted selection in breeding programs and for further cloning of Rpi-avl

Boron Nitride Monolayer: A Strain-Tunable Nanosensor

The influence of triaxial in-plane strain on the electronic properties of a hexagonal boron-nitride sheet is investigated using density functional theory. Different from graphene, the triaxial strain localizes the molecular orbitals of the boron-nitride flake in its center depending on the direction of the applied strain. The proposed technique for localizing the molecular orbitals that are close to the Fermi level in the center of boron nitride flakes can be used to actualize engineered nanosensors, for instance, to selectively detect gas molecules. We show that the central part of the strained flake adsorbs polar molecules more strongly as compared with an unstrained sheet.Comment: 20 pages, 9 figure

Treatment of primary headache in children: a multicenter hospital-based study in France

The aim of this 6-month, prospective, multicenter study of 398 children and adolescents with primary headaches was to collect data on headache treatment in neuropediatric departments. Treatments were compared before and after consultation. Prior to consultation, the acute treatments that had been prescribed most frequently were paracetamol (82.2% of children) and non-steroidal anti-inflammatory drugs treatment (53.5%); 10.3% had received a prophylactic treatment. No differences in either acute or prophylactic treatment with respect to headache diagnosis were observed. After the neuropediatric consultation, paracetamol was replaced by a non-steroidal anti-inflammatory drug in about three-quarters of cases and by triptan in about one-quarter of cases. The number of children prescribed a prophylactic treatment nearly doubled, whereas there was a 5-fold and 23-fold increase in psychotherapy and relaxation training, respectively, between pre-referral and referral. We conclude that specific treatments were underused for primary headache

Specifying computer-supported collaboration scripts

Collaboration scripts are activity programs which aim to foster collaborative learning by structuring interaction between learners. Computer-supported collaboration scripts generally suffer from the problem of being restrained to a specific learning platform and learning context. A standardization of collaboration scripts first requires a specification of collaboration scripts that integrates multiple perspectives from computer science, education and psychology. So far, only few and limited attempts at such specifications have been made. This paper aims to consolidate and expand these approaches in light of recent findings and to propose a generic framework for the specification of collaboration scripts. The framework enables a description of collaboration scripts using a small number of components (participants, activities, roles, resources and groups) and mechanisms (task distribution, group formation and sequencing)

Sensitivity of MEG and EEG to Source Orientation

An important difference between magnetoencephalography (MEG) and electroencephalography (EEG) is that MEG is insensitive to radially oriented sources. We quantified computationally the dependency of MEG and EEG on the source orientation using a forward model with realistic tissue boundaries. Similar to the simpler case of a spherical head model, in which MEG cannot see radial sources at all, for most cortical locations there was a source orientation to which MEG was insensitive. The median value for the ratio of the signal magnitude for the source orientation of the lowest and the highest sensitivity was 0.06 for MEG and 0.63 for EEG. The difference in the sensitivity to the source orientation is expected to contribute to systematic differences in the signal-to-noise ratio between MEG and EEG.National Institutes of Health (U.S.) (Grant NS057500)National Institutes of Health (U.S.) (Grant NS037462)National Institutes of Health (U.S.) (Grant HD040712)National Center for Research Resources (U.S.) (P41RR14075)Mind Research Networ