2,215 research outputs found

    Quantifying Uncertainties in Natural Language Processing Tasks

    Full text link
    Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.Comment: To appear at AAAI 201

    Translation of EEG spatial filters from resting to motor imagery using independent component analysis.

    Get PDF
    Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters

    Numerical Modelling of Oblique Subduction in the Southern Andes Region

    Get PDF
    The Southern Andes is an important region to study strain partitioning behavior due to the variable nature of its subduction geometry and continental mechanical properties. Along the plate margin between the Nazca plate and the South American plate, the strain partitioning behavior varies from north to south, while the plate convergence vector shows little change. The study area, the LOFZ region, lies between 38⁰S to 46⁰S in the Southern Andes at around 100 km east of the trench. It has been characterized as an area bounded by margin-parallel strike-slip faults that creates a forearc sliver, the Chiloe block. It is also located on top of an active volcanic zone, the Southern Volcanic Zone (SVZ). This area is notably different from the Pampean flat-slab segment directly to the north of it (between latitude 28⁰ S and 33⁰ S), where volcanic activity is absent, and slip seems to be accommodated completely by oblique subduction. Seismicity in central LOFZ is spatially correlated with NE trending margin-oblique faults that are similar to the structure of SC-like kinematics described by Hippertt (1999). The margin-oblique faults and rhomb-shaped domains that accommodate strain have also been captured in analog experiments by Eisermann et al. (2018) and Eisermann relates the change in GPS velocity at the northern end of LOFZ to a decrease in crustal strength southward possibly caused by the change in dip angle. This project uses DOUAR (Braun et al. 2008), a numerical modelling software, to explore the formation of the complex fault system in the LOFZ in relation to strain partitioning in the Southern Andes. We implement the numerical versions of the analog models from Eisermann et al. (2018), called the MultiBox and NatureBox models to test the possibility to reproduce analog modelling results with numerical models. We also create simplified models of the LOFZ, the Natural System models, to compare the model displacement field with deformation pattern in the area. Our numerical model results in general replicate the findings from MultiBox experiment of Eisermann et al. (2018). We observe the formation of NW trending margin-oblique faulting in the central deformation zone, which creates rhombshaped blocks together with the margin-parallel faults. More strain is accommodated in the stronger part of the model, where the strain is more distributed across the area or prefers to settle on a few larger bounding faults, whereas in the weaker part of the model, the strain tends to localize on more smaller faults. The margin-oblique faults and rhomb-shaped domains accommodating strain is not present in the Natural System models with and without a strength difference along strike. This brings the question about the formation of the complex fault system in both the analog models and our numerical versions of them and hypothesis other than a strength gradient could be tested in the future

    An “Assurance of Security” or “Agent of Tracking”? An Empirical Study of the Impact of Vocational Education on Social Mobility

    Get PDF
    Published in the Journal of East China Normal University (Educational Sciences), this study drew on data from the China Family Panel Survey (CFPS) by the Institute of Social Science Survey at Peking University as the research sample to examine the impact of education on social mobility as well as the influences of the registered residence, administrative region and other factors on social mobility of populations with different educational levels. The descriptive analysis and linear regression methods were adopted in the research

    Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

    Get PDF
    BackgroundBridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking.MethodsThis study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment.ResultsDespite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s).ConclusionsSSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications
    • …
    corecore