59,748 research outputs found
Anatomo-functional correspondence in the superior temporal sulcus
The superior temporal sulcus (STS) is an intriguing region both for its complex anatomy and for the multiple functions that it hosts. Unfortunately, most studies explored either the functional organization or the anatomy of the STS only. Here, we link these two aspects by investigating anatomo-functional correspondences between the voice-sensitive cortex (Temporal Voice Areas) and the STS depth. To do so, anatomical and functional scans of 116 subjects were processed such as to generate individual surface maps on which both depth and functional voice activity can be analyzed. Individual depth profiles of manually drawn STS and functional profiles from a voice localizer (voice > non-voice) maps were extracted and compared to assess anatomo-functional correspondences. Three major results were obtained: first, the STS exhibits a highly significant rightward depth asymmetry in its middle part. Second, there is an anatomo-functional correspondence between the location of the voice-sensitive peak and the deepest point inside this asymmetrical region bilaterally. Finally, we showed that this correspondence was independent of the gender and, using a machine learning approach, that it existed at the individual level. These findings offer new perspectives for the understanding of anatomo-functional correspondences in this complex cortical region
Low-frequency oscillatory correlates of auditory predictive processing in cortical-subcortical networks: a MEG-study
Emerging evidence supports the role of neural oscillations as a mechanism for predictive information processing across large-scale networks. However, the oscillatory signatures underlying auditory mismatch detection and information flow between brain regions remain unclear. To address this issue, we examined the contribution of oscillatory activity at theta/alpha-bands (4–8/8–13 Hz) and assessed directed connectivity in magnetoencephalographic data while 17 human participants were presented with sound sequences containing predictable repetitions and order manipulations that elicited prediction-error responses. We characterized the spectro-temporal properties of neural generators using a minimum-norm approach and assessed directed connectivity using Granger Causality analysis. Mismatching sequences elicited increased theta power and phase-locking in auditory, hippocampal and prefrontal cortices, suggesting that theta-band oscillations underlie prediction-error generation in cortical-subcortical networks. Furthermore, enhanced feedforward theta/alpha-band connectivity was observed in auditory-prefrontal networks during mismatching sequences, while increased feedback connectivity in the alpha-band was observed between hippocampus and auditory regions during predictable sounds. Our findings highlight the involvement of hippocampal theta/alpha-band oscillations towards auditory prediction-error generation and suggest a spectral dissociation between inter-areal feedforward vs. feedback signalling, thus providing novel insights into the oscillatory mechanisms underlying auditory predictive processing
Estimating snow cover from publicly available images
In this paper we study the problem of estimating snow cover in mountainous
regions, that is, the spatial extent of the earth surface covered by snow. We
argue that publicly available visual content, in the form of user generated
photographs and image feeds from outdoor webcams, can both be leveraged as
additional measurement sources, complementing existing ground, satellite and
airborne sensor data. To this end, we describe two content acquisition and
processing pipelines that are tailored to such sources, addressing the specific
challenges posed by each of them, e.g., identifying the mountain peaks,
filtering out images taken in bad weather conditions, handling varying
illumination conditions. The final outcome is summarized in a snow cover index,
which indicates for a specific mountain and day of the year, the fraction of
visible area covered by snow, possibly at different elevations. We created a
manually labelled dataset to assess the accuracy of the image snow covered area
estimation, achieving 90.0% precision at 91.1% recall. In addition, we show
that seasonal trends related to air temperature are captured by the snow cover
index.Comment: submitted to IEEE Transactions on Multimedi
MoSculp: Interactive Visualization of Shape and Time
We present a system that allows users to visualize complex human motion via
3D motion sculptures---a representation that conveys the 3D structure swept by
a human body as it moves through space. Given an input video, our system
computes the motion sculptures and provides a user interface for rendering it
in different styles, including the options to insert the sculpture back into
the original video, render it in a synthetic scene or physically print it.
To provide this end-to-end workflow, we introduce an algorithm that estimates
that human's 3D geometry over time from a set of 2D images and develop a
3D-aware image-based rendering approach that embeds the sculpture back into the
scene. By automating the process, our system takes motion sculpture creation
out of the realm of professional artists, and makes it applicable to a wide
range of existing video material.
By providing viewers with 3D information, motion sculptures reveal space-time
motion information that is difficult to perceive with the naked eye, and allow
viewers to interpret how different parts of the object interact over time. We
validate the effectiveness of this approach with user studies, finding that our
motion sculpture visualizations are significantly more informative about motion
than existing stroboscopic and space-time visualization methods.Comment: UIST 2018. Project page: http://mosculp.csail.mit.edu
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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