1,296 research outputs found
Application of advanced on-board processing concepts to future satellite communications systems
An initial definition of on-board processing requirements for an advanced satellite communications system to service domestic markets in the 1990's is presented. An exemplar system architecture with both RF on-board switching and demodulation/remodulation baseband processing was used to identify important issues related to system implementation, cost, and technology development
Techniques for the realization of ultra- reliable spaceborne computer Final report
Bibliography and new techniques for use of error correction and redundancy to improve reliability of spaceborne computer
Dense Video Object Captioning from Disjoint Supervision
We propose a new task and model for dense video object captioning --
detecting, tracking, and captioning trajectories of all objects in a video.
This task unifies spatial and temporal understanding of the video, and requires
fine-grained language description. Our model for dense video object captioning
is trained end-to-end and consists of different modules for spatial
localization, tracking, and captioning. As such, we can train our model with a
mixture of disjoint tasks, and leverage diverse, large-scale datasets which
supervise different parts of our model. This results in noteworthy zero-shot
performance. Moreover, by finetuning a model from this initialization, we can
further improve our performance, surpassing strong image-based baselines by a
significant margin. Although we are not aware of other work performing this
task, we are able to repurpose existing video grounding datasets for our task,
namely VidSTG and VLN. We show our task is more general than grounding, and
models trained on our task can directly be applied to grounding by finding the
bounding box with the maximum likelihood of generating the query sentence. Our
model outperforms dedicated, state-of-the-art models for spatial grounding on
both VidSTG and VLN
Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination.
The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled dataset of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p<0.05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high-dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA-based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC
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