Matrix rankBootstrapTwo-step testRank estimationIdentificationMatching dimensionThis paper develops a general framework for conducting inference on the rank of an unknown matrix $\\Pi_0$. A defining feature of our setup is the null hypothesis of the form $\\mathrm H_0: \\mathrm{rank}(\\Pi_...
as pointed out by Sabir et al. [2], the only way to determine whether or not a face is real or fake is by looking for features such as an unnaturally asymmetric face, weird teeth and other more obvious inconsistencies not localized on the face but ...
DISCERN allows for the realistic reconstruction of gene expression information by transferring the style of hq data onto lq data, in latent and gene space. Our experiments on real and simulated data show that DISCERN outperforms several existing algorithms in gene expression inference across a wide ...
Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights...
Vehicle re-identification (re-id) aims to solve the problems of matching and identifying the same vehicle under the scenes across multiple surveillance cameras. For public security and intelligent transportation system (ITS), it is extremely important to locate the target vehicle quickly and accurately...
Sounds enhance the detection of visual stimuli while concurrently biasing an observer’s decisions. To investigate the neural mechanisms that underlie such multisensory interactions, we decoded time-resolved Signal Detection Theory sensitivity and criter
With the progress of computer science, a series of machine learning-based methods have been developed to reconstruct the missing data. In the early years, researchers presented the adaptive-fuzzy inference system (ANFIS) to interpolate the missing wind speed and wind direction data (Yang et al.,...
Furthermore, Table 2 presents the Total Parameters, the Training and Inference time, the Flops and the mean value of epochs needed for each model to converge. Fig. 6 presents the confusion matrices of the test set for SqueezeNodule-Net V1 (left) and V2 (right) for the 2D setting. For...
Chen, X. & Yan, G. Y. Semi-supervised learning for potential human microRNA–disease associations inference.Sci. Rep.4, 5501 (2014). ArticleCASPubMedPubMed CentralGoogle Scholar Shen, Z.et al.miRNA-Disease Association Prediction with Collaborative Matrix Factorizationn....
. Note also that, while the resolved complexes are required during the training stage, no structural information is required for performing inference on novel proteins. While other work in the literature has exploited the possibility of tying the predictions at multiple levels, the presented ...