112 research outputs found
Determining sequencing depth in a single-cell RNA-seq experiment
An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes
Multi-hierarchical Convolutional Network for Efficient Remote Photoplethysmograph Signal and Heart Rate Estimation from Face Video Clips
Heart beat rhythm and heart rate (HR) are important physiological parameters
of the human body. This study presents an efficient multi-hierarchical
spatio-temporal convolutional network that can quickly estimate remote
physiological (rPPG) signal and HR from face video clips. First, the facial
color distribution characteristics are extracted using a low-level face feature
Generation (LFFG) module. Then, the three-dimensional (3D) spatio-temporal
stack convolution module (STSC) and multi-hierarchical feature fusion module
(MHFF) are used to strengthen the spatio-temporal correlation of multi-channel
features. In the MHFF, sparse optical flow is used to capture the tiny motion
information of faces between frames and generate a self-adaptive region of
interest (ROI) skin mask. Finally, the signal prediction module (SP) is used to
extract the estimated rPPG signal. The experimental results on the three
datasets show that the proposed network outperforms the state-of-the-art
methods.Comment: 33 pages,9 figure
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus Image
Domain adaptation (DA) has been widely applied in the diabetic retinopathy
(DR) grading of unannotated ultra-wide-field (UWF) fundus images, which can
transfer annotated knowledge from labeled color fundus images. However,
suffering from huge domain gaps and complex real-world scenarios, the DR
grading performance of most mainstream DA is far from that of clinical
diagnosis. To tackle this, we propose a novel source-free active domain
adaptation (SFADA) in this paper. Specifically, we focus on DR grading problem
itself and propose to generate features of color fundus images with
continuously evolving relationships of DRs, actively select a few valuable UWF
fundus images for labeling with local representation matching, and adapt model
on UWF fundus images with DR lesion prototypes. Notably, the SFADA also takes
data privacy and computational efficiency into consideration. Extensive
experimental results demonstrate that our proposed SFADA achieves
state-of-the-art DR grading performance, increasing accuracy by 20.9% and
quadratic weighted kappa by 18.63% compared with baseline and reaching 85.36%
and 92.38% respectively. These investigations show that the potential of our
approach for real clinical practice is promising
Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection
Spiking Neural Networks (SNNs) have garnered widespread interest for their
energy efficiency and brain-inspired event-driven properties. While recent
methods like Spiking-YOLO have expanded the SNNs to more challenging object
detection tasks, they often suffer from high latency and low detection
accuracy, making them difficult to deploy on latency sensitive mobile
platforms. Furthermore, the conversion method from Artificial Neural Networks
(ANNs) to SNNs is hard to maintain the complete structure of the ANNs,
resulting in poor feature representation and high conversion errors. To address
these challenges, we propose two methods: timesteps compression and
spike-time-dependent integrated (STDI) coding. The former reduces the timesteps
required in ANN-SNN conversion by compressing information, while the latter
sets a time-varying threshold to expand the information holding capacity. We
also present a SNN-based ultra-low latency and high accurate object detection
model (SUHD) that achieves state-of-the-art performance on nontrivial datasets
like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30%
mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS
COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based
object detection model to date that achieves ultra low timesteps to complete
the lossless conversion.Comment: 14 pages, 10 figure
BanditPAM++: Faster -medoids Clustering
Clustering is a fundamental task in data science with wide-ranging
applications. In -medoids clustering, cluster centers must be actual
datapoints and arbitrary distance metrics may be used; these features allow for
greater interpretability of the cluster centers and the clustering of exotic
objects in -medoids clustering, respectively. -medoids clustering has
recently grown in popularity due to the discovery of more efficient -medoids
algorithms. In particular, recent research has proposed BanditPAM, a randomized
-medoids algorithm with state-of-the-art complexity and clustering accuracy.
In this paper, we present BanditPAM++, which accelerates BanditPAM via two
algorithmic improvements, and is faster than BanditPAM in complexity and
substantially faster than BanditPAM in wall-clock runtime. First, we
demonstrate that BanditPAM has a special structure that allows the reuse of
clustering information each iteration. Second, we demonstrate
that BanditPAM has additional structure that permits the reuse of information
different iterations. These observations inspire our proposed
algorithm, BanditPAM++, which returns the same clustering solutions as
BanditPAM but often several times faster. For example, on the CIFAR10 dataset,
BanditPAM++ returns the same results as BanditPAM but runs over 10
faster. Finally, we provide a high-performance C++ implementation of
BanditPAM++, callable from Python and R, that may be of interest to
practitioners at https://github.com/motiwari/BanditPAM. Auxiliary code to
reproduce all of our experiments via a one-line script is available at
https://github.com/ThrunGroup/BanditPAM_plusplus_experiments.Comment: NeurIPS 202
On the theoretical analysis of cross validation in compressive sensing
On the theoretical analysis of cros
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