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StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.

 

The noise transition matrix plays a central role in the problem of learning with noisy labels. Among many other reasons, a large number of existing solutions rely on access to it. Identifying and estimating the transition matrix without ground truth labels is a critical and challenging task. When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging. Despite recent works proposing solutions for learning from instance-dependent noisy labels, the field lacks a unified understanding of when such a problem remains identifiable. The goal of this paper is to characterize the identifiability of the label noise transition matrix. Building on Kruskal's identifiability results, we are able to show the necessity of multiple noisy labels in identifying the noise transition matrix for the generic case at the instance level. We further instantiate the results to explain the successes of the state-of-the-art solutions and how additional assumptions alleviated the requirement of multiple noisy labels. Our result also reveals that disentangled features are helpful in the above identification task and we provide empirical evidence. 

This paper studies how RAID (redundant array of independent disks) could take full advantage of modern SSDs (solid-state drives) with built-in transparent compression. In current practice, RAID users are forced to choose a specific RAID level (e.g., RAID 10 or RAID 5) with a fixed storage cost vs. speed performance trade-off. The commercial market is witnessing the emergence of a new family of SSDs that can internally perform hardware-based lossless compression on each 4KB LBA (logical block address) block, transparent to host OS and user applications. Beyond straightforwardly reducing the RAID storage cost, such modern SSDs make it possible to relieve RAID users from being locked into a fixed storage cost vs. speed performance trade-off. In particular, RAID systems could opportunistically leverage higher-than-expected runtime user data compressibility to enable dynamic RAID level conversion to improve the speed performance without compromising the effective storage capacity. This paper presents techniques to enable and optimize the practical implementation of such elastic RAID systems. We implemented a Linux software-based elastic RAID prototype that supports dynamic conversion between RAID 5 and RAID 10. Compared with a baseline software-based RAID 5, under sufficient runtime data compressibility that enables the conversion from RAID 5 to RAID 10 over 60% of user data, the elastic RAID could improve the 4KB random write IOPS (I/O per second) by 42% and 4KB random read IOPS in degraded mode by 46%, while maintaining the same effective storage capacity. 

Spatial perception of our hand is closely linked to our ability to move the hand accurately. We might therefore expect that reach planning would take into account any changes in perceived hand position; in other words, that perception and action relating to the hand should depend on a common sensorimotor map. However, there is evidence to suggest that changes in perceived hand position affect a body representation that functions separately from the body representation used to control movement. Here, we examined target-directed reaching before and after participants either did (Mismatch group) or did not (Veridical group) experience a cue conflict known to elicit recalibration in perceived hand position. For the reaching task, participants grasped a robotic manipulandum that positioned their unseen hand for each trial. Participants then briskly moved the handle straight ahead to a visual target, receiving no performance feedback. For the perceptual calibration task, participants estimated the locations of visual, proprioceptive, or combined cues about their unseen hand. The Mismatch group experienced a gradual 70-mm forward mismatch between visual and proprioceptive cues, resulting in forward proprioceptive recalibration. Participants made significantly shorter reaches after this manipulation, consistent with feeling their hand to be further forward than it was, but reaching performance returned to baseline levels after only 10 reaches. The Veridical group, after exposure to veridically aligned visual and proprioceptive cues about the hand, showed no change in reach distance. These results suggest that perceptual recalibration affects the same sensorimotor map that is used to plan target-directed reaches. NEW & NOTEWORTHY If perceived hand position changes, we might assume this affects the sensorimotor map and, in turn, reaches made with that hand. However, there is evidence for separate body representations involved in perception versus action. After a cross-sensory conflict that results in proprioceptive recalibration in the forward direction, participants made shorter reaches as predicted, but only briefly. This suggests perceptual recalibration does affect the sensorimotor map used to plan reaches, but the interaction may be short-lived.