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Abstract We conducted an in-depth analysis of candidate member stars located in the peripheries of three ultra-faint dwarf (UFD) galaxy satellites of the Milky Way (MW): Boötes I (Boo1), Boötes II (Boo2), and Segue I (Seg1). Studying these peripheral stars has previously been difficult due to contamination from the MW foreground. We usedu-band photometry from the Dark Energy Camera (DECam) to derive metallicities to efficiently select UFD candidate member stars. This approach was validated on Boo1, where we identified both previously known and new candidate member stars beyond five half-light radii. We then applied a similar procedure to Boo2 and Seg1. Our findings hinted at evidence for tidal features in Boo1 and Seg1, with Boo1 having an elongation consistent with its proper motion and Seg1 showing some distant candidate stars, a few of which are along its elongation and proper motion. We find two Boo2 stars at large distances consistent with being candidate member stars. Using a foreground contamination rate derived from the Besançon Galaxy model, we ascribed purity estimates to each candidate member star. We recommend further spectroscopic studies on the newly identified high-purity members. Our technique offers promise for future endeavors to detect candidate member stars at large radii in other systems, leveraging metallicity-sensitive filters with the Legacy Survey of Space and Time and the new, narrowband Ca HK filter on DECam. 

We investigate the impact of bursty star formation on several galaxy scaling relations of dwarf galaxies using the $$\texttt{GRUMPY}$$ galaxy formation model. While this model reproduces the star formation rate (SFR)-stellar mass, stellar mass-gas mass, and stellar mass-metallicity relations, the scatter of these relations in the original model is smaller than observed. We explore the effects of additional stochasticity of SFR on the scaling relations using a model that reproduces the level of SFR burstiness in high-resolution zoom-in simulations. The additional SFR stochasticity increases the scatter in the SFR-stellar mass relation to a level similar to that exhibited by most nearby dwarf galaxies. The most extreme observed starbursting dwarfs, however, require higher levels of SFR stochasticity. We find that bursty star formation increases the scatter in the colour-magnitude distribution (CMD) for brighter dwarf galaxies $$(M_V < -12)$$ to the observed level, but not for fainter ones for which scatter remains significantly smaller than observed. This is due to the predominant old stellar populations in these faint model galaxies and their generally declining SFR over the past 10 Gyrs, rather than quenching caused by reionization. We examine the possibility that the colour scatter is due to scatter in metallicity, but show that the level of scatter required leads to an overestimation of scatter in the metallicity-mass relation. This illustrates that the scatter of observed scaling relations in the dwarf galaxy regime represents a powerful constraint on the properties of their star formation. 

Abstract Background Modeling of single cell RNA-sequencing (scRNA-seq) data remains challenging due to a high percentage of zeros and data heterogeneity, so improved modeling has strong potential to benefit many downstream data analyses. The existing zero-inflated or over-dispersed models are based on aggregations at either the gene or the cell level. However, they typically lose accuracy due to a too crude aggregation at those two levels. Results We avoid the crude approximations entailed by such aggregation through proposing an independent Poisson distribution (IPD) particularly at each individual entry in the scRNA-seq data matrix. This approach naturally and intuitively models the large number of zeros as matrix entries with a very small Poisson parameter. The critical challenge of cell clustering is approached via a novel data representation as Departures from a simple homogeneous IPD (DIPD) to capture the per-gene-per-cell intrinsic heterogeneity generated by cell clusters. Our experiments using real data and crafted experiments show that using DIPD as a data representation for scRNA-seq data can uncover novel cell subtypes that are missed or can only be found by careful parameter tuning using conventional methods. Conclusions This new method has multiple advantages, including (1) no need for prior feature selection or manual optimization of hyperparameters; (2) flexibility to combine with and improve upon other methods, such as Seurat. Another novel contribution is the use of crafted experiments as part of the validation of our newly developed DIPD-based clustering pipeline. This new clustering pipeline is implemented in the R (CRAN) package scpoisson . 

Tiny machine learning (TinyML) applications increasingly operate in dynamically changing deployment scenarios, requiring optimization for both accuracy and latency. Existing methods mainly target a single point in the accuracy/latency tradeoff space, which is insufficient as no single static point can be optimal under variable conditions. We draw on a recently proposed weight-shared SuperNet mechanism to enable serving a stream of queries that activates different SubNets within a SuperNet. This creates an opportunity to exploit the inherent temporal locality of different queries that use the same SuperNet. We propose a hardware–software co-design called SUSHI that introduces a novel SubGraph Stationary optimization. SUSHI consists of a novel field-programmable gate array implementation and a software scheduler that controls which SubNets to serve and which SubGraph to cache in real time. SUSHI yields up to a 32% improvement in latency, 0.98% increase in served accuracy, and achieves up to 78.7% off-chip energy saved across several neural network architectures. 

There is a growing rise of applications that need to support a library of models with diverse latency-accuracy trade-offs on a Pareto frontier, especially in the health-care domain. This work presents an end-to-end system for training and serving weight-sharing models. On the training end, we leverage recent research in creating a family of models on the latency- accuracy Pareto frontier that share weights, reducing the total number of unique parameters. On the serving (inference end), we propose a novel accelerator FastSwitch that extracts weight reuse across different models, thereby providing fast real-time switching between different models.