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Efficient single instance segmentation is critical for unlocking features in on-the-fly mobile imaging applications, such as photo capture and editing. Existing mobile solutions often restrict segmentation to portraits or salient objects due to computational constraints. Recent advancements like the Segment Anything Model improve accuracy but remain computationally expensive for mobile, because it processes the entire image with heavy transformer backbones. To address this, we propose TraceNet, a one-click-driven single instance segmentation model. TraceNet segments a user-specified instance by back-tracing the receptive field of a ConvNet backbone, focusing computations on relevant regions and reducing inference cost and memory usage during mobile inference. Starting from user needs in real mobile applications, we define efficient single-instance segmentation tasks and introduce two novel metrics to evaluate both accuracy and robustness to low-quality input clicks. Extensive evaluations on the MS-COCO and LVIS datasets highlight TraceNet’s ability to generate high-quality instance masks efficiently and accurately while demonstrating robustness to imperfect user inputs. 

Red-shifted and broadened Raman peaks of MoS₂reveal the strain and doping effects from Ti₃C₂MXene as a substrate. 

Abstract Heat shock factor 1 (HSF1) is a stress-responsive transcription factor that promotes cancer cell malignancy. To provide a better understanding of the biological processes regulated by HSF1, here we developed an HSF1 activity signature (HAS) and found that it was negatively associated with antitumor immune cells in breast tumors. Knockdown of HSF1 decreased breast tumor size and caused an influx of several antitumor immune cells, most notably CD8+ T cells. Depletion of CD8+ T cells rescued the reduction in growth of HSF1-deficient tumors, suggesting HSF1 prevents CD8+ T-cell influx to avoid immune-mediated tumor killing. HSF1 suppressed expression of CCL5, a chemokine for CD8+ T cells, and upregulation of CCL5 upon HSF1 loss significantly contributed to the recruitment of CD8+ T cells. These findings indicate that HSF1 suppresses antitumor immune activity by reducing CCL5 to limit CD8+ T-cell homing to breast tumors and prevent immune-mediated destruction, which has implications for the lack of success of immune modulatory therapies in breast cancer. Significance:The stress-responsive transcription factor HSF1 reduces CD8+ T-cell infiltration in breast tumors to prevent immune-mediated killing, indicating that cellular stress responses affect tumor-immune interactions and that targeting HSF1 could improve immunotherapies. 

Summary Complete randomization balances covariates on average, but covariate imbalance often exists in finite samples. Rerandomization can ensure covariate balance in the realized experiment by discarding the undesired treatment assignments. Many field experiments in public health and social sciences assign the treatment at the cluster level due to logistical constraints or policy considerations. Moreover, they are frequently combined with re-randomization in the design stage. We define cluster rerandomization as a cluster-randomized experiment compounded with rerandomization to balance covariates at the individual or cluster level. Existing asymptotic theory can only deal with rerandomization with treatments assigned at the individual level, leaving that for cluster rerandomization an open problem. To fill the gap, we provide a design-based theory for cluster rerandomization. Moreover, we compare two cluster rerandomization schemes that use prior information on the importance of the covariates: one based on the weighted Euclidean distance and the other based on the Mahalanobis distance with tiers of covariates. We demonstrate that the former dominates the latter with optimal weights and orthogonalized covariates. Last but not least, we discuss the role of covariate adjustment in the analysis stage, and recommend covariate-adjusted procedures that can be conveniently implemented by least squares with the associated robust standard errors.