Conformal prediction is an emerging technique for uncertainty quantification that constructs prediction sets guaranteed to contain the true label with a predefined probability. Recent work develops online conformal prediction methods that adaptively construct prediction sets to accommodate distribution shifts. However, existing algorithms typically assume *perfect label accuracy* which rarely holds in practice. In this work, we investigate the robustness of online conformal prediction under uniform label noise with a known noise rate. We show that label noise causes a persistent gap between the actual mis-coverage rate and the desired rate $\alpha$, leading to either overestimated or underestimated coverage guarantees. To address this issue, we propose a novel loss function *robust pinball loss*, which provides an unbiased estimate of clean pinball loss without requiring ground-truth labels. Theoretically, we demonstrate that robust pinball loss enables online conformal prediction to eliminate the coverage gap under uniform label noise, achieving a convergence rate of $\mathcal{O}(T^{-1/2})$ for both empirical and expected coverage errors (i.e., absolute deviation of the empirical and expected mis-coverage rate from the target level $\alpha$). This loss offers a general solution to the uniform label noise, and is complementary to existing online conformal prediction methods. Extensive experiments demonstrate that the proposed loss enhances the noise robustness of various online conformal prediction methods by achieving a precise coverage guarantee.

Multistage design has been utilized across a variety of scientific fields, enabling the adaptive allocation of sensing resources to effectively eliminate null locations and localize signals. We present a decision-theoretic framework for multi-stage adaptive testing that minimizes the total number of measurements while ensuring pre-specified constraints on both the false positive rate (FPR) and the missed discovery rate (MDR). Our method, SMART, explicitly addresses the often-overlooked aspect of uncertainty quantification in machine learning algorithms, incorporating it at every decision stage. This enables SMART to respond adaptively to important patterns in the data streams, adjusting its decisions based on the strength of evidence at specific locations. By leveraging technical tools and key concepts from multiple testing, adaptive thresholding, and compound decision theory, SMART not only enhances the aggregation of information across individual tests but also allows for varying thresholds tailored to the observed data, thereby ensuring effective error rate control and resulting in significant savings on total study costs. Through comprehensive analyses of large-scale A/B tests, high-throughput screening, and image analysis, we demonstrate that our approach yields substantial efficiency gains and improved control over error rates compared to existing methodologies.

Remoras employ their adhesive discs to rapidly attach to and detach from a wide range of marine surfaces. By analyzing high-speed images of remoras' (Echeneis naucrates) hitchhiking behavior, we describe the fish's detachment mechanism as a lip curling up to break the seal between the disc and substrate. By mimicking the kinematic and morphological properties of the biological disc, we fabricated a multi-material biomimetic disc (whose stiffness spans four orders of magnitude) that is capable of both attachment and detachment. Detachment is realized by a flexible cable-driven mechanism that curls the anterior region of the silicone soft lip, allows leakage under the disc, and equalizes the internal pressure to the external pressure. The disc lamellae with attached carbon fiber spinules can be rotated by hydraulic soft actuators whose internal pressure is precisely tuned to the ambient underwater pressure. During attachment, increasing the rotational angle of the lamellae and the preload of the disc significantly enhanced the adhesive forces. We found that curling up the soft lip and folding down the lamellae rapidly reduced the pulling force of the disc by a factor of 254 compared to that under the attached state, which lead to detachment. Based on these mechanisms, underwater maneuvers involving repeated attachment and detachment were demonstrated with an integrated ROV unit that had a self-contained actuation and control system for the disc. This study lays a foundation for the development of fully untethered robotic systems for underwater hitchhiking in real-world marine environments.

MOTIVATION: Genome-wide association studies (GWAS) interrogate common genetic variation across the entire human genome in an unbiased manner and hold promise in identifying genetic variants with moderate or weak effect sizes. However, conventional testing procedures, which are mostly P-value based, ignore the dependency and therefore suffer from loss of efficiency. The goal of this article is to exploit the dependency information among adjacent single nucleotide polymorphisms (SNPs) to improve the screening efficiency in GWAS. RESULTS: We propose to model the linear block dependency in the SNP data using hidden Markov models (HMMs). A compound decision-theoretic framework for testing HMM-dependent hypotheses is developed. We propose a powerful data-driven procedure [pooled local index of significance (PLIS)] that controls the false discovery rate (FDR) at the nominal level. PLIS is shown to be optimal in the sense that it has the smallest false negative rate (FNR) among all valid FDR procedures. By re-ranking significance for all SNPs with dependency considered, PLIS gains higher power than conventional P-value based methods. Simulation results demonstrate that PLIS dominates conventional FDR procedures in detecting disease-associated SNPs. Our method is applied to analysis of the SNP data from a GWAS of type 1 diabetes. Compared with the Benjamini-Hochberg (BH) procedure, PLIS yields more accurate results and has better reproducibility of findings. CONCLUSION: The genomic rankings based on our procedure are substantially different from the rankings based on the P-values. By integrating information from adjacent locations, the PLIS rankings benefit from the increased signal-to-noise ratio, hence our procedure often has higher statistical power and better reproducibility. It provides a promising direction in large-scale GWAS. AVAILABILITY: An R package PLIS has been developed to implement the PLIS procedure. Source codes are available upon request and will be available on CRAN (http://cran.r-project.org/). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.