Say More Versus Trending Now Look Up In My Bag Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Mashable Selects Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook AI at School All Series Influencers are shilling libido products. Which ones should you use? Experts break down what's worth your time and money. Anna Iovine is the associate editor of features at Mashable. Previously, as the sex and relationships reporter, she covered topics ranging from dating apps to pelvic pain. Before Mashable, Anna was a social editor at VICE and freelanced for publications such as Slate and the Columbia Journalism Review.

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Physical activity (PA) plays an important role in maintaining and improving health. Daily steps have been a key PA measure that is easily accessible with common wearable devices. However, methods are lacking to recommend a personalized optimal distribution of daily steps over a period of time for the best of certain health biomarkers. In this paper, we fill this void based on the data from the All of Us Research Program which includes months of step counts as well as repeated measurements of key health biomarkers. We develop a new offline reinforcement learning (RL) algorithm to learn personalized and optimal PA distributions associated with cardiometabolic risk, where the action is a function representing the daily step distribution over a period of time. Simulation studies demonstrate the advantage of the proposed approach over existing continuous-action RL methods. The learned optimal policy from the All of Us data generally suggests people take more daily steps and also follow a more consistent pattern of PA over time while offering tailored recommendations for subgroups in blood glucose level, body mass index, blood pressure, age, and sex.

Algorithm 1 Hard-gating Algorithm for In-Stage IDKCascade Input Ds: Training data containing Ns samples in stage-s Ms: Sorted list of the models trained for stage-s C: Dictionary of models' spatio-temporal costs cs: User-defined budget of spatio-temporal cost for stage-s q: Confidence function maxA: Value for the upper bound of the cutoffs to avoid over-fitting nBins: Number of bins for the grid search Output s: The optimal IDK cutoff vector for stage-s 1: procedure HARDGATING(Ds, Ms, cs, C, q, maxA, nBins) 2: s =[], ModelAssign = 1, cost = P We use the Sepsis-3 toolkit3 to obtain the suspected infection time in patients, and following the process in Seymour et al. (2016) to finally label the onset of sepsis. We result at a total number of 20,009 sepsis patients out of the 52,902 adult patients from MIMIC-III database. We exclude those patients who stay in ICUs less than 6 hours and also exclude those patients who developed sepsis within the first 6 hours after ICU admission. This reduces our cohort to a total of 34,475ICU patient, and only 2,370(6.8%) Then according to Singer et al. (2016), we identify the onset of septic shock as Algorithm 3 End-to-End Training algorithm for UnfoldML Input D: Full training data containing N instances M: Full model zoo C: Dictionary of models' spatio-temporal costs q: Confidence criterion Output: the optimal ICK1 gate parameters (or a,b): the optimal IDK gate parameters 1: procedure END-TO-ENDTRAINING (D, M) 2: Pre-allocate costs cs for each stage s. Figure 4: Transitions in model calls: both cascades always call the first model per each stage for an entrance and transition to next models (IDK) or next stage (ICK).

Understanding causal dependencies in observational data is critical for informing decision-making. These relationships are often modeled as Bayesian Networks (BNs) and Directed Acyclic Graphs (DAGs). Existing methods, such as NOTEARS and DAG-GNN, often face issues with scalability and stability in high-dimensional data, especially when there is a feature-sample imbalance. Here, we show that the denoising score matching objective of diffusion models could smooth the gradients for faster, more stable convergence. We also propose an adaptive k-hop acyclicity constraint that improves runtime over existing solutions that require matrix inversion. We name this framework Denoising Diffusion Causal Discovery (DDCD). Unlike generative diffusion models, DDCD utilizes the reverse denoising process to infer a parameterized causal structure rather than to generate data. We demonstrate the competitive performance of DDCDs on synthetic benchmarking data. We also show that our methods are practically useful by conducting qualitative analyses on two real-world examples. Code is available at this url: https://github.com/haozhu233/ddcd.

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Autoregressive forecasting is central to predictive control in diabetes and hemodynamic management, where different operating zones carry different clinical risks. Standard models trained with teacher forcing suffer from exposure bias, yielding unstable multi-step forecasts for closed-loop use. We introduce Soft-Token Trajectory Forecasting (SoTra), which propagates continuous probability distributions (``soft tokens'') to mitigate exposure bias and learn calibrated, uncertainty-aware trajectories. A risk-aware decoding module then minimizes expected clinical harm. In glucose forecasting, SoTra reduces average zone-based risk by 18\%; in blood-pressure forecasting, it lowers effective clinical risk by approximately 15\%. These improvements support its use in safety-critical predictive control.