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Neuromuscular injuries can impair hand function and profoundly impacting the quality of life. This has motivated the development of advanced assistive robotic hands. However, the current neural decoder systems are limited in their ability to provide dexterous control of these robotic hands. In this study, we propose a novel method for predicting the extension and flexion force of three individual fingers concurrently using high-density electromyogram (HD-EMG) signals. Our method employs two deep forest models, the flexor decoder and the extensor decoder, to extract relevant representations from the EMG amplitude features. The outputs of the two decoders are integrated through linear regression to predict the forces of the three fingers. The proposed method was evaluated on data from three subjects and the results showed that it consistently outperforms the conventional EMG amplitude-based approach in terms of prediction error and robustness across both target and non-target fingers. This work presents a promising neural decoding approach for intuitive and dexterous control of the fingertip forces of assistive robotic hands. 

This whitepaper focuses on the astrophysical systematics which are encountered in dark matter searches. Oftentimes in indirect and also in direct dark matter searches, astrophysical systematics are a major limiting factor to sensitivity to dark matter. Just as there are many forms of dark matter searches, there are many forms of backgrounds. We attempt to cover the major systematics arising in dark matter searches using photons -- radio and gamma rays -- to cosmic rays, neutrinos and gravitational waves. Examples include astrophysical sources of cosmic messengers and their interactions which can mimic dark matter signatures. In turn, these depend on commensurate studies in understanding the cosmic environment -- gas distributions, magnetic field configurations -- as well as relevant nuclear astrophysics. We also cover the astrophysics governing celestial bodies and galaxies used to probe dark matter, from black holes to dwarf galaxies. Finally, we cover astrophysical backgrounds related to probing the dark matter distribution and kinematics, which impact a wide range of dark matter studies. In the future, the rise of multi-messenger astronomy, and novel analysis methods to exploit it for dark matter, will offer various strategic ways to continue to enhance our understanding of astrophysical backgrounds to deliver improved sensitivity to dark matter. 

This paper establishes Hoeffding’s lemma and inequality for bounded functions of general-state-space and not necessarily reversible Markov chains. The sharpness of these results is characterized by the optimality of the ratio between variance prox- ies in the Markov-dependent and independent settings. The boundedness of functions is shown necessary for such results to hold in general. To showcase the usefulness of the new results, we apply them for non-asymptotic analyses of MCMC estima- tion, respondent-driven sampling and high-dimensional covariance matrix estimation on time series data with a Markovian nature. In addition to statistical problems, we also apply them to study the time-discounted rewards in econometric models and the multi-armed bandit problem with Markovian rewards arising from the field of machine learning. 

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county),as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases. 

Propensity score methods account for selection bias in observational studies. However, the consistency of the propensity score estimators strongly depends on a correct specification of the propensity score model. Logistic regression and, with increasing popularity, machine learning tools are used to estimate propensity scores. We introduce a stacked generalization ensemble learning approach to improve propensity score estimation by fitting a meta learner on the predictions of a suitable set of diverse base learners. We perform a comprehensive Monte Carlo simulation study, implementing a broad range of scenarios that mimic characteristics of typical data sets in educational studies. The population average treatment effect is estimated using the propensity score in Inverse Probability of Treatment Weighting. Our proposed stacked ensembles, especially using gradient boosting machines as a meta learner trained on a set of 12 base learner predictions, led to superior reduction of bias compared to the current state-of-the-art in propensity score estimation. Further, our simulations imply that commonly used balance measures (averaged standardized absolute mean differences) might be misleading as propensity score model selection criteria. We apply our proposed model - which we call GBM-Stack - to assess the population average treatment effect of a Supplemental Instruction (SI) program in an introductory psychology (PSY 101) course at San Diego State University. Our analysis provides evidence that moving the whole population to SI attendance would on average lead to 1.69 times higher odds to pass the PSY 101 class compared to not offering SI, with a 95% bootstrap confidence interval of (1.31, 2.20). 

Personal moisture management fabrics that facilitate sweat transport away from the skin are highly desirable for wearer’s comfort and performance. Here, we demonstrate a “skin-like” directional liquid transport fabric, which enables continuous one-way liquid flow through spatially distributed channels acting like “sweating glands” yet repels external liquid contaminants. The water transmission rate can be 15 times greater than that of best commercial breathable fabrics. This exceptional property is achieved by creating gradient wettability channels across a predominantly superhydrophobic substrate. The flow directionality is explained by the Gibbs pinning criterion. The permeability, mechanical property, and abrasion resistance (up to 10,000 cycles) of the fabric were not affected by the treatment. In addition to functional clothing, this concept can be extended for developing materials for oil-water separation, wound dressing, geotechnical engineering, flexible microfluidics, and fuel cell membranes. 

How do children’s visual concepts change across childhood, and how might these changes be reflected in their drawings? Here we investigate developmental changes in children’s ability to emphasize the relevant visual distinctions between object categories in their drawings. We collected over 13K drawings from children aged 2-10 years via a free-standing drawing station in a children’s museum. We hypothesized that older children would produce more recognizable drawings, and that this gain in recognizability would not be entirely explained by concurrent development in visuomotor control. To measure recognizability, we applied a pretrained deep convolutional neural network model to extract a high-level feature representation of all drawings, and then trained a multi-way linear classifier on these features. To measure visuomotor control, we developed an automated procedure to measure their ability to accurately trace complex shapes. We found consistent gains in the recognizability of drawings across ages that were not fully explained by children’s ability to accurately trace complex shapes. Furthermore, these gains were accompanied by an increase in how distinct different object categories were in feature space. Overall, these results demonstrate that children’s drawings include more distinctive visual features as they grow older.