The Internet's success arose from classical layering: protocols like TCP and Ethernet can be independently understood, changed, debugged, verified, and offloaded to hardware using a clean service interface between layers. To accrue the same benefits at a finer grain, we suggest sublayering, i.e., layering recursively within each layer. We show that the data link and routing layers have natural sublayers. However, while TCP intuitively decomposes into sub-functions (connection management, reliable delivery, congestion control) common state variables like sequence numbers and window sizes entangle these functions, making sublayering difficult. We propose an alternate sublayered TCP with equivalent functionality which enables easily changing congestion control and connection management. We also argue that sublayering can help create robust and verified Internet protocol implementations akin to seL4 for Operating Systems. To this end, we describe early experiments with a verified sublayered implementation of a simple bit-stuffing protocol using Coq, and a verified monolithic implementation of a lightweight TCP using Dafny. We end with a set of challenges for sublayered protocols.

OBJECTIVE: While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction. MATERIALS AND METHODS: We trained selective classifiers (logistic regression, random forest, support vector machine) to extract 5 variables from clinical notes: depression (n = 1563), glioblastoma (GBM, n = 659), rectal adenocarcinoma (DRA, n = 601), and abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601) of adenocarcinoma. We varied the cost of false positives (FP), false negatives (FN), and abstained notes and measured total misclassification cost. RESULTS: The depression selective classifiers abstained on anywhere from 0% to 97% of notes, and the change in total misclassification cost ranged from -58% to 9%. Selective classifiers abstained on 5%-43% of notes across the GBM and colorectal cancer models. The GBM selective classifier abstained on 43% of notes, which led to improvements in sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier and when compared to structured proxy variables. DISCUSSION: We showed that selective classifiers outperformed both non-selective classifiers and structured proxy variables for extracting data from unstructured clinical notes. CONCLUSION: Selective prediction should be considered when abstaining is preferable to making an incorrect prediction.

Recent human-in-the-loop (HIL) optimization studies using wearable devices have shown an improved average metabolic reduction by optimizing a small number of control parameters during short-duration walking experiments. However, the slow metabolic dynamics, high measurement noise, and experimental time constraints create challenges for increasing the number of control parameters to be optimized. Prior work applying gradient descent and Bayesian optimization to this problem have decoupled metabolic estimation and control parameter selection using fixed estimation intervals, which imposes a hard limit on the number of parameter evaluations possible in a given time budget. In this work, we take a different approach that couples estimation and parameter selection, allowing the algorithm to spend less time on refining the metabolic estimates for parameters that are unlikely to improve performance over the best observed values. Our approach uses a Kalman filter-based metabolic estimator to formulate an optimal stopping problem during the data acquisition step of standard Bayesian optimization. Performance was analyzed in numerical simulations and in pilot human subject testing with two subjects that involved optimizing six control parameters of a single-joint exosuit and four parameters of a multi-joint exosuit.

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.

Existing brain-computer interface (BCI) control of highly dexterous robotic manipulators and prosthetic devices typically rely solely on neural decode algorithms to determine the user's intended motion. Although these approaches have made significant progress in the ability to control high degree of freedom (DOF) manipulators, the ability to perform activities of daily living (ADL) is still an ongoing research endeavor. In this paper, we describe a hybrid system that combines elements of autonomous robotic manipulation with neural decode algorithms to maneuver a highly dexterous robotic manipulator for a reach and grasp task. This system was demonstrated using a human patient with cortical micro-electrode arrays allowing the user to manipulate an object on a table and place it at a desired location. The preliminary results for this system are promising in that it demonstrates the potential to blend robotic control to perform lower level manipulation tasks with neural control that allows the user to focus on higher level tasks thereby reducing the cognitive load and increasing the success rate of performing ADL type activities.