Robust Control Barrier Functions with Uncertainty Estimation

This paper proposes a safety controller for control-affine nonlinear systems with unmodelled dynamics and disturbances to improve closed-loop robustness. Uncertainty estimation-based control barrier functions (CBFs) are utilized to ensure robust safety in the presence of model uncertainties, which may depend on control input and states. We present a new uncertainty/disturbance estimator with theoretical upper bounds on estimation error and estimated outputs, which are used to ensure robust safety by formulating a convex optimization problem using a high-order CBF. The possibly unsafe nominal feedback controller is augmented with the proposed estimator in two frameworks (1) an uncertainty compensator and (2) a robustifying reformulation of CBF constraint with respect to the estimator outputs. The former scheme ensures safety with performance improvement by adaptively rejecting the matched uncertainty. The second method uses uncertainty estimation to robustify higher-order CBFs for safety-critical control. The proposed methods are demonstrated in simulations of an uncertain adaptive cruise control problem and a multirotor obstacle avoidance situation

Compensatory Cognitive Training for Latino Youth at Clinical High Risk for Psychosis: Study Protocol for a Randomized Controlled Trial.

Background: Early psychosocial interventions targeting cognitive and functional outcomes in individuals at clinical high risk for psychosis are a research priority. An even greater need is the identification of effective interventions in underserved populations. Compensatory Cognitive Training (CCT) is a psychosocial intervention with demonstrated efficacy in chronic schizophrenia and first episode psychosis, but remains to be evaluated in pre-illness phases. The aim of this study was to describe the development and implementation of an ongoing pilot randomized controlled trial investigating the efficacy of group-based, manualized CCT, as compared to recreational therapy (RT), for Latino participants at clinical high risk for psychosis (CHR) in both the United States and Mexico. It is hypothesized that, in comparison to those receiving RT, participants receiving CCT will show significant improvements in neurocognitive performance and functional capacity (co-primary outcomes) and self-rated functioning and clinical symptoms (secondary outcomes). Methods: Latino CHR participants aged 12-30 years will be included in the study. Both CCT and RT will be delivered in either Spanish or English, depending on group preference. Additionally, all assessments will be administered in participants' preferred language. A comprehensive assessment of neurocognitive and functional performance and clinical symptomatology will be performed at baseline, mid-intervention (4 weeks, 8 weeks), post-intervention (12 weeks) and 3-month follow-up. The primary outcome measures are neurocognition and functional capacity, as assessed by the MATRICS (Measurement and Treatment Research in Cognition in Schizophrenia) Consensus Cognitive Battery and the University of California, San Diego Performance-Based Skills Assessment-Brief, respectively. Furthermore, secondary outcomes measures will be used to examine change in clinical symptoms and self-reported functioning in response to CCT versus RT. Discussion: The evaluation of a novel treatment such as CCT in CHR youth will provide empirical support for a low risk, comprehensive cognitive intervention that could have important implications for public health if it improves neurocognition and functioning

Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data

Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introduce the concept of a Surface-at-Risk, a risk measure for stochastic processes that extends Value-at-Risk -- a commonly utilized risk measure in the risk-aware controls community. Second, we model the norm of the state discrepancy between the model and the true system evolution as a scalar-valued stochastic process and determine an upper bound to its Surface-at-Risk via Gaussian Process Regression. Third, we provide theoretical results on the accuracy of our fitted surface subject to mild assumptions that are verifiable with respect to the data sets collected during system operation. Finally, we experimentally verify our procedure by augmenting a drone's controller and highlight performance increases achieved via our risk-aware approach after collecting less than a minute of operating data

Effects of prior experience on shelter-seeking behavior of juvenile American lobsters

Author Posting. © University of Chicago, 2017. This article is posted here by permission of University of Chicago for personal use, not for redistribution. The definitive version was published in Biological Bulletin 232 (2017): 101-109, doi:10.1086/692697.Shelter-seeking behaviors are vital for survival for a range of juvenile benthic organisms. These behaviors may be innate or they may be affected by prior experience. After hatching, American lobsters Homarus americanus likely first come into contact with shelter during the late postlarval (decapodid) stage, known as stage IV. After the subsequent molt to the first juvenile stage (stage V), they are entirely benthic and are thought to be highly cryptic. We hypothesized that postlarval (stage IV) experience with shelter would carry over into the first juvenile stage (stage V) and reduce the time needed for juveniles to locate and enter shelters (sheltering). We found some evidence of a carryover effect, but not the one we predicted: stage V juveniles with postlarval shelter experience took significantly longer to initiate sheltering. We also hypothesized that stage V juveniles would demonstrate learning by relocating shelters more quickly with immediate prior experience. Our findings were mixed. In a maze, juveniles with immediate prior experience were faster to regain visual contact with shelter, suggesting that they had learned the location of the shelter. In contrast, there was no significant effect of immediate prior experience on time to initiate sheltering in an open arena, or in the maze after juveniles had regained visual contact. We conclude that very young (stage V) juvenile lobsters modify their shelter-seeking behavior based on prior experiences across several timescales. Ecologically relevant variation in habitat exposure among postlarval and early juvenile lobsters may influence successful recruitment in this culturally and commercially important fishery species.This work was supported by a Woods Hole Oceanographic Institution Postdoctoral Scholar Award (MWJ), a National Science Foundation Graduate Research Fellowship (SRB), NOAA Saltonstall-Kennedy Grant (MWJ), and National Science Foundation Grant IOS-0843440 (JA).2018-04-0

Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization

We present an approach for hierarchical super resolution (SR) using neural networks on an octree data representation. We train a hierarchy of neural networks, each capable of 2x upscaling in each spatial dimension between two levels of detail, and use these networks in tandem to facilitate large scale factor super resolution, scaling with the number of trained networks. We utilize these networks in a hierarchical super resolution algorithm that upscales multiresolution data to a uniform high resolution without introducing seam artifacts on octree node boundaries. We evaluate application of this algorithm in a data reduction framework by dynamically downscaling input data to an octree-based data structure to represent the multiresolution data before compressing for additional storage reduction. We demonstrate that our approach avoids seam artifacts common to multiresolution data formats, and show how neural network super resolution assisted data reduction can preserve global features better than compressors alone at the same compression ratios

A Learning-Based Framework for Safe Human-Robot Collaboration with Multiple Backup Control Barrier Functions

Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that leverages learning-based switching between multiple backup controllers to formally guarantee safety under bounded control inputs while satisfying driver intention. By leveraging backup controllers designed to uphold safety and input constraints, backup control barrier functions (BCBFs) construct implicitly defined control invariance sets via a feasible quadratic program (QP). However, BCBF performance largely depends on the design and conservativeness of the chosen backup controller, especially in our setting of human-driven vehicles in complex, e.g, off-road, conditions. While conservativeness can be reduced by using multiple backup controllers, determining when to switch is an open problem. Consequently, we develop a broadcast scheme that estimates driver intention and integrates BCBFs with multiple backup strategies for human-robot interaction. An LSTM classifier uses data inputs from the robot, human, and safety algorithms to continually choose a backup controller in real-time. We demonstrate our method's efficacy on a dual-track robot in obstacle avoidance scenarios. Our framework guarantees robot safety while adhering to driver intention

Incorporating Concomitant Medications into Genome-Wide Analyses for the Study of Complex Disease and Drug Response

Given the high costs of conducting a drug-response trial, researchers are now aiming to use retrospective analyses to conduct genome-wide association studies (GWAS) to identify underlying genetic contributions to drug-response variation. To prevent confounding results from a GWAS to investigate drug response, it is necessary to account for concomitant medications, defined as any medication taken concurrently with the primary medication being investigated. We use data from the Action to Control Cardiovascular Disease (ACCORD) trial in order to implement a novel scoring procedure for incorporating concomitant medication information into a linear regression model in preparation for GWAS. In order to accomplish this, two primary medications were selected: thiazolidinediones and metformin because of the wide-spread use of these medications and large sample sizes available within the ACCORD trial. A third medication, fenofibrate, along with a known confounding medication, statin, were chosen as a proof-of-principle for the scoring procedure. Previous studies have identified SNP rs7412 as being associated with statin response. Here we hypothesize that including the score for statin as a covariate in the GWAS model will correct for confounding of statin and yield a change in association at rs7412. The response of the confounded signal was successfully diminished from p = 3.19 × 10−7 to p = 1.76 × 10−5, by accounting for statin using the scoring procedure presented here. This approach provides the ability for researchers to account for concomitant medications in complex trial designs where monotherapy treatment regimens are not available

Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial

Background Individuals with type 2 diabetes are at an increased risk of cardiovascular disease. Alterations in circulating lipid levels, total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) are heritable risk factors for cardiovascular disease. Here we conduct a genome-wide association study (GWAS) of common and rare variants to investigate associations with baseline lipid levels in 7,844 individuals with type 2 diabetes from the ACCORD clinical trial. Methods DNA extracted from stored blood samples from ACCORD participants were genotyped using the Affymetrix Axiom Biobank 1 Genotyping Array. After quality control and genotype imputation, association of common genetic variants (CV), defined as minor allele frequency (MAF) ≥ 3%, with baseline levels of TC, LDL, HDL, and TG was tested using a linear model. Rare variant (RV) associations (MAF < 3%) were conducted using a suite of methods that collapse multiple RV within individual genes. Results Many statistically significant CV ( p  < 1 × 10 −8 ) replicate findings in large meta-analyses in non-diabetic subjects. RV analyses also confirmed findings in other studies, whereas significant RV associations with CNOT2 , HPN-AS1 , and SIRPD appear to be novel ( q  < 0.1). Discussion Here we present findings for the largest GWAS of lipid levels in people with type 2 diabetes to date. We identified 17 statistically significant ( p  < 1 × 10 −8 ) associations of CV with lipid levels in 11 genes or chromosomal regions, all of which were previously identified in meta-analyses of mostly non-diabetic cohorts. We also identified 13 associations in 11 genes based on RV, several of which represent novel findings

Rapid model-guided design of organ-scale synthetic vasculature for biomanufacturing

Our ability to produce human-scale bio-manufactured organs is critically limited by the need for vascularization and perfusion. For tissues of variable size and shape, including arbitrarily complex geometries, designing and printing vasculature capable of adequate perfusion has posed a major hurdle. Here, we introduce a model-driven design pipeline combining accelerated optimization methods for fast synthetic vascular tree generation and computational hemodynamics models. We demonstrate rapid generation, simulation, and 3D printing of synthetic vasculature in complex geometries, from small tissue constructs to organ scale networks. We introduce key algorithmic advances that all together accelerate synthetic vascular generation by more than 230-fold compared to standard methods and enable their use in arbitrarily complex shapes through localized implicit functions. Furthermore, we provide techniques for joining vascular trees into watertight networks suitable for hemodynamic CFD and 3D fabrication. We demonstrate that organ-scale vascular network models can be generated in silico within minutes and can be used to perfuse engineered and anatomic models including a bioreactor, annulus, bi-ventricular heart, and gyrus. We further show that this flexible pipeline can be applied to two common modes of bioprinting with free-form reversible embedding of suspended hydrogels and writing into soft matter. Our synthetic vascular tree generation pipeline enables rapid, scalable vascular model generation and fluid analysis for bio-manufactured tissues necessary for future scale up and production.Comment: 58 pages (19 main and 39 supplement pages), 4 main figures, 9 supplement figure