Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie

COMPATIBILITY TESTING FOR CAMERA APPLICATIONS TO FACILITATE SOFTWARE DEVELOPMENT

A system is described that provides a consistent application programming interface and enables compatibility testing for applications that interface with cameras across various mobile devices. The system includes one or more controlled testing environment modules that isolate mobile devices from ambient light and provide test charts and consistent internal lighting for camera testing. The controlled testing environment modules enable operating system developers to capture both landscape and portrait images, access features, and test applications in consistent testing environments across various mobile devices. Such testing may enable development of software shims to facilitate interaction with a wide variety of cameras across different vendors and implementations within individual mobile devices

Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving

General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions. Our neural network provides a mapping between features and actions of sampled driving policies of a model-predictive control-based planner and predicts reward functions for upcoming planning cycles. In our evaluation, we compare the driving style of reward functions predicted by our deep network against clustered and linear reward functions. Our proposed deep learning approach outperforms clustered linear reward functions and is at par with linear reward functions with a-priori knowledge about the situation.Comment: To appear in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, France, June 2020 (Virtual Conference). Accepted version. Corrected figure fon

COMBATING THE ANTIBIOTIC RESISTANCE THREAT

Objective: Bacteria have developed ability to resist antibiotics that previously served as effective treatment. There is an increasing concern by health care providers to address this problem in healthcare settings especially in underdeveloped countries where access to the latest antibiotics is limited. These antibiotic resistant pathogens, both Gram-positive and Gram-negative bacteria, usually found in health care facilities, can cause severe to fatal infections. Our research focused on five of the most problematic bacteria: Methicillin-resistant Staphylococcus aureus (MRSA), Methicillin-sensitive Staphylococcus aureus (MSSA), Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Methods: For centuries home treatments have relied on the use of essential oils to treat ailments. We tested four commonly found essential oils (wintergreen, cinnamon, tea tree, and spearmint) against the five bacteria as well as methylglyoxal, which is an ingredient in Manuka honey. The Kirby-Bauer disk diffusion method was used and diameter of the zone of inhibition for each bacterium was measured to compare with standard antibiotics used for each strain of bacteria. In addition to studying the antibacterial activity of these compounds, we also investigated a way to deliver these compounds to patients, as topical applications, to inhibit the transmission of these multidrug resistant bacteria in healthcare settings.Results: Wintergreen and cinnamon essential oils as well as methylglyoxal showed high inhibitory effect on the tested bacteria. We also tested and found that Aloe Vera oil, Aloe Vera gel and natural Aloe Vera served as effective carriers with the essential oils and methylglyoxal. Conclusion: The antibacterial activity found in wintergreen and cinnamon essential oils and in methylglyoxal may offer a cost-effective alternative to commercial antibiotics because these compounds are readily available and relatively inexpensive and would be a benefit to people globally.Â

Predicting Persistent Opioid Use, Abuse, and Toxicity Among Cancer Survivors.

BackgroundAlthough opioids play a critical role in the management of cancer pain, the ongoing opioid epidemic has raised concerns regarding their persistent use and abuse. We lack data-driven tools in oncology to understand the risk of adverse opioid-related outcomes. This project seeks to identify clinical risk factors and create a risk score to help identify patients at risk of persistent opioid use and abuse.MethodsWithin a cohort of 106 732 military veteran cancer survivors diagnosed between 2000 and 2015, we determined rates of persistent posttreatment opioid use, diagnoses of opioid abuse or dependence, and admissions for opioid toxicity. A multivariable logistic regression model was used to identify patient, cancer, and treatment risk factors associated with adverse opioid-related outcomes. Predictive risk models were developed and validated using a least absolute shrinkage and selection operator regression technique.ResultsThe rate of persistent opioid use in cancer survivors was 8.3% (95% CI = 8.1% to 8.4%); the rate of opioid abuse or dependence was 2.9% (95% CI = 2.8% to 3.0%); and the rate of opioid-related admissions was 2.1% (95% CI = 2.0% to 2.2%). On multivariable analysis, several patient, demographic, and cancer and treatment factors were associated with risk of persistent opioid use. Predictive models showed a high level of discrimination when identifying individuals at risk of adverse opioid-related outcomes including persistent opioid use (area under the curve [AUC] = 0.85), future diagnoses of opioid abuse or dependence (AUC = 0.87), and admission for opioid abuse or toxicity (AUC = 0.78).ConclusionThis study demonstrates the potential to predict adverse opioid-related outcomes among cancer survivors. With further validation, personalized risk-stratification approaches could guide management when prescribing opioids in cancer patients

Asia-Pacific ICEMR: Understanding Malaria Transmission to Accelerate Malaria Elimination in the Asia Pacific Region

Gaining an in-depth understanding of malaria transmission requires integrated, multifaceted research approaches. The Asia-Pacific International Center of Excellence in Malaria Research (ICEMR) is applying specifically developed molecular and immunological assays, in-depth entomological assessments, and advanced statistical and mathematical modeling approaches to a rich series of longitudinal cohort and cross-sectional studies in Papua New Guinea and Cambodia. This is revealing both the essential contribution of forest-based transmission and the particular challenges posed by Plasmodium vivax to malaria elimination in Cambodia. In Papua New Guinea, these studies document the complex host–vector–parasite interactions that are underlying both the stunning reductions in malaria burden from 2006 to 2014 and the significant resurgence in transmission in 2016 to 2018. Here we describe the novel analytical, surveillance, molecular, and immunological tools that are being applied in our ongoing Asia-Pacific ICEMR research program

Calpain-5 Expression in the Retina Localizes to Photoreceptor Synapses

Purpose: We characterize calpain-5 (CAPN5) expression in retinal and neuronal subcellular compartments. Methods: CAPN5 gene variants were classified using the exome variant server, and RNA-sequencing was used to compare expression of CAPN5 mRNA in the mouse and human retina and in retinoblastoma cells. Expression of CAPN5 protein was ascertained in humans and mice in silico, in mouse retina by immunohistochemistry, and in neuronal cancer cell lines and fractionated central nervous system tissue extracts by Western analysis with eight antibodies targeting different CAPN5 regions. Results: Most CAPN5 genetic variation occurs outside its protease core; and searches of cancer and epilepsy/autism genetic databases found no variants similar to hyperactivating retinal disease alleles. The mouse retina expressed one transcript for CAPN5 plus those of nine other calpains, similar to the human retina. In Y79 retinoblastoma cells, the level of CAPN5 transcript was very low. Immunohistochemistry detected CAPN5 expression in the inner and outer nuclear layers and at synapses in the outer plexiform layer. Western analysis of fractionated retinal extracts confirmed CAPN5 synapse localization. Western blots of fractionated brain neuronal extracts revealed distinct subcellular patterns and the potential presence of autoproteolytic CAPN5 domains. Conclusions: CAPN5 is moderately expressed in the retina and, despite higher expression in other tissues, hyperactive disease mutants of CAPN5 only manifest as eye disease. At the cellular level, CAPN5 is expressed in several different functional compartments. CAPN5 localization at the photoreceptor synapse and with mitochondria explains the neural circuitry phenotype in human CAPN5 disease alleles

Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes.

Mutations in whole organisms are powerful ways of interrogating gene function in a realistic context. We describe a program, the Sanger Institute Mouse Genetics Project, that provides a step toward the aim of knocking out all genes and screening each line for a broad range of traits. We found that hitherto unpublished genes were as likely to reveal phenotypes as known genes, suggesting that novel genes represent a rich resource for investigating the molecular basis of disease. We found many unexpected phenotypes detected only because we screened for them, emphasizing the value of screening all mutants for a wide range of traits. Haploinsufficiency and pleiotropy were both surprisingly common. Forty-two percent of genes were essential for viability, and these were less likely to have a paralog and more likely to contribute to a protein complex than other genes. Phenotypic data and more than 900 mutants are openly available for further analysis. PAPERCLIP