A Maximum Likelihood Look-Ahead Unscented Rao-Blackwellised Particle Filter

A new maximum likelihood technique for the look-ahead unscented Rao-Blackwellised particle filter (la-URBPF) to improve its robustness to noise is proposed in this paper. A radial basis function whose centre is at the state associated with the maximum likelihood is also used for masking lower likelihood states without destroying information embedded in low prior states. Simulation results show how the proposed maximum likelihood la-URBPF algorithm responds to various noise levels ranging from relatively low to aggressively high levels. The computational times for different noise levels of the proposed algorithm are also investigated to assess its applicability in time-critical or in resource-restricted embedded systems

Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

Building hybrid rover models: Lessons learned

Abstract. Particle filters have recently become popular for diagnosis and monitoring of hybrid systems. In this paper we describe our experiences applying particle filtering-based diagnosis algorithms on NASA Ames Research Center’s K-9 rover. As well as the challenge of modelling the dynamics of the system, there are two major issues in applying a particle filter to such a model. The first is the asynchronous nature of the system—observations from different subsystems arrive at different rates, and occasionally out of order, leading to large amounts of uncertainty in the state of the system. The second issue is data interpretation. The particle filter produces a probability distribution over the state of the system, from which summary statistics that can be used for control or higher-level diagnosis must be extracted. We describe our approaches to both these problems, as well as other modelling issues that arose in this domain.

Disrupted limbic-prefrontal effective connectivity in response to fearful faces in lifetime depression

Background: Multiple brain imaging studies of negative emotional bias in major depressive disorder (MDD) have used images of fearful facial expressions and focused on the amygdala and the prefrontal cortex. The results have, however, been inconsistent, potentially due to small sample sizes (typically N < 50 ). It remains unclear if any alterations are a characteristic of current depression or of past experience of depression, and whether there are MDD-related changes in effective connectivity between the two brain regions.Methods: Activations and effective connectivity between the amygdala and dorsolateral prefrontal cortex (DLPFC) in response to fearful face stimuli were studied in a large population-based sample from Generation Scotland. Participants either had no history of MDD ( N = 664 in activation analyses, N = 474 in connectivity analyses) or had a diagnosis of MDD during their lifetime (LMDD, N = 290 in activation analyses, N = 214 in connectivity analyses). The within-scanner task involved implicit facial emotion processing of neutral and fearful faces.Results: Compared to controls, LMDD was associated with increased activations in left amygdala ( PFWE = 0.031 , k E = 4 ) and left DLPFC ( PFWE = 0.002 , k E = 33 ), increased mean bilateral amygdala activation ( β = 0.0715, P = 0.0314 ), and increased inhibition from left amygdala to left DLPFC, all in response to fearful faces contrasted to baseline. Results did not appear to be attributable to depressive illness severity or antidepressant medication status at scan time.Limitations: Most studied participants had past rather than current depression, average severity of ongoing depression symptoms was low, and a substantial proportion of participants were receiving medication. The study was not longitudinal and the participants were only assessed a single time.Conclusions: LMDD is associated with hyperactivity of the amygdala and DLPFC, and with stronger amygdala to DLPFC inhibitory connectivity, all in response to fearful faces, unrelated to depression severity at scan time. These results help reduce inconsistency in past literature and suggest disruption of ‘bottom-up’ limbic-prefrontal effective connectivity in depression

Detection of unanticipated faults for autonomous underwater vehicles using online topic models

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Field Robotics 35 (2018): 705-716, doi:10.1002/rob.21771.For robots to succeed in complex missions, they must be reliable in the face of subsystem failures and environmental challenges. In this paper, we focus on autonomous underwater vehicle (AUV) autonomy as it pertains to self‐perception and health monitoring, and we argue that automatic classification of state‐sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to AUV sensor data in order to automatically characterize its performance patterns, then demonstrate how in combination with operator‐supplied semantic labels these patterns can be used for fault detection and diagnosis by means of a nearest‐neighbor classifier. The method is evaluated using data collected by the Monterey Bay Aquarium Research Institute's Tethys long‐range AUV in three separate field deployments. Our results show that the proposed method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults at a high rate of correct detection with a very low false detection rate.Office of Naval Research Grant Number: N00014‐14‐1‐0199; David and Lucile Packard Foundatio

Soldiers Marching Down the Garden Path: Comprehension of Complex Language in Veterans with MTBI

This investigation examined the comprehension of complex language in Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) soldiers, who have been diagnosed with a mild traumatic brain injury upon return from deployment. This experiment used the sentence structure known as a garden path sentence, where the ambiguity of the sentence leads the mind down the path to the wrong interpretation, to test the language comprehension skills of veterans diagnosed with mTBI and controls. Accuracy and reaction time were measured by answering comprehension questions about both garden path sentences and non-garden path sentences. Results demonstrated that the subjects with mTBI had difficulty with complex language which did not contain strong pragmatic cues, but that reaction times were not significantly different compared to the control subjects

Group-SMA Algorithm Based Joint Estimation of Train Parameter and State

The braking rate and train arresting operation is important in the train braking performance. It is difficult to obtain the states of the train on time because of the measurement noise and a long calculation time. A type of Group Stochastic M-algorithm (GSMA) based on Rao-Blackwellization Particle Filter (RBPF) algorithm and Stochastic M-algorithm (SMA) is proposed in this paper. Compared with RBPF, GSMA based estimation precisions for the train braking rate and the control accelerations were improved by 78% and 62%, respectively. The calculation time of the GSMA was decreased by 70% compared with SMA